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Net zero energy building: the integration of design strategies and PVs for zero-energy consumption
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Net zero energy building: the integration of design strategies and PVs for zero-energy consumption
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NET ZERO ENERGY BUILDING: THE INTEGRATION OF DESIGN STRATEGIES AND PVS FOR ZERO-ENERGY CONSUMPTION by Haotian Wu A Thesis Presented to the FACULTY OF THE USC SCHOOL OF ARCHITECTURE UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree MASTER OF BUILDING SCIENCE May 2022 Copyright 2022 Haotian Wu ii Acknowledgements I would like to express my deep gratitude to my thesis chair professor Karen Kensek for providing invaluable guidance throughout the research. She has taught me the methodology to conduct research and present research work as clearly as possible. It was a great privilege to work and study under her guidance. I am extremely grateful for my parents’ unconditional love and what they have offered me. I would also like to express my appreciation to my thesis member Professor Marc Schiler, and Gideon Susman for their patient instructions and encouragements. iii Table of Contents Acknowledgements ......................................................................................................................... ii List of Tables ................................................................................................................................. ix List of Figures ................................................................................................................................. x Abstract ...................................................................................................................................... xviii Chapter 1. Introduction ............................................................................................................... 1 1.1 Definition of Net Zero Energy Building .......................................................................... 2 1.1.1 The Connection to Grid ................................................................................................ 3 1.1.2 Development of Net Zero Energy Building ................................................................. 4 1.2 Different Strategies .......................................................................................................... 7 1.2.1 Passive Design Strategies ............................................................................................. 8 1.2.1.1 Building Orientation .............................................................................................. 8 1.2.1.2 Thermal Insulation ................................................................................................ 9 1.2.1.3 Windows.............................................................................................................. 11 1.2.1.4 Natural Ventilation .............................................................................................. 12 1.2.2 Active Design Strategies ............................................................................................ 13 1.2.2.1 HVAC System ..................................................................................................... 13 1.2.2.2 Lighting ............................................................................................................... 14 1.2.2.3 Geothermal Heat Exchange Pump ...................................................................... 14 1.2.3 On-site Renewable Energy Generation ...................................................................... 15 1.2.4 Combination of Passive and Active Design ............................................................... 16 1.3 Energy Simulation .......................................................................................................... 17 1.3.1 Weather Data .............................................................................................................. 20 1.3.2 Occupancy and Zones ................................................................................................. 21 1.3.3 Simulation Inputs and Outputs ................................................................................... 22 1.3.4 Building Energy Modeling Example .......................................................................... 23 1.3.5 Integrated Environment Simulation Virtual Environment (IES VE) ......................... 31 1.4 Energy Codes in California and China ........................................................................... 37 1.4.1 Energy Codes in California ........................................................................................ 37 1.4.2 Energy Codes in China ............................................................................................... 41 1.4.3 Los Angeles Versus Harbin ........................................................................................ 44 1.5 Reality Versus Simulation .............................................................................................. 46 1.6 Summary ........................................................................................................................ 46 Chapter 2. Background Research ............................................................................................. 48 2.1 Understanding the Factors That Affect the Building Energy Performance ................... 49 2.1.1 Weather Condition ...................................................................................................... 49 2.1.2 Building Facade .......................................................................................................... 50 2.1.2.1 Window to Wall Ratio (WWR) ........................................................................... 51 iv 2.1.2.2 Window U-factor and SHGC .............................................................................. 51 2.1.2.3 Window Overhang .............................................................................................. 51 2.1.2.4 Glazing Visible Transmittance (VT) ................................................................... 52 2.1.2.5 Building Insulation .............................................................................................. 52 2.1.3 Natural Ventilation ..................................................................................................... 53 2.1.4 Orientation .................................................................................................................. 54 2.1.5 HVAC System ............................................................................................................ 54 2.1.6 The Optimized Integration of All the Design Strategies ............................................ 57 2.2 Limitation and Challenges of The Balance between Energy Consumption and Renewable Energy Generation..................................................................................................................... 59 2.2.1 The Generation of On-Site Renewable Energy .......................................................... 59 2.2.2 The Limitation of Active Design Strategies ............................................................... 63 2.2.3 Challenges of Passive Design Strategies .................................................................... 63 2.2.4 Passive Design and Active Design ............................................................................. 64 2.3 Energy Simulation Utilized as A Decision Support Tool .............................................. 64 2.3.1 Building Energy Modeling ......................................................................................... 66 2.3.2 Simulation-based Tool - IESVE ................................................................................. 66 2.3.3 The Development of Codes for Energy Simulation and Energy Modeling ............... 70 2.4 A Gap between Energy Simulation and Actual Building Performance ......................... 71 2.5 Summary ........................................................................................................................ 76 Chapter 3. Methodology ........................................................................................................... 77 3.1 Methodology Overview.................................................................................................. 80 3.1.1 Base Model ................................................................................................................. 81 3.1.2 Base Model Applied with Passive Design Strategies ................................................. 81 3.1.3 Base Model Applied with Active Design Strategies .................................................. 82 3.1.1 Photovoltaic Panels..................................................................................................... 82 3.1.2 Base Model with Integration of Passive and Active Design Strategies ..................... 83 3.2 Base Model ..................................................................................................................... 83 3.2.1 Location and Weather Data File ................................................................................. 87 3.2.2 Set up Parameters in Tabular BTM ............................................................................ 89 3.2.2.1 Occupancy Data Profile ...................................................................................... 89 3.2.2.2 Construction Materials Thermal Properties ........................................................ 90 3.2.2.3 Internal Heat Gains.............................................................................................. 93 3.2.2.4 Air Exchange ....................................................................................................... 94 3.2.3 Assign Thermal Template and Construction Materials to Base Model ...................... 95 3.2.4 Calculation of Building Energy Consumption ........................................................... 97 3.2.4.1 Lighting Energy Consumption ............................................................................ 98 3.2.4.2 Computer Energy Consumption .......................................................................... 98 3.2.4.3 Domestic Hot Water Energy Consumption ......................................................... 98 3.2.4.4 Cooling and Heating Load .................................................................................. 98 v 3.2.4.5 Fan Power .......................................................................................................... 100 3.2.4.6 EUI of the Building Model................................................................................ 101 3.3 Base Model with Passive Design Strategies ................................................................ 102 3.3.1 Roof Insulation ......................................................................................................... 103 3.3.2 Exterior Wall Insulation ........................................................................................... 107 3.3.3 Window .................................................................................................................... 110 3.3.3.1 Window U-factor............................................................................................... 110 3.3.3.2 Window Wall Ratio (WWR) ............................................................................. 110 3.3.3.3 Window SHGC ................................................................................................. 111 3.3.3.4 Window VT ....................................................................................................... 112 3.3.4 Orientation ................................................................................................................ 113 3.3.5 The Integration of Passive Design Strategies ........................................................... 114 3.4 Base Model with Active Design Strategies .................................................................. 114 3.4.1 HVAC System .......................................................................................................... 115 3.5 Photovoltaic Panels ...................................................................................................... 120 3.5.1 PV Panels in Los Angeles ........................................................................................ 122 3.5.2 PV Panels in Harbin ................................................................................................. 126 3.6 Base Model with Integration of Passive and Active Design Strategies ....................... 130 3.7 NZEBs with Different Integration................................................................................ 135 3.7.1 Base Model ............................................................................................................... 136 3.7.2 Integration 1 .............................................................................................................. 137 3.7.2.1 Integration 1 in Los Angeles ............................................................................. 137 3.7.2.2 Integration 1 in Harbin ...................................................................................... 139 3.7.3 Integration 2 .............................................................................................................. 140 3.7.3.1 Integration 2 in Los Angeles ............................................................................. 140 3.7.3.2 Integration 2 in Harbin ...................................................................................... 141 3.7.4 Integration 3 .............................................................................................................. 142 3.7.4.1 Integration 3 in Los Angeles ............................................................................. 142 3.7.4.2 Integration 3 in Harbin ...................................................................................... 143 3.7.5 Integration 4 .............................................................................................................. 144 3.7.5.1 Integration 4 in Los Angeles ............................................................................. 144 3.7.5.2 Integration 4 in Harbin ...................................................................................... 145 3.8 Summary ...................................................................................................................... 146 Chapter 4A. Los Angeles Base Model Results ......................................................................... 150 4A.1 Energy Consumption of the Base Model ..................................................................... 150 4A.2 How Passive Design Strategies Affect Building Energy Consumption ....................... 158 4A.2.1 Effects of Wall Insulation on Building Energy Consumption ............................... 159 4A.2.1.1 EUI Break Down ............................................................................................. 159 4A.2.1.2 Exterior Wall Insulation U = 0.12 Btu/h ft 2 F ................................................. 162 vi 4A.2.2 Effects of Roof Insulation on Building Energy Consumption............................... 164 4A.2.2.1 Roof Insulation U = 0.019 Btu/h ft 2 F ............................................................. 165 4A.2.2.2 Roof Insulation U = 0.079 Btu/h ft2 F ............................................................ 167 4A.2.3 Effects of Window U-value on Building Energy Consumption ............................ 171 4A.2.3.1 Window U = 0.30 Btu/h ft2 F ......................................................................... 171 4A.2.3.2 Window U = 0.38 Btu/h ft 2 F .......................................................................... 173 4A.2.4 Effects of WWR on Building Energy Consumption ............................................. 177 4A.2.4.1 WWR 20% ...................................................................................................... 177 4A.2.4.2 WWR 60% ...................................................................................................... 179 4A.2.5 Effects of Window SHGC on Building Energy Consumption .............................. 182 4A.2.5.1 Window SHGC 0.18 ....................................................................................... 183 4A.2.5.2 Window SHGC 0.12 ....................................................................................... 185 4A.2.6 Effects of Window VT on Building Energy Consumption ................................... 188 4A.2.9 Effects of Building Orientation on Building Energy Consumption ...................... 191 4A.2.9.1 Northwest (rotating 45°) ................................................................................. 191 4A.2.9.2 Orientation West (rotating 90°) ...................................................................... 193 4A.2.9.3 Orientation Southwest (rotating 135°) ............................................................ 196 4A.4 Summary for Los Angeles ............................................................................................ 199 Chapter 4B. Harbin Base Model Results .................................................................................. 207 4B.1 Energy Consumption of the Base Model ...................................................................... 207 4B.2 How Passive Design Strategies Affect Building Energy Consumption ....................... 211 4B.2.1 Effects of Wall Insulation on Building Energy Consumption ............................... 211 4B.2.1.1 Exterior Wall Insulation U = 0.016 Btu/h ft 2 F ............................................... 212 4B.2.1.2 Exterior Wall Insulation U = 0.12 Btu/h ft 2 F ................................................. 214 4B.2.2 Effects of Roof Insulation on Building Energy Consumption ............................... 218 4B.2.2.1 Roof Insulation U = 0.019 Btu/h ft 2 F ............................................................. 218 4B.2.2.2 Roof Insulation U = 0.079 Btu/h ft 2 F ............................................................. 220 4B.2.3 Effects of Window U-value on Building Energy Consumption ............................ 224 4B.2.3.1 Window U = 0.30 Btu/h ft 2 F .......................................................................... 224 4B.2.3.2 Window U = 0.38 Btu/h ft 2 F .......................................................................... 226 4B.2.4 Effects of WWR on Building Energy Consumption.............................................. 230 4B.2.4.1 WWR 20% ...................................................................................................... 230 4B.2.4.2 WWR 60% ...................................................................................................... 232 4B.2.5 Effects of Window SHGC on Building Energy Consumption .............................. 235 4B.2.5.1 Window SHGC 0.18 ....................................................................................... 236 4B.2.5.2 Window SHGC 0.12 ....................................................................................... 238 4B.2.6 Effects of Window VT on Building Energy Consumption .................................... 241 4B.2.9 Effects of Building Orientation on Building Energy Consumption....................... 244 4B.2.9.1 Northwest (rotating 45°) ................................................................................. 245 4B.2.9.2 Orientation West (rotating 90°) ....................................................................... 247 4B.2.9.3 Orientation Southwest (rotating 135°) ............................................................ 250 vii 4B.4 Summary for Harbin ..................................................................................................... 253 Chapter 5A. Los Angeles Base Model Analysis ....................................................................... 261 5A.1 Energy Consumption of Base Model Analysis ............................................................ 261 5A.2 Analysis of Model Applied with Passive Design Strategies ........................................ 264 5A.2.1 Analysis of Wall Insulation on the Model ............................................................. 264 5A.2.1.1 Exterior Wall Insulation U=0.016 Btu/h ft 2 F .................................................... 266 5A.2.1.2 Exterior Wall Insulation U=0.12 Btu/h ft 2 F ................................................... 270 5A.2.1.3 Wall Insulation Summary ............................................................................... 274 5A.2.2 Analysis of Roof Insulation on the Model ............................................................. 275 5A.2.2.1 Roof Insulation U=0.019 Btu/h ft 2 F .................................................................. 275 5A.2.2.2 Roof Insulation U=0.079 Btu/h ft 2 F ............................................................... 278 5A.2.2.3 Roof Insulation Summary ............................................................................... 282 5A.2.3 Analysis of Window U-value on the Model .......................................................... 283 5A.2.3.1 Window U=0.38 Btu/h ft 2 F ............................................................................ 283 5A.2.2.2 Window U=0.30 Btu/h ft 2 F ............................................................................ 286 5A.2.3.3 Window U-value Summary ............................................................................ 290 5A.2.4 Analysis of WWR on the Model .......................................................................... 291 5A.2.4.1 WWR Summary .............................................................................................. 296 5A.2.5 Analysis of Window SHGC on the Model ............................................................ 297 5A.2.5.1 Window SHGC Summary ............................................................................... 301 5A.2.6 Analysis of VT on the Model ................................................................................ 301 5A.2.6.1 Glazing VT Summary ..................................................................................... 304 5A.2.7 Analysis of Building Orientation on the Model .................................................... 305 5A.2.7.1 Model Orientation Summary .......................................................................... 307 5A.4 Los Angeles Summary ................................................................................................. 307 Chapter 5B. Harbin Base Model Analysis ................................................................................ 314 5B.1 Base Model Analysis .................................................................................................... 314 5B.2 Analysis of Model Applied with Passive Design Strategies ........................................ 317 5B.2.1 Analysis of Wall Insulation on the Model ............................................................. 317 5B.2.1.1 Exterior Wall Insulation U=0.016 Btu/h ft 2 F ..................................................... 319 5B.2.1.2 Exterior Wall Insulation U=0.12 Btu/h ft 2 F ................................................... 323 5B.2.1.3 Wall Insulation Summary................................................................................ 327 5B.2.2 Analysis of Roof Insulation on the Model ............................................................. 328 5B.2.2.1 Roof Insulation U=0.019 Btu/h ft 2 F .................................................................. 328 5B.2.2.2 Roof Insulation U=0.079 Btu/h ft 2 F ............................................................... 332 5B.2.2.3 Roof Insulation Summary ............................................................................... 336 5B.2.3 Analysis of Window U-value on the Model .......................................................... 337 5B.2.3.1 Window U=0.38 Btu/h ft 2 F ................................................................................ 337 5B.2.2.2 Window U=0.30 Btu/h ft 2 F ............................................................................ 341 5B.2.3.3 Window U-value Summary ............................................................................. 345 viii 5B.2.4 Analysis of WWR on the Model ........................................................................... 346 5B.2.4.1 WWR Summary .............................................................................................. 351 5B.2.5 Analysis of Window SHGC on the Model ............................................................ 352 5B.2.5.1 Window SHGC Summary ............................................................................... 356 5B.2.6 Analysis of VT on the Model ................................................................................. 356 5B2.6.1 Glazing VT Summary ...................................................................................... 359 5B.2.7 Analysis of Building Orientation on the Model ..................................................... 360 5B.2.7.1 Model Orientation Summary ........................................................................... 362 5B.4 Summary ....................................................................................................................... 362 5B.5 Comparison between the Simulation Results of Los Angeles and Harbin ................... 367 Chapter 6. Eight Net Zero Buildings Attempts Using an Integrative Approach ........................ 372 6.1 The Integration of Design Strategies of Model in Los Angeles ...................................... 372 6.1.1 Los Angeles Integration 1........................................................................................... 372 6.1.2 Los Angeles Integration 2........................................................................................... 375 6.1.3 Los Angeles Integration 3........................................................................................... 378 6.1.4 Los Angeles Integration 4........................................................................................... 381 6.1.5 Los Angeles Integration with Best Result .................................................................. 384 6.2.1 Harbin Integration 1 .................................................................................................... 387 6.2.2 Harbin Integration 2 .................................................................................................... 390 6.2.3 Harbin Integration 3 .................................................................................................... 393 6.2.4 Harbin Integration 4 .................................................................................................... 396 6.2.5 Harbin Integration with Best Result ........................................................................... 399 6.3 Summary ........................................................................................................................... 402 6.3.1 Los Angeles and Harbin Integration 1 ........................................................................ 403 6.3.2 Los Angeles and Harbin Integration 2 ........................................................................ 404 6.3.3 Los Angeles and Harbin Integration 3 ........................................................................ 405 6.3.4 Los Angeles and Harbin Integration 4 ........................................................................ 406 Chapter 7. Discussion and Future Work ................................................................................. 408 7.1 Discussion .................................................................................................................... 408 7.2 Study Results for Los Angeles and Harbin .................................................................. 411 7.3 Future Work ................................................................................................................. 415 7.3.1 Near Term Research ................................................................................................. 416 7.3.2 Long Term Research (Energy) ................................................................................. 417 7.4 Conclusion .................................................................................................................... 418 References ................................................................................................................................... 421 ix List of Tables Table 1-1 Roof U-factor Requirements (Title 24 2019) ............................................................... 10 Table 1-2 Nonresidential Wall U-factor Requirements (Title 24 2019) ....................................... 10 Table 1-3 Window Prescriptive Requirements (Title 24 2019) .................................................... 11 Table 1-4 Overhang Factors (Title 24 2019) ............................................................................... 12 Table 1-5 Lists of Energy Simulation Software ........................................................................... 18 Table 1-6 List of Software with Simulation Engine ..................................................................... 23 Table 1-7 Building Energy Codes System in China (J. Li and Shui 2015) .................................. 43 Table 3-1 Modules in IES VE ....................................................................................................... 81 Table 3-2 Spaces Information ....................................................................................................... 87 Table 3-3 PV Los Angeles Panels Energy Generation and Base Model Energy Consumption ..................................................................................................................................................... 126 Table 3-4 PV Harbin Panels Energy Generation and Base Model Energy Consumption .......... 130 Table 3-5 Integration of Los Angeles Base Model ..................................................................... 136 Table 3-6 Integration of Harbin Base Model .............................................................................. 137 Table 3-7 Integration 1 in Los Angeles ..................................................................................... 138 Table 3-8 Integration 1 in Harbin .............................................................................................. 139 Table 3-9 Integration 2 in Los Angeles ..................................................................................... 140 Table 3-10 Integration 2 in Harbin ............................................................................................ 141 Table 4A-1 Base Model Passive Design Strategies .................................................................... 158 Table 4B-1 Base Model Passive Design Strategies .................................................................... 211 Table 5A-1 Los Angeles Strategies Energy Efficiency Importance ........................................... 308 Table 5A-2 Building Energy Efficiency Importance .................................................................. 312 Table 5B-1 Harbin Strategies Energy Efficiency Importance .................................................... 363 Table 5B-2 Building Energy Efficiency Importance .................................................................. 366 Table 5B-3 Los Angeles and Harbin Strategies Energy Efficiency Importance ........................ 367 Table 5B-4 Strategies Energy Efficiency Importance and Comparison ..................................... 370 Table 6-1 Los Angeles Base Model and Integration 1 ............................................................... 373 Table 6-2 Los Angeles Base Model and Integration 2 ............................................................... 376 Table 6-3 Los Angeles Base Model and Integration 3 ............................................................... 379 Table 6-4 Los Angeles Base Model and Integration 4 ............................................................... 382 Table 6-5 Los Angeles Base Model, Integration 1, Integration 2, Integration 3, and Integration 4 Strategies ............................................................................................................... 386 Table 6-6 Harbin Base Model and Integration 1 ........................................................................ 388 Table 6-7 Harbin Base Model and Integration 2 ........................................................................ 391 Table 6-8 Harbin Base Model and Integration 3 ........................................................................ 394 Table 6-9 Harbin Base Model and Integration 4 ........................................................................ 397 Table 6-10 Harbin Base Model, Integration 1, Integration 2, Integration 3, and Integration 4 Strategies ..................................................................................................................................... 401 x List of Figures Figure 1-1 Powerhouse Kjørbo in Sandvika, Norway (Snøhetta 2014) ......................................... 5 Figure 1-2 The Bullitt Center in Seattle, Washington (Think | Architect 2016) ............................ 5 Figure 1-3 Santa Monica City Hall, CA (Reiner-Roth 2020) ......................................................... 6 Figure 1-4 Nowon EZ House, Korea (Schöck 2018) ...................................................................... 6 Figure 1-5 Energy flows between building and grid (ENERGY STAR n.d.) .............................. 15 Figure 1-6 The framework of the simulation plugin for the design tool (Han et al. 2018) .......... 17 Figure 1-7 Site Energy Use Intensity (“Workbook: Energy Efficiency Benchmarking Dashboard” n.d.) ........................................................................................................................... 19 Figure 1-8 Commercial building EUI by Property Type (Edelstein 2017) .................................. 20 Figure 1-9 Occupancy Table in IES VE ....................................................................................... 22 Figure 1-10 Base Model in IES .................................................................................................... 25 Figure 1-11 BEM from Autodesk Insight ..................................................................................... 25 Figure 1-12 System Zone Division of Insight Model ................................................................... 26 Figure 1-13 Selection of Weather File of Insight model .............................................................. 27 Figure 1-14 Building Operating Schedule of Insite Model .......................................................... 28 Figure 1-15 EUI Change of the Insight Model with Different Strategies (1) ............................... 29 Figure 1-16 EUI Change of the Insight Model with Different Strategies (2) ............................... 29 Figure 1-17 EUI Difference of HVAC Systems with Different Efficiency.................................. 30 Figure 1-18 EUI Difference between Different WWR of Western Wall ..................................... 30 Figure 1-19 Roof Material Table in IES VE ................................................................................. 32 Figure 1-20 Window Material Table in IES VE ........................................................................... 32 Figure 1-21 External Wall Material Table in IES VE .................................................................. 33 Figure 1-22 Weekly Occupancy Profile in IES VE ...................................................................... 33 Figure 1-23 Interior Zones Division in IES VE ............................................................................ 34 Figure 1-24 Simulation Weather File in IES VE .......................................................................... 34 Figure 1-25 Energy Report in IES VE .......................................................................................... 35 Figure 1-26 Monthly Room Cooling Sensible Load through the Year ........................................ 36 Figure 1-27 The External Gain, Solar Gain, Air Temperature and Dry-Bulb Temperature of a Zone on July 1 st .......................................................................................................................... 36 Figure 1-28 Climate Zone Map of California (Title 24 2019) ...................................................... 39 Figure 1-29 Improvement in ASHRAE Standard 90.1 (National Impact of ANSI 2016) ............ 41 Figure 1-30 The Divisions of China’s Climate Zones (J. Li and Shui 2015) ............................... 42 Figure 1-31 Building Types in China (J. Li and Shui 2015) ........................................................ 43 Figure 1-32 China Installed Electricity Generating Capacity ( U.S. Energy Information Administration (EIA) 2020) .......................................................................................................... 45 Figure 1-33 California Electricity Generation (“File:California Electricity Generation Sources Pie Chart.Svg - Wikimedia Commons” n.d.) .................................................................. 45 Figure 2-1 U.S. Energy Consumption by Source, 2020 (U.S. Energy System Factsheet |Center for Sustainable Systems 2021) ......................................................................................... 60 xi Figure 2-2 U.S Electricity Generation from All Sectors (U.S. Energy Information Administration 2021) .................................................................................................................... 61 Figure 2-3 PV Technology Types and Efficiencies (Photovoltaic Energy Factsheet 2021) ........ 62 Figure 2-4 Actual and Simulated HVAC Electricity Consumption (Daaboul, Ghali, and Ghaddar 2018) .............................................................................................................................. 68 Figure 2-5 Actual and Simulated Case Electricity Consumption ................................................. 68 Figure 2-6 Radiance Analysis on South-East Wall (Taleb 2014) ................................................. 69 Figure 2-7 Cooling Load Analysis (Taleb 2014) .......................................................................... 70 Figure 2-8 Potential Risk on Energy Use from Reported Underlying Causes (Dronkelaar et al. 2016) ........................................................................................................................................ 72 Figure 2-9 Ratio of Measured EUI/Design EUI of LEED Buildings (Frankel & Turner 2008) ....................................................................................................................................................... 73 Figure 2-10 Percentage of Measured and Predicted Energy Savings (Frankel & Turner 2008) ....................................................................................................................................................... 74 Figure 2-11 High and Medium Energy Type Buildings (Frankel & Turner 2008) ...................... 75 Figure 3-1 Methodology Diagram 1 ............................................................................................. 77 Figure 3-2 Methodology Diagram 2 ............................................................................................ 78 Figure 3-3 Methodology Diagram for Design Strategies Application.......................................... 79 Figure 3-4 Methodology Diagram for Creating the Base Model ................................................. 84 Figure 3-5 Base Model in IES VE ................................................................................................ 85 Figure 3-6 1 st Floor Plan of The Base Model ............................................................................... 85 Figure 3-7 Name of All The Zones (Spaces) ................................................................................ 86 Figure 3-8 Location Set up ........................................................................................................... 88 Figure 3-9 Weather Data File ...................................................................................................... 88 Figure 3-10 Building Template Manager Space Conditions ........................................................ 89 Figure 3-11 Weekly Profile .......................................................................................................... 90 Figure 3-12 Wall Construction Database ...................................................................................... 91 Figure 3-13 Window Construction Database ................................................................................ 92 Figure 3-14 Internal Heat Gains ................................................................................................... 93 Figure 3-15 Fluorescent Lighting, Computers and People Heat Gain Setup Details ................... 94 Figure 3-16 Air Exchange ............................................................................................................. 94 Figure 3-17 Air Exchange Details Setup ...................................................................................... 95 Figure 3-18 Apache...................................................................................................................... 96 Figure 3-19 Space Condition and Internal Gains in IES VE ........................................................ 97 Figure 3-20 Gains Derived from IES VE .................................................................................... 99 Figure 3-21 Heating and Cooling Load of Each Room in Each Hour During The Whole Year ..................................................................................................................................................... 100 Figure 3-22 EUI Stacked Bar Chart of Wall Insulation ............................................................. 102 Figure 3-23 Methodology Diagram for Passive Design Strategies Application......................... 103 Figure 3-24 Roof U=0.049 Btu/h ft 2 F 3 rd Floor Interior Space 02/December External Conduction .................................................................................................................................. 104 Figure 3-25 Roof U=0.079 Btu/h ft 2 F 3 rd Floor Interior Space 02/December External Conduction .................................................................................................................................. 105 xii Figure 3-26 Roof U=0.019 Btu/h ft 2 F 3 rd Floor Interior Space 02/December External Conduction .................................................................................................................................. 106 Figure 3-27 Wall U=0.12 Btu/h ft 2 F 1st Floor North Space 09/December External Conduction, Indoor and Outdoor Temperature ........................................................................... 108 Figure 3-28 Wall U=0.016 Btu/h ft 2 F 1st Floor North Space 09/December External Conduction, Indoor and Outdoor Temperature ........................................................................... 109 Figure 3-29 Interface to Change WWR ...................................................................................... 111 Figure 3-30 Interface to Change Transmittance of Outer Pane .................................................. 112 Figure 3-31 Interface to Change Visible Transmittance ............................................................. 113 Figure 3-32 Site Rotation ............................................................................................................ 113 Figure 3-33 Methodology Diagram for Active Design Strategies Application .......................... 115 Figure 3-34 HVAC ZONES with AHU ...................................................................................... 116 Figure 3-35 Airside System Selection ........................................................................................ 117 Figure 3-36 Waterside and Plant Equipment Selection .............................................................. 117 Figure 3-37 AHU1 HAVC Components..................................................................................... 118 Figure 3-38 AHU2 HAVC Components..................................................................................... 119 Figure 3-39 AHU3 HAVC Components..................................................................................... 119 Figure 3-40 AHU4 HAVC Components..................................................................................... 120 Figure 3-41 Methodology Diagram of PV Panels ...................................................................... 121 Figure 3-42 Model Floor Plan Dimension .................................................................................. 122 Figure 3-43 PVWatts System Informantion ............................................................................... 123 Figure 3-44 PV Panels Energy Generation ................................................................................. 124 Figure 3-45 PV Half of the Roof Area Panels Energy Generation Report ................................. 125 Figure 3-46 Methodology Diagram for Integration of Passive and Active Design Strategies ... 131 Figure 3-47 Parametric Batch Processor ..................................................................................... 132 Figure 3-48 Relation of Building Energy End Use and Design Strategies ................................. 133 Figure 3-49 Cooling and Heating Load in Spreadsheet .............................................................. 134 Figure 3-50 Methodology Diagram for Different Integration of Design Strategies ................... 135 Figure 4A-1 Gains of Base Model .............................................................................................. 152 Figure 4A-2 Spreadsheet of Cooling and Heating Load ............................................................. 153 Figure 4A-3 Temperature Difference Calculation in Spreadsheet ............................................. 155 Figure 4A-4 Fan Power Calculation in Spreadsheet ................................................................... 156 Figure 4A-5 Base Model EUI Stacked Bar Chart ....................................................................... 157 Figure 4A-6 Model with Exterior Wall U=0.016 Btu/h ft 2 F EUI Stacked Bar Chart ............... 161 Figure 4A-7 Model with Exterior Wall U=0.012 Btu/h ft 2 F EUI Stacked Bar Chart ............... 164 Figure 4A-8 Model with Roof Insulation U=0.019 Btu/h ft 2 F EUI Stacked Bar Chart ............ 167 Figure 4A-9 Model with Roof Insulation U=0.079 Btu/h ft 2 F EUI Stacked Bar Chart ............ 170 Figure 4A-10 Model with Window U=0.30 Btu/h ft 2 F EUI Stacked Bar Chart........................ 173 Figure 4A-11 Model with Window U=0.38 Btu/h ft 2 F EUI Stacked Bar Chart........................ 176 Figure 4A-12 Model with Window WWR 20% EUI Stacked Bar Chart ................................... 179 Figure 4A-13 Model with Window WWR 60% EUI Stacked Bar Chart ................................... 182 Figure 4A-14Model with Window SHGC 0.18 EUI Stacked Bar Chart .................................... 185 Figure 4A-15 Model with Window WWR 20% EUI Stacked Bar Chart ................................... 188 xiii Figure 4A-16 Model with Orientation 45° EUI Stacked Bar Chart ........................................... 193 Figure 4A-17 Model with Orientation 90° EUI Stacked Bar Chart ........................................... 196 Figure 4A-18 Model with Orientation 135° EUI Stacked Bar Chart ......................................... 199 Figure 4A-19 Wall Insulation EUI Comparison Histogram (kBtu/sf)........................................ 200 Figure 4A-20 Roof Insulation EUI Comparison Histogram (kBtu/sf) ....................................... 201 Figure 4A-21 Window U-value EUI Comparison Histogram (kBtu/sf) ..................................... 202 Figure 4A-22 WWR EUI Comparison Histogram (kBtu/sf) ...................................................... 203 Figure 4A-23 Window SHGC EUI Comparison Histogram (kBtu/sf) ....................................... 204 Figure 4A-24 Window VT EUI Comparison Histogram (kBtu/sf) ............................................ 205 Figure 4A-25 Orientation EUI Comparison Histogram (kBtu/sf) .............................................. 206 Figure 4B-1 Base Model Stacked Bar Chart............................................................................... 210 Figure 4B-2 Model with Wall Insulation U=0.016 Btu/h ft 2 F EUI Stacked Bar Chart ............. 214 Figure 4B-3 Model with Wall Insulation U=0.12 Btu/h ft 2 F EUI Stacked Bar Chart ............... 217 Figure 4B-4 Model with Roof Insulation U=0.019 Btu/h ft 2 F EUI Stacked Bar Chart ............ 220 Figure 4B-5 Model with Roof Insulation U=0.079 Btu/h ft 2 F EUI Stacked Bar Chart ............ 223 Figure 4B-6 Model with Window U=0.30 Btu/h ft 2 F EUI Stacked Bar Chart .......................... 226 Figure 4B-7 Model with Window U=0.38 Btu/h ft 2 F EUI Stacked Bar Chart .......................... 229 Figure 4B-8 Model with Window WWR 20% EUI Stacked Bar Chart ..................................... 232 Figure 4B-9 Model with Window WWR 60% EUI Stacked Bar Chart ..................................... 235 Figure 4B-10 Model with Window SHGC 0.18 EUI Stacked Bar Chart ................................... 238 Figure 4B-11 Model with Window SHGC 0.12 EUI Stacked Bar Chart ................................... 241 Figure 4B-12 Base Model Stacked Bar Chart............................................................................. 244 Figure 4B-13 Model with Orientation 45° EUI Stacked Bar Chart ............................................ 247 Figure 4B-14 Model with Orientation 90° EUI Stacked Bar Chart ............................................ 250 Figure 4B-15 Model with Orientation 135° EUI Stacked Bar Chart .......................................... 253 Figure 4B-16 Wall Insulation EUI Comparison Histogram (kBtu/sf) ........................................ 254 Figure 4B-17 Roof Insulation EUI Comparison Histogram (kBtu/sf)........................................ 255 Figure 4B-18Window U-value EUI Comparison Histogram (kBtu/sf) ...................................... 256 Figure 4B-19 WWR EUI Comparison Histogram (kBtu/sf) ...................................................... 257 Figure 4B-20 Window SHGC EUI Comparison Histogram (kBtu/sf) ....................................... 258 Figure 4B-21 Window VT EUI Comparison Histogram (kBtu/sf) ............................................ 259 Figure 4B-22 Orientation EUI Comparison Histogram (kBtu/sf) .............................................. 260 Figure 5A-1 Los Angeles Base Model EUI Stacked Bar Chart.................................................. 262 Figure 5A-2 Average High and Low Temperature in Los Angeles (Weather Spark 2022) ....... 263 Figure 5A-3 Los Angeles Base Model December 1 st External Conduction, Indoor and Outdoor Temperature .................................................................................................................. 265 Figure 5A-4 Los Angeles Base Model July 1 st External Conduction, Indoor and Outdoor Temperature ................................................................................................................................ 266 Figure 5A-5 Los Angeles Base Model and Wall Insulation U=0.016 Btu/h ft 2 F EUI Comparison ................................................................................................................................. 267 Figure 5A-6 Los Angeles Wall Insulation U=0.016 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature ........................................................................... 268 xiv Figure 5A-7 Los Angeles Wall Insulation U=0.016 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature ............................................................................................... 269 Figure 5A-8 Los Angeles Base Model and Wall Insulation U=0.12 Btu/h ft 2 F EUI Comparison ................................................................................................................................. 271 Figure 5A-9 Los Angeles Wall Insulation U=0.12 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature ........................................................................... 272 Figure 5A-10 Los Angeles Wall Insulation U=0.12 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature ............................................................................................... 273 Figure 5A-11 Los Angeles Wall Insulation EUI Comparison Bar Chart .................................. 274 Figure 5A-12 Los Angeles Base Model and Roof Insulation U=0.019 Btu/h ft 2 F EUI Comparison ................................................................................................................................. 275 Figure 5A-13 Los Angeles Roof Insulation U=0.019 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature ........................................................................... 276 Figure 5A-14 Los Angeles Roof Insulation U=0.079 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature ............................................................................................... 277 Figure 5A-15 Los Angeles Base Model and Roof Insulation U=0.12 EUI Comparison .......... 279 Figure 5A-16 Los Angeles Roof Insulation U=0.079 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature ........................................................................... 280 Figure 5A-17 Los Angeles Roof Insulation U=0.079 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature ............................................................................................... 281 Figure 5A-18 Los Angeles Roof Insulation EUI Comparison Bar Chart ................................... 282 Figure 5A-19 Los Angeles Base Model and Window U=0.38 Btu/h ft 2 F EUI Comparison ..... 283 Figure 5A-20 Los Angeles Window U=0.38 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature ............................................................................................... 284 Figure 5A-21 Los Angeles Window U=0.38 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature ........................................................................................................... 285 Figure 5A-22 Los Angeles Base Model and Window U=0.30 Btu/h ft 2 F EUI Comparison ..... 287 Figure 5A-23 Los Angeles Window U=0.30 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature ............................................................................................... 288 Figure 5A-24 Los Angeles Window U=0.30 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature ........................................................................................................... 289 Figure 5A-25 Los Angeles Window U-value EUI Comparison Bar Chart ................................ 290 Figure 5A-26 Los Angeles Base Model, Model with WWR 60% and Model with WWR 20% EUI Comparison ......................................................................................................................... 291 Figure 5A-27 Los Angeles WWR 60% July 1 st External Conduction and Solar Gain ............... 293 Figure 5A-28 Los Angeles Base Model WWR 40% July 1 st External Conduction and Solar Gain ............................................................................................................................................. 294 Figure 5A-29 Los Angeles WWR 20% July 1 st External Conduction and Solar Gain ............... 295 Figure 5A-30 Los Angeles WWR EUI Comparison Bar Chart .................................................. 296 Figure 5A-31 Los Angeles Base Model, Model with Window SHGC 0.18 and Model with Window SHGC 0.12 EUI Comparison ....................................................................................... 297 Figure 5A-32 Los Angeles Base Model SHGC 0.22 July 1 st Solar Gain ................................... 298 Figure 5A-33 Los Angeles Window SHGC 0.18 July 1 st Solar Gain ......................................... 299 xv Figure 5A-34 Los Angeles Window SHGC 0.12 July 1 st Solar Gain ......................................... 300 Figure 5A-35 Los Angeles Window SHGC EUI Comparison Bar Chart .................................. 301 Figure 5A-36 Los Angeles Base Model, Model with Window VT 0.43 and Model with Window VT 0.55 EUI Comparison ............................................................................................ 302 Figure 5A-37 Base Model VT July 1 st External Conduction, Internal Gain, Solar Gain, and Infiltration Gain .......................................................................................................................... 303 Figure 5A-38 Base Model VT December 1 st External Conduction, Internal Gain, Solar Gain, and Infiltration Gain .................................................................................................................... 304 Figure 5A-39 Los Angeles Glazing VT EUI Comparison Bar Chart ......................................... 305 Figure 5A-40 Los Angeles Model Rotating 45°, Base Model, Model Rotating 90° and Model Rotating 135° .............................................................................................................................. 306 Figure 5A-41 Los Angeles Model Orientation EUI Comparison Bar Chart .............................. 307 Figure 5A-42 Los Angeles Model Best Strategies EUI Comparison Bar Chart......................... 309 Figure 5B-1 Harbin Base Model EUI Stacked Bar Chart ........................................................... 315 Figure 5B-2 Average High and Low Temperature in Harbin (Weather Spark 2022) ................ 316 Figure 5B-3 Harbin Base Model December 1 st External Conduction, Indoor and Outdoor Temperature ................................................................................................................................ 318 Figure 5B-4 Harbin Base Model July 1 st External Conduction, Indoor and Outdoor Temperature ................................................................................................................................ 319 Figure 5B-5 Harbin Base Model and Wall Insulation U=0.016 Btu/h ft 2 F EUI Comparison ... 320 Figure 5B-6 Harbin Wall Insulation U=0.016 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature ............................................................................................... 321 Figure 5B-7 Harbin Wall Insulation U=0.016 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature ............................................................................................... 322 Figure 5B-8 Harbin Base Model and Wall Insulation U=0.12 Btu/h ft 2 F EUI Comparison ..... 324 Figure 5B-9 Harbin Wall Insulation U=0.12 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature ............................................................................................... 325 Figure 5B-10 Harbin Wall Insulation U=0.12 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature ............................................................................................... 326 Figure 5B-11 Harbin Wall Insulation EUI Comparison Bar Chart ............................................ 328 Figure 5B-12 Harbin Base Model and Roof Insulation U=0.019 Btu/h ft 2 F EUI Comparison ..................................................................................................................................................... 329 Figure 5B-13 Harbin Roof Insulation U=0.019 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature ........................................................................... 330 Figure 5B-14 Harbin Roof Insulation U=0.019 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature ............................................................................................... 331 Figure 5B-15 Harbin Base Model and Roof Insulation U=0.079 Btu/h ft 2 F EUI Comparison ..................................................................................................................................................... 333 Figure 5B-16 Harbin Roof Insulation U=0.079 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature ........................................................................... 334 Figure 5B-17 Harbin Roof Insulation U=0.079 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature ............................................................................................... 335 Figure 5B-18 Harbin Roof Insulation EUI Comparison Bar Chart ............................................ 337 xvi Figure 5B-19 Harbin Base Model and Window U=0.38 Btu/h ft 2 F EUI Comparison .............. 338 Figure 5B-20 Harbin Window U=0.38 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature ........................................................................................................... 339 Figure 5B-21 Harbin Window U=0.38 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature .................................................................................................................. 340 Figure 5B-22 Harbin Base Model and Window U=0.30 EUI Comparison ................................ 342 Figure 5B-23 Harbin Window U=0.30 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature ........................................................................................................... 343 Figure 5B-24 Harbin Window U=0.30 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature .................................................................................................................. 344 Figure 5B-25 Harbin Window U-value EUI Comparison Bar Chart ......................................... 345 Figure 5B-26 Harbin Base Model, Model with WWR 60% and Model with WWR 20% EUI Comparison ................................................................................................................................. 346 Figure 5B-27 Harbin WWR 60% July 1 st External Conduction and Solar Gain ........................ 348 Figure 5B-28 Harbin Base Model WWR 40% July 1 st External Conduction and Solar Gain.... 349 Figure 5B-29 Harbin WWR 20% July 1 st External Conduction and Solar Gain ........................ 350 Figure 5B-30 Harbin WWR EUI Comparison Bar Chart ........................................................... 351 Figure 5B-31 Harbin Base Model, Model with Window SHGC 0.18 and Model with Window SHGC 0.12 EUI Comparison ....................................................................................... 352 Figure 5B-32 Harbin Base Model SHGC 0.22 July 1 st Solar Gain ............................................ 353 Figure 5B-33 Harbin Window SHGC 0.18 July 1 st Solar Gain .................................................. 354 Figure 5B-34 Harbin Window SHGC 0.12 July 1 st Solar Gain .................................................. 355 Figure 5B-35 Harbin Window SHGC EUI Comparison Bar Chart ............................................ 356 Figure 5B-36 Harbin Base Model, Model with Window VT 0.43 and Model with Window VT 0.55 EUI Comparison ........................................................................................................... 357 Figure 5B-37 Harbin Base Model VT July 1 st External Conduction, Solar Gain, Internal Gain, Infiltration ......................................................................................................................... 358 Figure 5B-38 Harbin Base Model VT December 1 st External conduction, Solar Gain, Internal Gain, Infiltration ......................................................................................................................... 359 Figure 5B-39 Harbin Glazing VT EUI Comparison Bar Chart .................................................. 360 Figure 5B-40 Harbin Base Model, Model Rotating 45°, Model Rotating 90° and Model Rotating 135°EUI Comparison ................................................................................................... 361 Figure 5B-41 Los Angeles Model Orientation EUI Comparison Bar Chart .............................. 362 Figure 5B-42 Harbin Model Best Strategies EUI Comparison Bar Chart ................................ 364 Figure 6-1 Los Angeles Base Model and Integration 1 EUI Comparison .................................. 374 Figure 6-2 Los Angeles Base Model and Integration 2 EUI Comparison .................................. 377 Figure 6-3 Los Angeles Base Model and Integration 3 EUI Comparison .................................. 380 Figure 6-4 Los Angeles Base Model and Integration 4 EUI Comparison .................................. 383 Figure 6-5 Los Angeles Base Model, Integration 1, Integration 2, Integration 3, and Integration 4, PV Generation Comparison .................................................................................. 385 Figure 6-6 Harbin Base Model and Integration 1 EUI Comparison ........................................... 389 Figure 6-7 Harbin Base Model and Integration 2 EUI Comparison ........................................... 392 Figure 6-8 Harbin Base Model and Integration 3 EUI Comparison ........................................... 395 xvii Figure 6-9 Harbin Base Model and Integration 4 EUI Comparison ........................................... 398 Figure 6-10 Harbin Base Model, Integration 1, Integration 2, Integration 3, and Integration 4, PV Generation Comparison .................................................................................................... 400 Figure 6-11 Los Angeles and Harbin Base Model and Integration 1 Comparison ..................... 403 Figure 6-12 Los Angeles and Harbin Base Model and Integration 2 Comparison ..................... 404 Figure 6-13 Los Angeles and Harbin Base Model and Integration 3 Comparison ..................... 405 Figure 6-14 Los Angeles and Harbin Base Model and Integration 4 Comparison ..................... 406 Figure 7-1 Los Angeles Base Model and Harbin Base Model ................................................... 411 Figure 7-2 Los Angeles Model Best Strategies EUI Comparison Bar Chart ............................. 412 Figure 7-3 Harbin Model Best Strategies EUI Comparison Bar Chart ...................................... 413 Figure 7-4 Los Angeles and Harbin Base Model and Integration 4 Comparison ....................... 414 Figure 7-5 The Strategies Applied (Words in Black) and Strategies Not Applied (Words in Yellow) ....................................................................................................................................... 415 Figure 7-6 The Path to Net Zero Energy for The Bullitt Center (Peña, 2014) ........................... 419 xviii Abstract A four story office building (in two climate zones, Los Angeles and Harbin) in compliance with the California building energy code (Title 24) was chosen as a base case to examine the energy efficiency of different strategies: wall and roof insulation with low U-values, windows with different factors (U-factor, solar heat gain coefficient, visible transmittance, window to wall ratio, overhang factor), building orientation, natural ventilation, geothermal thermal heat exchange, smart control technology, high efficiency HVAC, system and lighting fixture. Energy performance of each design strategy was analyzed and compared to the base case. The design strategies that could save the most energy were adopted and adjusted on the model to create an integration that could achieve a net zero energy building. An application of integration with all selected energy efficient strategies is key to gain the energy balance of zero, as each strategy has positive and negative effects on the others. The photovoltaic panels placed on the roof were created to provide renewable energy. Under the analysis of PV system, a total amount of energy 1,751 MBtu (Los Angeles) and 1,945 MBtu (Harbin) can be generated annually; considering the energy consumption of the base model, it is difficult to achieve net zero. Heat recovery system (9% saved in Los Angeles and 8% saved in Harbin) and LED lighting fixture (7% saved in Los Angeles and 6% saved in Harbin) achieved the best savings as individual strategies. The integration of WWR 20%, window SHGC 0.12, window U=0.3 Btu/h ft 2 F, wall insulation U=0.016 Btu/h ft 2 F, roof insulation U=0.019 Btu/h ft 2 F, orientation facing southwest, heat recovery system, overhang, and LED lighting fixture with on-site energy generation by PVs were still not able to bring the base case building up to ZNE. EUI of Los Angeles model was reduced to 31.7 kBtu/ft 2 and Harbin model 33.9 kBtu/ft 2 . xix It was concluded that although energy consumption was reduced from the base case, it was still not low enough that PVs on the roof could make up the amount of energy needed. It is not possible to create a four-story NZE office building in climate of Los Angeles and Harbin, unless other energy strategies beyond those at the building level could be applied, perhaps including energy efficient office equipment, natural ventilation, different thermostat set points, and good user behavior. Hypothesis It is possible to make a four stories office building a net zero energy building in the climate of Los Angeles, applied with an integration of active and passive strategies, such as high efficiency HVAC system, wall and roof insulation with high R-value, windows with different factors (U- factor, solar heat gain coefficient, visible transmittance, window to wall ratio, overhang factor), lighting, natural ventilation, geothermal thermal heat exchange, smart control technology, orientation, listed from high efficiency to low efficiency. Generating more energy than the building energy requirement would allow for excess energy to be sent back to the grid. However, to achieve NZEB, PV panels on-site energy generation have to be employed. Keywords: Energy simulation, Building Energy Model (BEM), Net Zero Energy Building (NZEB), sustainable design, IES VE, energy consumption Research objectives xx 1. To explore the most effective design strategies to reduce energy consumption of a low-rise office building in Los Angeles (USA) and Harbin (China). 2. To quantify how specific design strategies will affect the energy efficiency of building: high efficiency HVAC system, wall and roof insulation with high R-value, windows, and different factors (U-factor, solar heat gain coefficient, visible transmittance, air leakage, window to wall ratio, overhang factor), lighting, natural ventilation, geothermal heat exchange, smart control technology, orientation. 3. To learn how to use IES VE for modeling passive design alternatives without HVAC system to explore their effectiveness, combined with post-process calculation. 4. To propose an integration of design strategies to achieve low or zero energy consumption for a case study building in Los Angeles (USA) and Harbin. 1 Chapter 1. Introduction With the increasing concern of extreme climate changes and rising energy prices, designers need to understand more about the amount of resources buildings consume and their resultant impact on the environment. Approximately 76% of electricity usage and 40% of prime energy consumption in the U. S. is utilized by buildings section which emphasizes the importance of decreasing the energy consumption of buildings to reduce resultant greenhouse gas emissions (GHG) (Department of Energy 2015). Thus, more effort on building energy savings is required to meet worldwide energy and environmental challenges. The building energy consumption could be highly reduced by the incorporation of different strategies in the phases of design, construction, and operation to increase building energy efficiency for new design and retrofit projects. In addition, the renewable sources from on-site generation and off-site is beneficial to reducing the dependency on the conventional energy sources. Net Zero Energy Building (NZEB), which is the combination of high energy efficiency and on-site renewable energy generation, is considered an effective metric for energy savings and reduced carbon emissions in the building sector. A NZEB is designed to use the lowest amount of energy as possible and then to generate on-site energy for the remaining balance. Zero energy consumption is an ambitious and increasingly achievable goal as the advanced development of materials, technologies, design strategies and ability of renewable on-site generation. “Private commercial property owners have a growing interest in developing zero energy buildings to meet their corporate goals. In response to regulatory mandates, federal government agencies and many state and local governments are beginning to move toward zero energy building targets” (“Zero Energy Buildings | Department of Energy” n.d.). 2 Because the concept of Net Zero Energy Building and Zero Energy Building (ZEB) is relatively new, there are some metrics that widely utilized. The Department of Energy (DOE) and National Renewable Energy Laboratory (NREL) have given some definitions and explanations for zero energy building and net zero energy building. This chapter defines what a net zero building is, discusses different strategies for achieving energy efficiency, explains energy simulation, compares American and Chinese energy codes, and briefly explores why building simulation may not match reality. 1.1 Definition of Net Zero Energy Building The definition of NZEB has drawn increasing global attention in last few years. It is represented in the reestablishment of EU Directive on Energy Performance of Buildings (EPBD) that “all the new buildings constructed by the end of 2020 should be nearly zero energy buildings;” in the United States, the goal of DOE is “achieving marketable zero energy homes in 2020 and commercial zero energy buildings in 2025” (Sartori, Napolitano, and Voss 2012). Nonetheless, there still is no standard definition of NZEB in the world. A ZEB could be defined as a building that with highly reduced energy demand and achieved a balance of energy demanded and supplied by renewable energy (D’Agostino and Mazzarella 2019). Without considering the zero balance, the definitions of ZEB still have a wide range of expression with different terms and phrases (Yu et al. 2019). NZEB represents a performance goal that achieved by adopting series of strategies and using energy simulation from design to construction process. The concept of a NZEB is comprehended as an energy efficient building that has the capability of generating the same amount of renewable energy as much as the energy it consumes from traditional energy sources. National Renewable 3 Energy Laboratory (NREL) has presented several terms (net zero site energy, net zero source energy, net zero energy costs and net zero energy emissions) to fit different projects as the roles vary, designers, owners, and operators (Sartori, Napolitano, and Voss 2012). To use the electricity generated from traditional energy sources when the renewable energy produced on-site could not meet the buildings’ energy load, they are still connected to electric grid. Examples of on-site energy generation includes the following: wind turbines, photovoltaic panels, solar hot water, geothermal system, and others. Some important features of net zero energy building are helpful to better understand its concept, such as its connection to grid and the development of net zero energy building. The concept of NZEB selected is a building with annual site energy balance of energy demanded and supplied by renewable energy. 1.1.1 The Connection to Grid The electrical grid is the power system network for electricity delivery that consists of power station, electrical substations, distribution, and transmission lines. In the US there are three grids, Eastern Grid, Western Grid, and Texas Grid, connecting together and operating independently. In China, there are also three grids named State Grid, China Southern Power Grid, and Inner Mongolia Grid. Not being connected to one of the state or national grids is often called being “off the grid.” Net zero energy buildings are connected to the grid to provide electricity from traditional energy sources when the renewable on-site generation cannot support building energy consumption. Also, the electricity will be exported back to the grid when the on-site generation exceeds the building energy requirements, when it is allowed by law. It is often necessary to connect with the grid to maintain the building energy balance. Because the excess energy could tradeoff the excess demand 4 from grid, which could help the net zero energy building achieve net energy consumption of zero. The connection to grid is utilized as a method to achieve net zero energy consumption due to the limitation of the energy storage. One question should be considered carefully is the payment for the excess energy exported to the grid as the different position of building owner and utilities (“Net Zero Energy Buildings | WBDG - Whole Building Design Guide” n.d.). Different states have different laws regarding how this is done. For example, in California, PG&E was allowed by California State Assembly Bill 920 to pay the customers who are in Net Energy Metering (NEM) program for the any electricity generated in excess of what t they use annually. The rate of compensation is based on the average of 12 months market energy rate, which is around 2-4 cents per kilowatt hour (kWh) (“Net Energy Metering (NEM) and Your Bill” n.d.). 1.1.2 Development of Net Zero Energy Building One of the first prototype buildings in the modern age to be a NZEB with the goal of zero-heating was created in 1950 (Ferrante and Cascella 2011). Then other international examples have been developing from low-rise houses to multi-story residential and office buildings. A few examples are the Powerhouse Kjørbo (Figure 1-1), the Bullitt Center (Figure 1-2), Santa Monica City Hall (Figure 1-3), and Nowon ZE (Figure 1-4). 5 Figure 1-1 Powerhouse Kjørbo in Sandvika, Norway (Snøhetta 2014) Figure 1-2 The Bullitt Center in Seattle, Washington (Think | Architect 2016) 6 Figure 1-3 Santa Monica City Hall, CA (Reiner-Roth 2020) Figure 1-4 Nowon EZ House, Korea (Schöck, 2018) 7 NZEB has been proved to be functioning well in different regions and climate zones. With all the results coming from previous examples showing, passive measures usually have better energy performance and less time period of payback than active measures (Yu et al. 2019). NZEB still have higher energy efficiency than conventional buildings despite the generation of on-stie renewable energy. As more research has been made on zero energy buildings, projects adopted with the concept of NZEB have been constructed. A research showed that ZEB in western countries, such as U.S., Canada, are developing faster than the Asian countries like China, Korea, and Japan (Yu et al. 2019). 1.2 Different Strategies There are many ways to increase building energy efficiency, ranging from simple behavioral and extensive improvement in phase of design, construction, and operation. The strategies incorporated to buildings should be based on the condition of specific project. The appropriate strategies in optimal combination could significantly increase energy savings in life cycle of building. Space conditioning, water heating and lighting usually account for more than half of energy use in building sector (Sartori, Napolitano, and Voss 2012). Therefore, the strategies are mainly considered to reduce electricity consumption in those areas. Active strategies usually consist of heating and cooling systems, smart control technology and lighting, while passive design measures include building orientation, insulation, window choice, and natural ventilation. 8 1.2.1 Passive Design Strategies Passive designs are the strategies that take advantage of natural energy based on the weather conditions and location of project. Most of the time, front-end cost will be lower compared to active design strategies (Marro 2018). They are very significant for increasing energy efficiency and enhancing human comfort. Designers can use these strategies first before exploring other methods of saving energy (Marro 2018). However, the adoption of passive design should be selected more carefully when considering the cost and effectiveness, especially in hot-humid climates with dense urban contexts, and the acceptable period of payback is generally less than 10 years (Sun, Gou, and Lau 2018). One thing that should be mentioned is that NZEB is still test-bedding project, so there are some factors that could affect the cost of passive design strategies. It is hard to achieve certain goals when the project which applied with specific strategy is under small economic scale. For example, the small rooftop greenery is hard to have significant impact on energy saving. Also, the products for energy savings should be special customized if they are not available in the market, which usually have more cost. 1.2.1.1 Building Orientation The orientation of a building is considered as a feature for street appeal and scenic view. With the concerns of energy issues, it is becoming more important for designers to maximize the usage of solar energy with local climate condition to maintain the indoor thermally comfortable condition. The appropriate orientation of building based on the position of sun is thought as a fundamental and general passive solar design strategy, often a low-cost solution that is developed in the early design stage. The relative position of the sun is a major factor in heat gain in buildings, which 9 makes accurate orientation of the building a fundamental consideration in passive solar construction. The amount of solar heat gains that building envelope received is based on the angle of the buildings (Pacheco, Ordóñez, and Martínez 2012). A building facing south is widely considered as an optimal design for more heat in winter and controlling solar gain in summer. It is also beneficial to increasing the penetration of daylight and the performance of other related passive strategies, such as windows, building shape and natural ventilation. 1.2.1.2 Thermal Insulation The appropriate selection of building insulation is a simple and energy efficient method in the design to reduce energy consumption. Insulation consists of the material that is featured, with high thermal resistance, such as foam, fiberglass and mineral wool, to reduce the heat loss and heat gain through envelope (Aditya et al. 2017). Therefore, the building with proper insulation could keep the heat inside and resist the undesired heat penetrating inside. As a result, the energy consumption for heating and cooling the inner space could be highly reduced. Also, the interior environment comfort level could be controlled steadily. According to the requirements of Title 24, the assembly U-factor of roof for the nonresidential metal frame structure in the climate zone 6 (Los Angeles) should be equal or lower than 0.049 Btu/h·ft 2 ·F (Table 1-1); the assembly U-factor of wall must be equal or lower than 0.069 Btu/h·ft 2 ·F (Table 1-2) (Title 24 2019). 10 Table 1-1 Roof U-factor Requirements (Title 24 2019) Table 1-2 Nonresidential Wall U-factor Requirements (Title 24 2019) 11 1.2.1.3 Windows Windows are often identified as the significant factors that affect building energy efficiency as its potential for energy saving is relatively high. U-factor, SHGC, visible transmittance (VT) are the parameters that affecting the energy performance of windows. Compared to the other parts of a façade, like walls and roof, windows have the U-factor that times larger than them, which results in a large amount of heat loss (Kaasalainen et al. 2020). On the contrary, an enormous amount of solar heat and daylight are received through the building façade. Therefore, the heating load in winter, cooling load in summer and the electricity consumption are relatively based on the design of windows. To have a Title 24 acceptable thermal performance, the U-factor of operable and fixed window should not be higher than 0.46 Btu/h·ft 2 ·F and 0.36 Btu/h·ft2·F correspondingly (Title 24 2019). For proper solar heat gain and daylight penetration, the maximum relative solar heat gain (RSHGC) should be lower than 0.22 and 0.25, and minimum visible transmittance (VT) of operable and fixed window cannot be lower than 32% and 42% (Title 24 2019). In addition, the window to wall ratio (WWR) cannot be higher than 40% (Table 1-3) (Title 24 2019). The requirements of overhang factors for the windows are arranged with the directions of building (Table 1-4). Table 1-3 Window Prescriptive Requirements (Title 24 2019) 12 Table 1-4 Overhang Factors (Title 24 2019) 1.2.1.4 Natural Ventilation For natural ventilation, it is essential to consider if air could blow through the design space depending on the orientation of window and other openings when natural ventilation is adapted as passive design. Natural ventilation uses natural force, such as wind flow, air-pressure difference, and thermal buoyancy, to provide fresh air into the indoor environment without using mechanical ventilation. Some strategies like cross ventilation, stack ventilation, and the chimney effect are usually utilized in the design of natural ventilation. The energy demand of the building will be reduced if more indoor air ventilation is provided by natural wind force. However, natural ventilation will be more efficient in the certain condition that the area where there is comfortable air temperature. The requirement of energy code in terms of insulation is usually just the bare minimum. Design the building with high R-value insulation could resist the conductive flow of heat and cold air, which results in the reduction of cooling and heating load. 13 1.2.2 Active Design Strategies Different from passive design strategies, active design strategies are adopted to use or generate electricity in the form of a system or structure or use electricity to help save electricity (like powering fans for better air flow). Most of the machine, equipment and infrastructure combined with active design to increase energy efficiency, such as high efficiency HVAC system, geothermal heat exchange. Smart control technology and design of a more efficient lighting system can be used. The active design strategies that have been successfully used are the choice of a more efficient HVAC (heating, ventilation, and cooling) system, smart lighting controls with LED bulbs, and geothermal heat exchange pump (utilizing the energy from ground). 1.2.2.1 HVAC System The optimization of HVAC systems intended to highly reduce building energy consumption and increase thermal comfort has been explored in the last decades. Statistics from many studies show that the HVAC system consumes 50 percent of building energy usage to adjust indoor environment (Nasruddin et al. 2019).The radiant cooling system has been considered as the energy efficient method to reduce energy demand for building conditioning in recent years, which has drawn more attention by more engineers and designers. The usage of water has better thermal capacity and pump are the reasons that the energy efficiency of radiant cooling system is higher than conventional HVAC system (Nasruddin et al. 2019). It is also more amenable to allowing operable windows. Coefficient of performance (COP) refers to the efficiency of heat pump, refrigerator, and air conditioning systems. The COP of a cooling system is the ratio of energy removed from the cold 14 reservoir to the energy input. What’s opposite, the COP of a heating pump is the ratio of heat delivered to hot reservoir to the energy input. It is important because a better heating or cooling system could deliver or remove a certain amount of heat with less work input which is the main part of HVAC system energy consumption. A COP of 3 or greater is common, which means that the system is moving three times as much heat energy as the energy input required. This makes up for the difference between site and source energy, which is expected to disappear in the future. 1.2.2.2 Lighting In the history of lighting controls, varied performance resulted from improper design, installation, programming and so on. Smart lighting controls are developed to solve those problems. The energy consumption of lighting in the commercial section is around 350 terawatt-hours (King and Perry 2017). Replacing lighting equipment with LED could achieve 30% of energy reduction (King and Perry 2017). The smart lighting controls is considered to have 44% more energy reduction on the base of that replacement (King and Perry 2017). Furthermore, an integration with LED luminaires, sensors combined with centralized control system is possible to decrease 90% energy usage compared to traditional lighting system (King and Perry 2017). 1.2.2.3 Geothermal Heat Exchange Pump A geothermal heat pump is a method of high efficiency to provide renewable energy that applied on residential and commercial buildings in some locations. The geothermal heat pump could provide energy for both heating and cooling load of inner space. It is not the method for the generation of energy or consumption of traditional energy sources but transfer of heat or cool energy caused by the temperature difference of underground and above ground which is natural 15 and renewable. The ground could be considered as energy storage for building section if utilized in a perfect way (“Geothermal Heat Pumps | Department of Energy” n.d.) . 1.2.3 On-site Renewable Energy Generation The NZEB that keeps an annual balance of the energy demand of building and renewable energy generation is highly depending on the on-site energy generation (Figure 1-5). The on-site renewable generation includes photovoltaic (PV) panels, wind turbine and solar thermal production. Because of its scalability and possibility to be placed with building, PV panels are significantly suiting for building on-site energy source (Luthander et al. 2019). But storage must also be provided to balance excess energy and low energy generation periods. This is usually done by connecting to the grid or supplying sufficient battery capacity on site. Figure 1-5 Energy flows between building and grid (ENERGY STAR n.d.) 16 As the method of PV panels’ electricity generated is depending on the solar collection, it is impossible to satisfy the building energy demand all the time, especially during the night, winter, and cloudy days, which is the same as traditional energy sources. On the other hand, the excess electricity generated from the PV panels could be sent back to the grid when there is an excess of energy being produced. Therefore, the possibility of a periodically unbalance between the energy demand and supply is relatively high. 1.2.4 Combination of Passive and Active Design Engineers and architects are using both active and passive design strategies to ensure comfortable living spaces and use energy efficiently by adaption of both passive and active design strategies. Generally, mechanical equipment is utilized to create energy and comfort in active design while passive design are strategies that reduce energy consumption by actual design. To optimize the energy performance, active and passive strategies should be integrated in building design. Emphasis on one design strategy may cause another strategy function less. Therefore, for several strategies working actively and passively on a project, their integration is important when designing to make the building most efficient. Energy simulation can be used to predict the behavior of these strategies before the building is built. For example, the orientation of a building could affect the energy performance of many other strategies, such as windows and natural ventilation. The solar intensity of window depends on the angle of penetrating incidence which is decided by the orientation. More solar radiation could be achieved when a larger angle is set with solar rays, especially at noon and in the summer. Therefore, the more solar heat gain will be provided to the inner space, which results in the temperature increase. In addition, the main wind direction of the buildings is a significant condition and should 17 be considered when creating the natural ventilation. The proper direction of windows incorporating the local main wind direction could provide more air flow for the building. 1.3 Energy Simulation A lot of effort has been put on reduction of building energy consumption and improvement of climate resilience for buildings. Engineers and specialists have been working on energy simulation to develop design strategies that are beneficial to increasing energy efficiency in last decades. The development of energy simulation is to explore a computational method to predict the results of multiple design strategies. Energy simulation is a computer-based analytical process that helps owners and designers to evaluate the energy performance of a building generally through the use of computer software designed to do it (Figure 1-6). Figure 1-6 The framework of the simulation plugin for the design tool (Han et al. 2018) 18 It could be conducted for ZEB to test energy savings based on different design strategies, so the modification could be made before the construction to make the building more energy efficient (Table 1-5). Table 1-5 Lists of energy simulation software Software Name Cost Stage Primary use Web EnergyPlus Open source Detailed Study https://energyplus.net/ IES VE Detailed study Office https://www.iesve.com/ Autodesk Ecotect No longer in use Early design Office No longer used anymore Openstudio Open source Early design Office https://openstudio.net/ Autodesk Insight Open source Early design https://www.g2.com/products/autod esk-insight/reviews DesignBuilde r $3,599/ year Detailed study https://designbuilder.co.uk/ Grasshopper Open source Early design https://www.rhino3d.com/6/new/gr asshopper/ DOE-2 Open source Detailed study https://www.doe2.com/ eQUEST Open source Detailed study https://doe2.com/equest/index.html HEED Open source Early design Residential https://www.sbse.org/resources/hee d SPEED Open source Early design https://speed.perkinswill.com/ Tangent (China) $400/ year Detailed study Commercia l/ Residential http://www.tangent.com.cn/cpzhon gxin/lvjian/997.html DeST (China) Open source Early design/ Detailed design https://www.buildenvi.com/softwar e/beea/dest PKPM- Energy (China) $2000 Detailed design https://www.pkpm.cn/index.php?m =content&c=index&a=show&catid =34&id=237 19 Some output from the results of energy simulation is utilized to evaluate the overall building energy performance. Energy use intensity (EUI) that represents the annual energy consumption relative to its gross square-footage is the key metric to compare the building energy consumption with the buildings in California Building Energy Benchmark Program. Data of 1,252 buildings (gross floor area range from 50,001 ft 2 to 100,000 ft 2 ) were collected in California, the median weather normalized site EUI was 49 kBtu/ft 2 in 2020 (Figure 1-7)(“Workbook: Energy Efficiency Benchmarking Dashboard” n.d.). Figure 1-7 Site Energy Use Intensity (“Workbook: Energy Efficiency Benchmarking Dashboard” n.d.) The comparison of office and other commercial building type were showed (Figure 1-8). In the dynamic simulation, the calculation thermal balance is needed for each time step of energy simulation. 20 Figure 1-8 Commercial building EUI by Property Type (Edelstein 2017) Cooling and heating demand are the critical metrics to keep this balance, which are highly depending on the weather condition and design of building, as they the main energy use for keeping the comfort of indoor environment. Unlike them, internal heat gains, such as lighting heat gains, receptacle heat gains and people heat gains, which are depending on the regulation of building energy codes. 1.3.1 Weather Data Weather data is critical for the simulation of energy use of a building because energy use is partially dependent on where the building is located. Weather data with good quality is essential for the accurate simulation results. The dynamic simulation requires weather data with hourly (for IES minutely) value that including the temperature, rain, radiation, humidity, and wind flow; also, 21 the elevation and time zone should be contained, which are all concluded in the weather files from Energy Plus website. A typical meteorological year (TMY) is a collection of weather data which in the duration of more 12 years with hourly data values in a year for a given geographical location. For every period of time selected from a year, it is selected as a typical one in the whole duration, which is more accurate over an average than a given year (Sawaqed, Zurigat, and Al-Hinai 2005). As the weather condition of one year may differ drastically comparing to a range of years, a specific year could hardly represent the climate of a location. The climate data files in IES VE are from various places, most of which are directly from Energy Plus website and converted in the standard format of FWT. But it accepts both FWT and epw files. All simulating details such as thermal zones, occupancy loads, air-conditioned and non-air- conditioned space, and HVAC settings which combined with different strategies should analyzed and concluded (Sun, Gou, and Lau 2018). FWT files are the file type primarily used in IES VE for weather data, which contain an abbreviation that points out where the data was obtained. Epw files are the Energy Plus format that contain analyzed and organized weather data. Weather files are also being constructed for predicted climate change values. 1.3.2 Occupancy and Zones Not only is weather a changing factor that effects each hour of energy calculation, but so is the occupancy in the building. Some software programs only allow a simple calculation for this; others let the user put in a table of work hours, assumed numbers of people working and even holidays (Figure 1-9). 22 Figure 1-9 Occupancy Table in IES VE To achieve even more efficient and comfortable buildings, many buildings, including some single- family residences, are set up with multi-zone HVAC systems that must be simulated properly. For example, in an office building, multiple zones might include conference room, computer room, warehouse as the specific needs of those areas. A multi-zone thermal model created with detailed inputs such as, conditioning, lighting, and other conditions of each zone because of calculation and input purposes. The input from the efficiency characteristics and operating schedules of the equipment is one of the key points to make a simulation works. In addition, the software will do simulation of each thermal zone under well-defined boundary conditions (Wulfinghoff et al. n.d.). 1.3.3 Simulation Inputs and Outputs To achieve simulation results with accuracy, a lot of parameters should be taken into consideration, such as construction material thermal properties, building geometry, window thermal properties, lighting, equipment, HVAC system, DHW, control strategies and so on. Also, the building type, occupancy schedule, cooling, and heating setpoints are significant parameters. The standard simulation process is using input parameters mentioned above and conducting the simulation 23 through the duration of a year under the certain weather condition. Outputs of energy simulation include delivered energy (heating, cooling, lighting, equipment, fan power and pump), source of energy carriers, cost, and the amount of carbon emissions. The outputs could be visualized in format of graphs, charts, and summaries in the period of a certain day, week, month, and year. EnergyPlus and IES VE contain an integrated simulation engine, while DesignBuilder and eQuest are docking to a certain engine. Honeybee and Autodesk Green Building Studio are the plugins for other software. EnergyPlus fits more for small projects with specific studies; eQuest is very useful for the projects with time limitation while IES VE with Apache engine could provide handy outputs (Table 1-6). Table 1-6 List of Software with Simulation Engine 1.3.4 Building Energy Modeling Example Building energy modeling (BEM) is a methodology that utilizes computer-based simulation software of analyzing building energy consumption (Figure 1-10). BEM could be utilized in Software Simulation engine IES VE Apache EnergyPlus EnergyPlus DesignBuilder EnergyPlus EQuest DOE-2 Honeybee Rhino Green Building Studio Autodesk 24 multiple use, for example, green certification, retrofit and new building design, code compliance and real-time building control. It can also be adopted in the development of building energy code and policy creation (Franconi et al. 2013). The BEM is working to represent building. The model is built by the modeler with the input of data from climate condition, construction materials, occupant schedules, HVAC system and building geometry. The calculation engine solves equations rooted in thermodynamics and building science. The running time of simulation depends on the detail and complexity of building, which may cost seconds to hours to run the model for a building simulation. The results perform a year of calculation on an hourly or shorter basis that include daylighting, mechanical equipment usage, energy cost, cooling, and heating load (Franconi et al. 2013). The BEM programs could also work on the system interactions like natural ventilation and cooling. 25 Figure 1-10 Base Model in IES An example BEM (from Autodesk Insight) included a 3d model of a building with no plenum space (Figure 1-11). Each floor of the model was divided into five zones (Figure 1-12). Figure 1-11 BEM from Autodesk Insight 26 Figure 1-12 System Zone Division of Insight Model In the model, the location was set as the Los Angeles International Airport (Figure 1-13). The occupancy profile was selected “24/7 Facility” which represented that building operating 7 hours every day of the workday (Figure 1-14). 27 Figure 1-13 Selection of weather file of Insight model (Location of Los Angeles International Airport) 28 Figure 1-14 Building Operating Schedule of Insite Model Sample outputs from Autodesk Insight include EUI which is shown with a base case and different options (Figures 1-15 and 1-16). The outputs of the sample model from Autodesk Insight showed the energy demand could be changed resulting from application of design strategies. The EUI of the base model from Insight was 58.8 kBtu/ft 2 which was over the ASHRAE 90.1 standard (Figure 1-15). The energy demand was further lowered to 22.7 kBtu/ft 2 after the integration of design controls were applied; this was the lower than the Architecture 2030 goals (Figure 1-15). The exact EUI difference is shown for each strategy (Figure 1-16). Some of them could result in the large difference while others can only cause slight or no difference of the building energy consumption of the model. For example, the HVAC system with high efficiency reduced the EUI of model almost 10 kBtu/ft 2 every year (Figure 1-17). However, the modification of WWR of western wall only contributed to an annual 0.8 kBtu/ft 2 reduction to the model (Figure 1-18). 29 Figure 1-15 EUI Change of the Insight Model with Different Strategies (1) Figure 1-16 EUI Change of the Insight Model with Different Strategies (2) 30 Figure 1-17 EUI Difference of HVAC Systems with Different Efficiency Figure 1-18 EUI Difference between Different WWR of Western Wall 31 1.3.5 Integrated Environment Simulation Virtual Environment (IES VE) IES is identified as a 3D building performance analysis software for building energy simulation widely used in EU and United States. An easy-to-use graphical user interface (GUI) make it easier for the team to utilize in design, construction, and operation phases. IES provides multiple functions for users to conduct the design, such as examining different strategies, analysis energy consumption on different aspects (hourly or sub hourly), comfort of occupant, lighting control, airflow, CO2 emissions and cost with the compliance of ARHRAE 90.1, Title 24 and LEED rating system. In addition, the latest features allow users to conduct minute by minute dynamic energy simulation to get more detailed data for specific results, such as the cooling demand for each room every minute, so the engineers could have a better understanding of the building energy consumption. Just like the Autodesk Insight example, IES <VE> would include a 3d model, tables of information about the building’s materials (Figure 1-19) (Figure 1-20) (Figure 1-21), occupancy charts (Figure 1-22), and zones division (Figure 1-23). A weather file is also necessary for the accuracy of energy simulation (Figure 1-24). 32 Figure 1-19 Roof material table in IES VE Figure 1-20 Window material table in IES VE 33 Figure 1-21 External wall material table in IES VE Figure 1-22 Weekly Occupancy Profile in IES VE 34 Figure 1-23 Interior Zones Division in IES VE Figure 1-24 Simulation Weather File in IES VE Sample outputs from IES <VE> include the energy report that shows annual Site Energy, Source Energy, CO2 Emissions per unit area, annual breakdown of energy consumption and Site Energy Use Intensity (Figure 1-25). In addition, the graph, charts, and summaries could be used to show certain, specific results, such as the monthly room sensible cooling through the year (Figure 1-26); the graph of external conduction, solar gain, air temperature and Air Dry-Bulb temperature of one zone in a specific day (Figure 1-27). 35 Figure 1-25 Energy Report in IES VE 36 Figure 1-26 Monthly Room Cooling Sensible Load through the year Figure 1-27 The External Gain, Solar Gain, Air Temperature and Dry-Bulb Temperature of a Zone on July 1 st 37 1.4 Energy Codes in California and China Building energy codes that create the criterion requirements and guide construction are a subdivision of building codes. The utilization of building energy codes is for the assurance of energy demand reduction in the building life cycle by setting minimum energy requirement for new and renovated buildings. Because the decisions in the design stage of buildings could highly affect the building energy performance, building energy codes provide opportunity for designers to assure the effect of energy efficient design, technologies, and construction strategies. 1.4.1 Energy Codes in California The California Building Energy Efficiency Standards (sixth section of California Building Standards Code, Title 24) is intended to support the public human health, environment, and clean energy goals; it was first developed in 1976. A new version is adopted by California Energy Commission (CEC) every three years to reduce the carbon emissions from building and increase the energy efficiency (“California Energy Commission” n.d.). The 2019 California Energy Code focuses on four key aspects: smart residential photovoltaic systems, update of thermal envelope standards, residential and commercial ventilation, and nonresidential lighting requirements. According to those upgrades, energy consumption in new residential buildings will be cut by more than 50%, at the same time, nonresidential building section will reduce 30 percent energy consumption (because of the lighting requirements) (“California Energy Commission” n.d.). Title 24 standards effectively require that new single-family homes and multi-family dwellings up to three floors must have solar photovoltaic systems starting in 2020, so the Californians could generate their own clean energy. In addition, the ventilation measures will benefit to 38 increase indoor air quality and prevent outdoor and indoor air pollution (“California Energy Commission” n.d.). The 2022 California Energy Code would have gone into effect if approved by California Building Standards Commission (CBSC). In new residential and commercial building section, 2022 Energy Code targets on four aspects: electric heat pump technology, establishment of electric-ready requirement, extension of solar photovoltaic system and battery storage standards, and strengthen of ventilation standards (“Energy Commission Adopts Updated Building Standards to Improve Efficiency, Reduce Emissions From Homes and Businesses” n.d.). In the 2022 California Energy Code, some sections were changed to increase the building energy efficiency and indoor human comfort. For example, the standards of photovoltaic panels and battery storages have been expended. Also, the part of ventilation of code has been strengthening to increase the indoor air quality. In Title 24, California is divided into sixteen climate zones (Figure 1-28). It is essential for designers and builders to be familiar with climate zone where the project is located, because some requirements for envelope and fenestration are different based on the specific climate zone that building is located in. 39 Figure 1-28 Climate Zone Map of California (Title 24 2019) There are two methods to achieve California Building Codes compliance, prescriptive and performance. The prescriptive method is a simpler but less flexible method for the energy codes compliance as all the elements should meet the minimum energy requirements (Title 24 2019). For example, if one chooses to use the prescriptive method it is required that the nonresidential building maximum WWR is 40%, then design of WWR should not be over that value; and the wall insulation requirement for non-resident building should not be higher than U=0.069 Btu/h ft 2 F (Metal frame, climate zone 6), then the adopted wall insulation U-value must lower than 0.069 Btu/h ft 2 F. In addition, as no trade-off of energy consumption, a component meets the criteria will not result in the reward for compliance. 40 The performance approach, by contrast, uses a computer method approved by Energy Commission (Title 24 2019). It provides high flexibility and accuracy as the possibility of calculation for energy trade-offs among all the measures, however, at a cost of the requirements of effort. Software programs that can be used to achieve the performance requirements are California’s Building Energy Code Compliance Software-Residential (CBECC-Res), EnergyPro, Right-Energy Title 24, California’s Building Energy Code Compliance Software-Commercial (CBECC-Com) and IES VE. ASHRAE Standard 90.1 has been considered as a benchmark for commercial building energy codes in United States and has resulted in the significantly improvement of building energy consumption in United States since 1975 (Figure 1-29). It is also a crucial base for the codes of other countries for last decades. Excluding low-rise residential buildings, the minimum requirements of energy efficiency is provided in ASHRAE Standard 90.1 for new buildings’ design and construction, new portions of buildings, systems, and the equipment in existing buildings. In addition, this standard provides regulation for building thermal envelope, HVAC system, lighting, and power system. However, the energy loads for appliance are not considered in the standard. ASHRAE 90.1 or equivalent standards are applied by most of the states for all the commercial buildings while the other states are using it only on government projects. ASHRAE generally used as a baseline for the comparison with new designs. It is also referenced in the LEED building certificate process. The allowable normalized energy use has gone down since 1975 (Figure 1-29). 41 Figure 1-29 Improvement in ASHRAE Standard 90.1 (National Impact of ANSI 2016) 1.4.2 Energy Codes in China As the climate conditions are the most significant factors that affecting the building energy performance, the building energy codes in China are developed based on the climate zones. Five climate regions were divided: Severe Cold, Cold, Hot Summer Cold Winter (HSCW), Hot Summer Warm Winter (HSWW). For the Severe Cold and Cold climate zones, space heating system in the building for urban area is mandatory and provided as public service in winter (Figure 1-30) (J. Li and Shui 2015). 42 Figure 1-30 The Divisions of China’s Climate Zones (J. Li and Shui 2015) The classification of building section in China is different from the common definition in western countries. The Chinese Ministry of Construction developed its own regulation. Public buildings include governmental offices, commercial buildings, service industries, education, hospitals and others (Figure 1-31) (J. Li and Shui 2015). Public buildings usually defined as general public buildings that smaller than 20,000 m 2 and large size public buildings that larger than 20,000 m 2 (J. Li and Shui 2015). Most non-residential buildings are publicly owned while most of residential buildings are mostly private properties, which is a reflection economy characteristics and governance structure in China (J. Li and Shui 2015). 43 Figure 1-31 Building types in China (J. Li and Shui 2015) Table 1-7 Building energy codes system in China (J. Li and Shui 2015) The reason that equipment performance is not included in Chinese buildings codes is because of the different mode of energy supply which causes the significantly variation of average energy consumption. The large difference varies between different heating supply. The main part of heating areas in China, Severe Cold and Cold zones, district heating is adopted with burning the coal. This is different in United States, natural gas provides most of energy use for residential and 44 commercial buildings. The energy consumption of individual coal stove is almost four times as the combined heat and power. 1.4.3 Los Angeles Versus Harbin The main differences between the California versus China energy codes are the organizational structure. The standards in California like ASHRAE 90.1, IECC and Title 24 are functioning individually while the in China, all the cities and provinces should follow the national laws; orders by the State Council; and rules, standards, and plans issued by ministries and departments (Hu and Qiu 2019). Specifically, for Los Angeles and Harbin, the differences are the goals set by the different codes. The Title 24 in California has set a goal that new residential buildings achieve zero energy consumption by 2020, and new commercial buildings will be net zero by 2030; also, half of the retrofit commercial buildings will be NZEB by 2025 (“Zero Net Energy” n.d.). However, the new and revised building energy standards issued by Ministry of Housing and Urban–Rural Development and Ministry of Finance and State Council set the goal is in China, including Harbin, to achieve an overall energy efficiency level of 82% in residential buildings and 79% in commercial buildings by 2030, compared to the 1980s baseline performance (Hu and Qiu 2019). In addition, comparing to the codes in Harbin, Tile 24 has regulation covered more aspects of the building sections, such as the lighting, service hot water and pumping, electrical power, and renewable energy. In addition, more than half percentage of the electricity generation are provided by coal in China while hydropower (19%) and wind (10%) take up almost 30% of United States’ energy consumption. (Figure 1-32 & Figure 1-33) 45 Figure 1-32 China Installed Electricity generating capacity ( U.S. Energy Information Administration (EIA) 2020) Figure 1-33 California Electricity Generation (“File:California Electricity Generation Sources Pie Chart.Svg - Wikimedia Commons” n.d.) 46 1.5 Reality Versus Simulation There are some reasons that result in the difference between reality and simulation, the energy use of the real building versus that predicted by similar, including inaccurate design parameters, lack of consideration for uncertainties, the design of inefficiency and over-complicity, lack of experience and feedback and lack of post-construction testing (Zou, Wagle, and Alam 2019). The weather changes, occupants’ use of the space are also the factors that cannot be neglected. Environmental uncertainties are happening more often as it is related to climate change. The use of synthetic weather data files is based on the average of a long period. However, the uncertainties will still happen because the climate change. Occupant is another factor that affects the difference between reality and simulation. For example, if residents of building have sense of energy saving and the operating schedules. Even though there is always the difference between reality and simulation, more strategies could still be considered to avoid or reduce the difference (Sanders 2021). 1.6 Summary Buildings are one of the big contributions to the global warming and climate change. Energy efficiency should be increased to reduce the amount of energy used and greenhouse gasses produces. Even though the definition of NZEB is relatively new, many countries have started to adopt this concept in the building energy policy and codes. Net Zero Energy Building status is an achievable goal. It represents the strategies combined the reduction of building energy demand and generation of on-site renewable energy to gain an annual energy balance. To get this balance, a series of passive and active design strategies should be applied to the building. Passive design controls include building orientation, construction insulation, design of window, natural 47 ventilation and so on. Active design strategies include high efficiency HVAC system, lighting, geothermal heat exchange pump and so on. There are more energy efficient design strategies to reduce building energy consumption and energy efficiency, but the specific condition of building should be considered carefully in the design stage. While some design controls will have positive or negative effect on the others, it takes effort to find the optimized integration of all of them to have an optimal building energy performance under the condition of assuring the indoor environment quality. The integration of many methods could significantly increase the building energy efficiency. These strategies will probably differ depending on the location of the building. Still, this is an important process to achieve the goal of Zero Energy Building. Energy simulation is the methodology to evaluate the sustainable design and optimize the integration of design strategies. In addition, the output of energy consumption and costs could be the data for owners and designers to analysis the project. There is still requirement of more actual ZEB to test in the same climate zone, even though accumulation of the research and practice have been conducted a lot. On-site energy production can be used to make up any shortfall. This chapter defined what a net zero building is, discussed different strategies for achieving energy efficiency, explained energy simulation, compared American and Chinese energy codes, and briefly explored why building simulation may not match reality. 48 Chapter 2. Background Research The urbanization of United States will reach 67% that is a record high by 2050 (Raji, Tenpierik, and Van Den Dobbelsteen 2015). Every week, a new city is needed for more than a million new urban residents in the world (Raji, Tenpierik, and Van Den Dobbelsteen 2015). With the development of urbanism of this high speed, a large amount of energy will be consumed in next decades. Therefore, owners, designers and operators should take sustainability of projects as a priority in the stage of design, construction, and operation, which means that the projects should be adopted with the features of climate-resilience and renewable energy generation. The buildings with the environment controlled by high technology now, were developed from the buildings only with passive systems (Athienitis & O’Brien, 2015). The adoption of new materials and technologies drove the development of this evolution in building section. For example, the utilization of electric lighting in early 20th century resulted in the reducing of area of window; the invention and adoption of insulated glass unit (IGU) with coating in building sector in 1980s resulted in the increasing fenestration area of the façade system, which caused the large amount energy consumption of HVAC system (Athienitis & O’Brien, 2015). Then the exploration of the energy converted from solar radiation in 1990s led to the concept of NZEB (Athienitis & O’Brien, 2015). NZEB is defined as the annual energy consumption of the building could be supported by the on-site renewable energy generation. This chapter discusses understanding the factors that affect the building energy performance, limitation and challenges of the balance between energy consumption and renewable energy generation, energy simulation utilized as a decision support tool, and a gap between energy simulation and actual building performance. 49 2.1 Understanding the Factors That Affect the Building Energy Performance Some factors and design strategies could have serious influence on the building energy performance, such as the local climate condition, façade design, construction insulation, natural ventilation, lighting, and HVAC system. Understanding of local weather conditions is beneficial to reducing the resilience of building from outdoor effect and increasing the positive impact of passive design strategies. The smart control system, efficient HVAC, and lighting system are usually adapted in the NZEB to provide high energy efficiency and energy savings. The energy simulation process is an effective methodology to find the optimized integration of all the strategies for the accurate prediction of the building performance with the compliance of building energy code. 2.1.1 Weather Condition The weather conditions affect building energy consumption, such as HVAC system (heating, cooling and mechanical ventilation) and lighting. Thus, it is essential for architects and engineers to be conscious of the importance that how weather data could affect the building performance simulation as the results of the simulation inform the designer and engineer as to strategies to use for constructing the real building. The energy usage of HVAC system is highly affected by the local climate as the natural factors, temperature, solar heat, humidity, and wind speed. According to the results of energy simulation from Tianzhen, weather has more impacts on the buildings in hotter and colder climates; the local climate could affect the energy consumption of heating, cooling and mechanical system a lot, especially for the medium or larger than medium office building in cold climate, while the influence of office buildings in small scale is relatively low comparing to others (Hong, Chang, and Lin 2013). In addition, weather can affect both the peak 50 electric demand and energy consumption of office building section; nevertheless, the impact on the peak demand is even greater. The simulated energy use using the typical metrological weather data is not necessarily representing the average energy use using the actual meteorological year across the 30-year period, and the typical metrological year results can be significantly higher or lower than those of the actual meteorological year (Hong, Chang, and Lin 2013). Modified weather files can also be used to simulate conditions that could happen in the future. This is important if a building is expected to last a long enough time that the climate could change during its lifetime. In the modified weather conditions, the heating energy consumption reduces from 21% to 22% each year, while the cooling load from each year jumps from 29% to 31% (Farah et al., 2019). Comparing to the weather files that without considering the climate change, the total cooling and heating load reduces around 4% (Farah et al., 2019). The data showed that modified weather files is essential to dealing with the effect of climate change on building energy consumption. 2.1.2 Building Facade The building envelope, consisting of windows, roof and walls, has a critical impact on the energy consumption overall. It regulates the building interior environment, which represents the energy demand of heating, cooling, and ventilation. In US, 34% of energy use in commercial building sector is related to window, which was estimated by the Lawrence Berkley National Laboratory (Apte & Arasteh, 2008). Even though office buildings are mostly dominated by internal gains, an appropriate design of façade system could result in 10% - 40% energy reduction on lighting and mechanical system based on climate (Troup et al. 2019). In addition, the improvement on the materials of components that make up the façade system could have a positive impact on some energy demand, but may have opposite influence on other aspects, which made it crucial to conduct the evaluation of the building performance as an entity (Pacheco, Ordóñez, and Martínez 2012). 51 2.1.2.1 Window to Wall Ratio (WWR) WWR represents the ratio of glazed area to the area of exterior wall. It has influence on building energy consumption and indoor human comfort because of the heat transfer, solar radiation, air leakage, ventilation and daylighting (Troup et al. 2019). In cold climate, higher WWR increased the heating demand as more heat loss (conduction); in warm and hot climate, more solar heat gain penetrated to the indoor space because of higher WWR; although larger window area could achieve more daylighting, the electric light could not be replaced by daylight (Troup et al. 2019). 2.1.2.2 Window U-factor and SHGC Different facade elements can result in energy saving in an office building. “The solar reflectance, U-value of the opaque parts, U-value of the windows, and solar heat gain coefficient (SHGC) of the windows were the considered facade properties. SHGC reduction was found to be the most effective means of reducing the annual energy demand, followed by reduction in the window-U value and then increase in the solar reflectance (Ihara, Gustavsen, and Jelle 2015). 2.1.2.3 Window Overhang Window overhangs s are utilized to prevent too much solar radiation and glare. The simulation of a box model which was conduct by COMFEN (EnergyPlus interface) in the location of Kolkata, India, the results of which showed that total energy usage was reduced by 3.0% when applied horizontal overhang (Ghosh and Neogi 2018). In addition, an evaluation of how solar shading affects tropical low income buildings on thermal comfort in Uganda was performed through EnergyPlus too (Hashemi and Khatami 2017). Results presented that overhang was one of the most effective solar shading strategies in the hottest seasons of the year. 52 2.1.2.4 Glazing Visible Transmittance (VT) The glass of windows with different VT could have influence on building energy usage. The glass with low VT reduces the daylight penetration and too much solar heat gain. At the same time, the illuminance levels will be not enough which may increase the energy for electric lighting. Thus, it is important to design the proper VT value for the glazing to have an optimum window performance based on the local weather conditions. An eQUEST simulation was created to test the effect of integration of strategies for facade on the energy consumption of building (Pacheco, Ordóñez, and Martínez 2012). A reduction of up to 25.92% in the heating and cooling energy consumption was achieved by utilizing an optimized integration of strategies of insulation, WWR, glass, and shading systems (Pacheco, Ordóñez, and Martínez 2012). 2.1.2.5 Building Insulation The insulation of building in beneficial to increasing the climate resilience. It helps improve the level of human comfort and reduce the size of HVAC system, because the indoor environment is not easily affected by outdoor environment (Al-Homoud, 2005). Therefore, the building energy cost could be highly reduced if the utilization of thermal insulation is properly selected. The building energy performance could be seriously improved by choosing the proper envelope components with appropriate materials. The insulation of roof and wall are highly suggested in all kinds of climate condition as it is beneficial for heat loss by conduction through the building envelope (Al-Homoud, 2005). And roof insulation could affect the building performance than wall insulation, which should be paid more attention (Al-Homoud, 2005). 53 2.1.3 Natural Ventilation Natural ventilation is based on the positive psychological effect of air flow blowing through the building inner space to provide human comfort (Pisello et al. 2016). It can improve well-being feeling of residence when inside and outside has the similar air condition (temperature and humidity). The combination natural ventilation and proper infiltration rates, combined with adaptive design to local microclimate, is not only beneficial for the indoor air quality but also the energy cost of building. For example, night cooling is designed with consideration of both natural ventilation and local weather condition to have positive performance on reduction of cooling load (Pisello et al. 2016). Night cooling is based on the lower temperature during the night, so the operation of natural ventilation could be conducted to cool the building inner space and provide comfort environment for the next day without using the electricity. This technique works very well in hot-dry climates with a strong diurnal swing in temperatures. The evaluation on how natural ventilation on the building interior space in the aspect of building energy demand was conducted. Other studies concentrated on the prediction of ventilation performance and the cooling ability natural ventilation system in office building sector (Pisello et al. 2016). Instead, this research utilized preliminary sensitivity analysis and numerical analysis to evaluate the importance of natural ventilation for predicting the energy demand of building in the condition of dynamic simulation environment (Pisello et al. 2016). Natural ventilation is beneficial to reducing the energy consumption by blowing the fresh into the indoor environment instead of mechanical ventilation. In addition, the fresh air provided by natural ventilation could blow the indoor hot air away to reduce indoor air temperature without using the 54 HVAC system. The energy could be saved by 10% - 30% in a favorable climate when natural ventilation utilized as air conditioning system (Walker, 2016). 2.1.4 Orientation If the building orientation could have influence on the building energy consumption is based on the location of building. In the area of northern hemisphere (latitude larger than 22.450), the sun position is in the south side of building, vice versa (Lapisa, 2018). For the location in cold climate, the wall with more area of windows should be facing sun position for more solar penetration. For the area near equator, the sun position is changing from northern and southern direction during the different season. Therefore, the walls with large area of windows facing south instead of facing east-west to reduce the solar penetration. Building energy consumption could be reduced 30 percent, if a south facing building turned east-west direction (Koranteng & Abaitey, 2009). 2.1.5 HVAC System HVAC system is usually utilized to provide cooling, heating and mechanical ventilation to the building-built environment, the energy consumption of which makes up 50% of building energy usage and 20% of electricity usage in U.S., so engineers are seeking methods to increase the HVAC system energy efficiency (Pérez-Lombard, Ortiz, and Pout 2008). A study conducted by Fasiuddin and Budaiwi of five HVAC systems on their energy efficiency for commercial buildings, such as PMZ, VAV, CAV-Rh, MZ and TPFC. The results of the study showed that VAV system saved energy by 22.5% while PMZ system consumed 14% more energy (Fasiuddin and Budaiwi 2011). CAV-Rh system achieved electricity saving by 6.4% (Fasiuddin 55 and Budaiwi 2011). MZ system saved energy at 8.3%, but it could not control the indoor air temperature very well (Fasiuddin and Budaiwi 2011). All the design strategies mentioned in the previous paragraphs were listed in first column based on their results of energy efficiency from IES energy simulation (Table 2-1). Also, three lists from other ranking resources were considered as the comparison for the analysis (Table 2-1). The Building Designer Ranking in Khan’s research was based Professor Marc Schiler’s intuition (second column) (Khan, 2021). The Regression Ranking (third column) and Decision Tree Ranking (forth column) were from the results of data analysis through two methods, stepwise regression and decision tree (Khan, 2021). 56 Table 2-1 Building Energy Efficiency Importance Ranking Thesis Study Results Building Designer Ranking REGRESSION RANKING Decision Tree Ranking Citation (Khan, 2021) (Khan, 2021) (Khan, 2021) 1 HVAC System HVAC System Cooling System Occupancy Density 2 WWR Occupancy Density Glass Type Floor Height 3 Glazing SHGC WWR Heating System Heating System 4 Window Overhang Roof Material Shading Cooling System 5 Wall Insulation Wall Material Orientation WWR 6 Roof Insulation Window Glass Type Wall Insulation Stories 7 Window U- factor Orientation Rise_N (Low/High) Shading 8 Orientation Shading Window Type Wall Material 9 Glazing VT Window Frame Type Occupancy Density Window Glass Type 10 Stories Roof Material Ceiling Height 11 Floor Height Building Footprint Roof Material 12 Ceiling Height Orientation 13 WWR Window Frame Type 14 Floor Area Size 57 2.1.6 The Optimized Integration of All the Design Strategies Based on the climate, usually only one or some design controls applied to the building can achieve a certain amount of energy requirement reduction, but not a NZEB. The integration of many design strategies represents a better interaction of the most energy efficient design controls for a certain building in the corresponding conditions, such as local climate, function of building, thermal properties of construction materials, HVAC system and so on. The effectiveness of optimal integration for improving energy goals, such as building energy demand, indoor environment quality, is of the highly significance. It provides an opportunity for engineers to select a proper methodology from a brunch of integration of strategies that could best achieve the design objective. For the studies conducted in the cold and temperate climate zones by Hasan, the design strategies that consist of the optimization are U-factor of windows, heat recovery system, insulation of external walls, roof and floors (Hasan, Vuolle, and Sirén 2008). The research results claimed that 23-49% heating energy reduction was achieved for the house with optimal integration of design measures comparing to the reference detached house (Hasan, Vuolle, and Sirén 2008). Thirteen design control were tested through the energy simulation by EnergyPlus for small and large office building in United States. The design strategies including window upper and lower positions for four directions, cooling air supply temperature, heating and cooling setback set point temperature for night and whole day (Kämpf et al. 2010). An energy savings up to 30% was performed by utilizing the optimal algorithms (Kämpf et al. 2010). Concluded from those results, the utilization of optimized integration of design strategies could result in a total energy demand reduction from 20% to 30% (Nguyen, Reiter, and Rigo 2014). 58 Nevertheless, the performance of optimization of integration is functioning differently in warmer climates. An EnergyPlus simulation model of three residential units in 2 running modes in 3 hot humid climates was improved by thermal comfort and energy demand through the application of optimization (Nguyen 2013). The design strategies including window size, overhang, external and internal wall insulation, glazing type, natural ventilation, and infiltration rate. Compared with the base building model, the discomfort periods were reduced by 86% in the dwellings(Nguyen 2013). Some integrations of active and passive design strategies are utilized by the architects and engineers to provide high energy performance buildings (Table 2-2). 59 Table 2-2 Integration of design strategies Integration of design strategies 1 Integration of design strategies 2 Integration of design strategies 3 Shading Window size Window position Insulation Overhang Window direction Natural ventilation Wall insulation Cooling air supply temperature Daylighting Glazing type Cooling setback set point Natural ventilation Heating setback set point Infiltration rate 2.2 Limitation And Challenges of The Balance between Energy Consumption and Renewable Energy Generation NZEB is widely considered as an environment friendly solution combined energy and GHG reduction in building section. With the use of photovoltaics or potentially also wind power, it is possible that excess energy from the on-site generation can be sent back to the grid. Moreover, the well-developed building systems for renewable energy generation have been considered to apply in NZEB (Deng et al., 2014). However, the limitations and challenges of the development of NZEB cannot be neglected as the balance between energy consumption and energy generation is still difficult to be implemented. 2.2.1 The Generation of On-Site Renewable Energy The renewable energy consumption is 12.5% of the total energy consumption of United States in 2020, and will increase to 17% by 2050 with an annually growth of more than 5% (Figure 2-1) 60 (U.S. Energy System Factsheet | Center for Sustainable Systems, 2021). However, only 2.3% of the electricity was generated by the solar technologies (Photovoltaic Energy Factsheet | Center for Sustainable Systems, 2021). Figure 2-1 U.S. Energy Consumption by Source, 2020 (U.S. Energy System Factsheet | Center for Sustainable Systems, 2021) The electricity generated from renewable sources is increasing since 1950 and the utilization of coal to generate electricity started reducing around 2006, while the usage of natural gas is increasing since 1950 (Figure 2-2) (U.S. Energy Information Administration, 2021). 61 Figure 2-2 U.S Electricity Generation from All Sectors (U.S. Energy Information Administration, 2021) Photovoltaic panels are the semiconductors that utilizes solar energy to generate electricity. There is a high initial cost compared with the low amount of cost for the maintenance and operation and the cost for energy generation is inverse to the life span of PV panels (Ayompe and Duffy 2014). The PV panels can be categorized into crystalline, thin film and emerging based on the materials (Figure 2-3). A silicon-based modules was proved to have life span around 20-30 years (Ayompe and Duffy 2014). The efficiency of PV panels with the best technologies is under 30% (Shafique, Luo, and Zuo 2020). 62 Figure 2-3 PV Technology Types and Efficiencies (Photovoltaic Energy Factsheet, 2021) In addition, temperature and cloud can affect the amount of electricity generation. In cold temperature, PV panels are working with higher efficiency as the low temperature results in the more production of voltage which generate more electricity. or the temperatures above 77 degrees, PV panel will become one less efficient (Gambone, 2021). Even though the PV panels are more efficient in the cold weather, they don’t necessarily produce more electricity in the winter than the summer, as the longer time of daytime results in the more electricity. Consider with all the condition, 70% to 90% energy generation will be reduced in cloudy days compared with the sunny days (Gambone, 2021). The first investment tax credits (ITC) was issued for the encouragement of renewable energy in 1978 in U.S. (Solangi et al. 2011). Both residential and business tax credits were provided for the investment in solar or wind electricity generation. In addition, it is a consistent policy extended until. In China, after the cost of PV panel electricity generation was largely reduced, government realized the potential of it and started highly encouraging the utilization of it. The policies and 63 regulations were issued to encourage power companies, such as higher electricity price than conventional price is allowed, allowances for the renewable resources industry (Solangi et al. 2011). 2.2.2 The Limitation of Active Design Strategies The design of active design strategy highly affects its efficiency. A proper design could provide high level of indoor environment for occupants and increase the building energy efficiency. Multiple factors and uncertainties should be considered carefully during the design phase, such as the weather conditions, occupancy capacity, building floor plan and so on. For example, an oversized HVAC system results in the unnecessary high initial cost and energy consumption during the operation phase, while underestimating the cooling and heating load of building will result in the indoor comfort problem. 2.2.3 Challenges of Passive Design Strategies Passive design strategies have the ability to direct and indirect impact on the energy demand of mechanical and electrical systems of buildings, so they are considered as the foundation in the design of NZEB (Aelenei et al. 2013). Passive controls are known as taking advantage of natural forces, weather, and geographical conditions, to reduce the energy consumption of building. Therefore, there is some challenges in the process of passive strategies design because of the uncertainty of natural environment. For example, when considering the effect of solar radiation on a building interior temperature, there is a balance between the usage of solar radiation and prevention of overheating (Aelenei et al. 2013). It is important to keep the balance between U- value and g-value (used in Europe, very similar to SHGC). The combination of low U-value and 64 high g-value is proper for the projects pursuing high heat performance in the cold climate. On the contrary, facing with the cooling loads, the passive design strategies are used for the intension towards solar heat gain minimization and prevention of cooling loss. 2.2.4 Passive Design and Active Design The integration of passive and active design strategies has been extensively adopted in ZEBs in recent years. Photovoltaic panel, high value insulation, and heat pumps are utilized with high frequency by designers. At the meantime, high performance of façade, and electric appliance and HVAC system were widely used too (Yu et al., 2019). With the development of both passive and active strategies technologies, it is essential to seek more mature methodologies to integrate them. Some integrations consist of different strategies were proposed continuously: passive design, service system and renewable energy generation (Deng et al., 2014); passive design strategy like building envelope, orientation, geometric/ratios, active design strategies such as HVAC, hot water, lighting, appliances, hydroelectric power, PV panel and wind turbine (Yu et al., 2019). 2.3 Energy Simulation Utilized as A Decision Support Tool Simulation is beneficial to understanding the complexity of building energy use before the building is constructed. It can also be used to study integration methods for the features that affecting energy balance of buildings, such as exterior climate, the geometry of the building, internal heat gains, and the HVAC system. One analysis of the factors resulted in an explanation of the complicated relation between the energy consumption and energy supply (Bot et al. 2019). The interaction of the integrated energy simulation about energy demand and energy supply is still the topic people 65 are pursuing, even though the progressive development has been made in the section of building energy performance. Some engineers suggest the methodology of coupled simulation and optimization, while others provide the lighting, mechanical and natural ventilation, air conditioning and on-site energy generation as the input for frame of the of building simulations (Bot et al. 2019). However, programming skills are always required for those methodologies. In addition, the approach with a software for dynamic simulation and another software for on-site renewable energy generation (Bot et al. 2019). It is difficult for engineers to have a quantized analysis for the relation between design strategies. The energy simulation software could provide relatively accurate analysis for the relation between design strategies which is complex, balance of energy demand and supply. With the quantized results of how much a design could affect building energy consumption, it is easier for architects and engineers to make decision for the design plans of buildings to have better energy performance. More generally, energy simulation can be used at many stages of the building process depending on the questions asked and how detailed the answers need to be. For example, an architect might want to study the energy consumption of a building based on different orientations of the building and later want a more detailed study on specific window characteristics. A lighting designer could use a balance of daylight and high efficiency lighting to achieve code standards by first running simulation software. A building engineer could study specific HVAC systems in providing energy in a building and their efficiency and sizing. Each of these individuals is using simulation as a decision support tool based on some aspect of building energy modeling. 66 2.3.1 Building Energy Modeling Building energy modeling (BEM) is widely utilized as it increases people’s understanding on the effect of different design strategies on building performance and balance between energy demand and supply (Bot et al. 2019). Energy of heating, cooling and mechanical ventilation consumes 30% of the total building energy (X. Li and Wen 2014). It is necessary to increase building energy efficiency and human comfort through the improvement of more efficient building design and operating strategies as most of the research presented that the buildings with equipment and operational problems often resulted in more energy consumption. For instance, 4% to 20% more energy was utilized in lighting and HVAC system because of the equipment and operation problem (X. Li and Wen 2014). How much a design strategy could affect the building performance depends on how well the BEM was developed and calibrated (Harish and Kumar 2016). Therefore, a BEM with high accuracy is essential to examine the precise impact of each control strategy. The BEM has been developed an interdisciplinary field of study that involves all the majors, such as mechanical engineering, architecture, civil engineering and electrical engineering (Harish and Kumar 2016). 2.3.2 Simulation-based Tool - IESVE Building energy simulation is identified as a methodology for predicting the building performance in multiple scenarios (Nasaruddin et al., 2018). For existing buildings, it is usually used to achieve a base model; in the design phase, it is for the precise of early prediction of the on several possibilities (Nasaruddin et al., 2018). The output of the simulation represents the building performance, for example, the thermal performance and level of optimization of design strategies. The software that conducts the building energy simulation is highly affecting the precise of the 67 simulation output. Numerous simulation-based software is adopted by most of the projects including: EnergyPlus, Ecotect, Green Building Studio, IES VE and so on. IES is utilized to simulate and analyze the comprehensive building energy performance. In addition, it enables the engineers to conduct simulation with multiple elements, such as air flow simulation, HVAC system, daylighting and so on (Nasaruddin et al., 2018). A case study conducted (through IES VE) to simulate the energy saving of an office with mixed- mode ventilation in Lebanon was validated with the actual operating performance of the building. Mixed mode ventilation is the approach that combined natural ventilation, mechanical ventilation, and cooling system to provide cooling load. The input with precise is critical to achieve the accurate prediction results from the simulation (Daaboul, Ghali, and Ghaddar 2018). For example, the computer heat dissipation was set as 60W/computer (computer heat gain 3.07W/m 2 ), which resulted in a difference of 18% corresponding energy consumption lower comparing to the actual case (Daaboul, Ghali, and Ghaddar 2018). With the computer heat dissipation was revised to 77W/computer (computer heat gain 4.8W/m 2 ), the difference between simulation result and actual performance was reduced to an average of 6% and maximum of 10.4% in May (Daaboul, Ghali, and Ghaddar 2018) (Figure 2-4 and Figure 2-5). 68 Figure 2-4 Actual and simulated HVAC electricity consumption (Daaboul, Ghali, and Ghaddar 2018) Figure 2-5 Actual and simulated case electricity consumption In the research conducted by Attia, many different energy simulation software, IES VE, HEED, eQUEST, ECOTECT, DesignBuilder, Green Building Studio, DOE-2, EnergyPlus, were 69 compared on two aspects: “usability and information management”, and “integration of intelligent design knowledge-base” (Attia et al., 2009). IES VE received highest percentage in the survey that participated with architects, designers, architecture educators and students, which is 85% (Attia et al., 2009). IES VE was utilized by Taleb in the research on testing the passive cooling strategies. A verity of design strategies could be tested through IES VE, such as the radiance of façade system (Figure 2- 6) (Taleb, 2014). The result of energy consumption can be showed in the graphs which is easier for designer to analyze. For example, the cooling load was conducted in Taleb’s research to examine the effect of design controls (Figure 2-7). Figure 2-6 Radiance analysis on south-east wall (Taleb, 2014) 70 Figure 2-7 Cooling load analysis (Taleb, 2014) 2.3.3 The Development of Codes for Energy Simulation and Energy Modeling Building energy codes are utilized to provide minimum or maximum requirements to control the design, construction, and operation of buildings for a certain level of energy performance. Energy simulation is getting more and more important in building design as the requirement of indoor environment quality and building performance. To reduce building energy consumption, many countries have updated their energy codes over the last few decades to encourage and sometimes require the use of less energy. In addition, energy simulation software is operated to conduct building energy analysis for energy codes. Thus, the requirements for energy efficiency design and determination of code compliance through performance-based approach could be achieved and adopted (Hui, 2003). 71 The formulation of building energy codes with the utilization of energy modeling software is a new tendency, as it is beneficial to analyzing the complexity of building energy performance (Hui, 2003). Nonetheless, the energy modeling software is complicated and needs engineers with modelling and analytical skills to implement the results with high accuracy. A lot building codes, regulations on the building energy efficiency were developed aiming to reduce global carbon emissions after 1980, such as the LEED rating system and Title 24 (the strictest building energy codes in U.S.) (Li & Shui, 2015). The building energy codes started to be issued in 1990s to regulate the building design, construction, and operation stage to control building energy performance (Li & Shui, 2015). In California, the Building Energy Efficiency Standards Title 24 is the energy code. ASHRAE 90.1 and the International Energy Conservation Code (IECC) are applied by most of the states. In China, China National Standard GB50189-2005 is the design standard for energy efficiency of buildings. 2.4 A Gap Between Energy Simulation and Actual Building Performance The gap between energy simulation and measured building performance should be considered when trying to create a NZEB. Dronkelaar explained “differences between energy performance quantification and classifies this energy performance gap as a difference between compliance and performance modeling with measured energy use” (Dronkelaar et al., 2016). More than 62 buildings in the previous research were studied in his paper and the deviation of +34% of the gap between prediction and actual performance (Dronkelaar et al., 2016). The uncertainty that caused the gap identified in energy simulation, occupant behavior, operation process was identified with impacts of 20-60%, 10-80, and 15-80 correspondingly (Figure 2-8) (Dronkelaar et al., 2016). 72 Figure 2-8 Potential risk on energy use from reported underlying causes (Dronkelaar et al., 2016) There are other factors that also have serious influence on the gap, such as the building performance goal and early design decisions, discrepancy in modeling. The action measures and feedback processes are considered as effective solutions for reducing the gap. In addition, the in- use policy, accuracy of building model and detailed energy data could be the strong support for decreasing the gap between gap and actual performance (Dronkelaar et al., 2016). A study was conducted to explore the difference between energy simulation results and actual building performance of around 90 LEED buildings (medium energy type). The ratio of measured/design EUI was utilized as a metric to analyze the relation of predicted and actual building performance. Its results showed a good amount of spread in the chart for Certified, Silver and Gold-Platinum LEED level (Figure 2-9). The results could be 92% if three levels of LEED Rating level projects were summed up to achieve the ratio (Figure 2-9) (Frankel & Turner, 2008). 73 Figure 2-9 Ratio of measured EUI/design EUI of LEED Buildings (Frankel & Turner, 2008) The comparison of simulation energy savings (horizontal axis) and operation savings (vertical axis) was also conducted in the study (Figure 2-10). The diagonal line represents the simulation energy saving equals operation energy savings. Buildings above the diagonal line saved more energy than simulation, and vice versus. 74 Figure 2-10 Percentage of measured and predicted energy savings (Frankel & Turner, 2008) However, compare to the medium energy type buildings, the energy modeling results of high energy types were less accurate (Figure 2-11). The average of those projects consumed almost 2.5 times amount of energy as the energy modeling results. 75 Figure 2-11 High and medium energy type buildings (Frankel & Turner, 2008) The gap between energy simulation results and actual operation results is the key problem that cannot be neglected. In the design phase, the procedures to assure the accuracy of energy modeling should be conducted to achieve the correct results. Then in the construction and operation stages, all the processes should be conducted as possible as the design to assure the buildings constructed and operated as the energy simulation. As the study conducted by Frankel and Turner, the actual energy savings larger or similar to the predicted energy savings for energy efficient buildings (medium energy type) could be achieved. However, for the high energy type buildings, predicted energy consumption were not always in the same range of actual building energy consumption (Frankel & Turner, 2008). 76 2.5 Summary This chapter discussed understanding the factors that affect the building energy performance, limitations, and challenges of the balance between energy consumption and renewable energy generation, energy simulation utilized as a decision support tool, and a gap between energy simulation and actual building performance. As NZEB becomes more common, widely accepted by more people, and adopted by many countries in the building energy legislation, a large amount of research and experiments were conducted to explore how to achieve the goal of zero energy consumption can be achieved. It is an ambitious goal as the it evolves three aspects of the buildings: energy demand reduction, increasing energy efficiency and on-site energy generation. With the exploration of all passive strategies, the energy consumption of building could be highly reduced; with development of new technologies, materials, the energy efficiency largely increased and abundant renewable energy is generated; with the advanced simulation software, the prediction of the building model performance is getting closer the actual building performance and an optimized integration of all the strategies is possible to be achieved. However, there is still challenges and limitations for the design strategies that applied to the buildings. The same design will not work for all the buildings. The design of the building should be conducted with different consideration correspondingly depending on the different climate and function of the building. In addition, some design controls would have negative effects on the others. Energy simulation, even if not perfect, is still a critical methodology to examine the building performances. 77 Chapter 3. Methodology The overall workflow for the energy simulation based sustainable design is introduced in this chapter (Figure 3-1 and Figure 3-2). The overall workflow includes the studied building modeling, application of design strategies, and feasibility studies. The main research process consists of creation of building model, assignment of weather conditions, design strategies analysis (Figure 3-3), integration of strategies, and performance analysis. The process of creation of base model, the application of passive design strategies on the base model, the application of active design strategies, and the base model with integration of passive and active design controls are listed and described in this chapter. Figure 3-1 Methodology Diagram 1 78 Figure 3-2 Methodology Diagram 2 79 Figure 3-3 Methodology Diagram for Design Strategies Application 80 3.1 Methodology Overview The reference building model was selected from the website of the U.S. Department of Energy. A low-rise office building in two different climate zones (Los Angeles and Harbin) was simulated through IES VE. Then energy simulation of the building was conducted to derive the annual results of energy consumption, such as cooling and heating load, lighting, appliance, and mechanical ventilation, with the hourly and minutely analysis. The energy consumption of lighting, appliance, mechanical ventilation, cooling and heating load, and EUI of the building were set as metrics to test the building performance. Another comparison model was created; it was the same as the original one but applied with different design strategies correspondingly. After that, the most energy efficient strategies were created by comparing the energy performance of each strategy. Then the integration of efficient strategies was applied to the original model with the adjustment to gain an optimal result. Several buildings using different methods were designed that show the feasibility of achieving a net zero energy building for two different climate zones. The progress of energy simulation was conducted through the modules of IES VE, such ModelIT, SunCast, Apache, ApacheHAVC, MacroFlo, VistaPro, RadianceIES (Table 3-1). 81 Table 3-1 Modules in IES VE Modules Name Description ModelIT Creation of the 3D models SunCast Performing shading and solar insolation studies Apache Providing facilities for preparation of input data (ApacheSim), calculations and simulations using (Apachesim, ApacheHVAC and MacroFlo) ApacheHVAC Support the detailed definition, configuration, control, and modeling of HVAC systems MacroFlo Analysis of infiltration and natural ventilation in buildings VistaPro A package to read ApacheSim dynamic thermal simulation files and real climate data files RadianceIES A research tool for predicting the distribution of visible radiation in illuminated spaces 3.1.1 Base Model The base model is a four-floor office building in Los Angeles, California, USA. The construction materials thermal properties (wall and roof insulation, window), internal gains (people, computer, and lighting), and air exchange (infiltration, natural ventilation, auxiliary ventilation) were selected to comply with Title 24 and ASHRAE 90.1. This base model was utilized to be the comparison for building performance with other models to see the effect of each strategy, integration of multiple strategies, and different weather conditions. 3.1.2 Base Model Applied with Passive Design Strategies The passive design strategies use environmental data such as wind flow, daylight, and dry-bulb temperature. The passive design strategies are building orientation, roof and wall insulation, window features (U-factor, WWR, SHGC, VT and shading factor), and natural ventilation. The application of each of them was applied with a range of parameters to check how passive design controls could affect the building performance. Then those strategies were listed with efficiency 82 from high to low, and the most efficient ones were selected for the integration of passive design strategies and the integration of both passive and active design strategies. 3.1.3 Base Model Applied with Active Design Strategies The active design strategies are using efficient technologies to increase the efficiency of equipment in the building, such as the HVAC system, a smart control system for lighting equipment and natural ventilation, and a geothermal heat exchange system. The method of examining the influence of those active design controls is similar to the process of testing passive design controls; each is applied to the base model one at a time with a range of parameters. Then those strategies were listed with efficiency from high to low and the most efficient ones were selected for the integration of active design strategies and the integration of both passive and active design strategies. 3.1.1 Photovoltaic Panels The amount of the energy generation of the base model was based on the area of the roof. The calculation of the roof should consider the maintenance space and the space occupied by other equipment on the roof. The calculation of PV panel electricity generation was conducted by the software PVsyst. An annual result provided by it after input the data of PV system inverter and PV module. A maximum calculation was made using the entire roof surface. Then later in the process to achieve a net zero energy building, a conscious effort was made to use as few PVs as reasonably possible. 83 3.1.2 Base Model with Integration of Passive and Active Design Strategies As one design control could have negative and active impact on the others, the trade-off of the design controls was considered for the process of exploring the effect of integration of passive design strategies, active design strategies, and the integration of passive and active design strategies. For example, the large window could reduce the utilization of lighting by providing more natural daylighting, while the energy consumption of HVAC system increased as the result of solar heat gain. The function of the IES Parametric Batch Processor could analyze the integration of all the applied design strategies and provide the optimized results. The Parametric Batch Processor is a tool that enables engineers to create, and batch run a series of simulations instead of manually changing the parameters, such as construction materials, weather files, and other parameters. To achieve a batch queue, it allows a single parameter to be revised before running the simulation. 3.2 Base Model The base model was constructed first in IES VE. Weather data and parameters were set, and the annual energy consumption was calculated (Figure 3-4). 84 Figure 3-4 Methodology Diagram for Creating the Base Model The building selected is a four-floor office building with total area of 100,000 ft 2 and volume of 1,300,000 ft 3 (Figure 3-5). It was simulated in two different climate zones, Los Angeles and Hardin. The building information, such as wall, roof, windows construction materials, occupancy profile, cooling, and heating setpoints and so on, which are the parameters that could be modified. 85 Figure 3-5 Base Model in IES VE The floor plan is a rectangle divided to five zones (Figure 3-6). A zone is also considered as a thermal zone, which represents a space that has the similar space conditioning requirements, the same heating and cooling setpoint. The upper longitudinal side of base model is facing the north direction. The default name of each zone based on its location. Figure 3-6 1 st Floor plan of the base model 86 For example, the five zones of the first floor named as 1st Floor North, 1st Floor West, 1st Floor South, 1st Floor East and 1st Floor Interior. The zones of second and third floor named under the same rule (Figure 3-7). The specific information of all the spaces such as, area, volume and glazing area of each zone could be concluded from IES VE (Table 3-2). Figure 3-7 Name of all the zones (spaces) 87 Table 3-2 Spaces information Area (ft 2 ) Volume (ft 3 ) Exterior wall area (ft 2 ) Exterior window area (ft 2 ) Internal wall area (ft 2 ) 1st, 2nd, 3rd Floor North 5,775 75,075 1,040 1,560 0 1st, 2nd, 3rd Floor West 3,150 40,950 650 975 0 1st, 2nd, 3rd Floor South 5,775 75,075 1,040 1,560 0 1st, 2nd, 3rd Floor East 3,150 40,950 650 975 0 1st, 2nd, 3rd Floor Interior 7,150 92,850 0 0 0 3.2.1 Location and Weather Data File The location of the base model was set as Los Angeles and Harbin. IES VE provides a more specific location for the weather file as Los Angeles International Airport (Figure 3-8 and Figure 3-9). The summer design conditions took as 0.4% dry bulb temperature and coincident wet bulb temperature as normal, and the winter weather condition taken to be the 99.6% dry bulb temperature (Figure 3-9). The simulation weather data files usually contain dry bulb and wet bulb temperature, wind speed and direction, solar altitude and azimuth, cloud cover, and other parameters for each hour and every 30 minutes of the year (Figure 3-8 and Figure 3-9). 88 Figure 3-8 Location set up Figure 3-9 Weather data file 89 3.2.2 Set up Parameters in Tabular BTM Tabular BTM (building template manager) is a window provide interaction with building templates of model. Most of the parameters of the model could be set through Tabular BTM, such as the profile data, construction data, internal heat gains, air exchange, and so on (Figure 3-10). Figure 3-10 Building Template Manager Space Conditions 3.2.2.1 Occupancy Data Profile Setting accurate data profile is necessary to simulate the occupancy and occupant-related energy performance of the model. The occupant behavior is one of the most relevant factors that affecting difference between the simulation and reality. A weekly profile sets the model similar to the reality (Figure 3-11). For the base building, it was assumed that the building was occupied from 8am to 90 6pm during the weekdays; this is also considered in the profile. Therefore, some energy consumption was not running in the simulation out of the assumed time duration, which matches the real office occupancy condition. Figure 3-11 Weekly Profile 3.2.2.2 Construction Materials Thermal Properties The construction materials, external walls, internal walls, roof, windows, ground floor, thermal properties were set in the Construction Database. For example, the R-values of the wall were changed by modifying the properties of each material that consists of the wall, like thickness, conductivity, and resistance (Figure 3-12). 91 Figure 3-12 Wall construction database The modified U-value, total R-value, thickness, thermal mass, and mass cannot be modified by just changing the numbers in the column. They can only be modified by changing the materials, material thickness, and properties of the materials. The outside and inside emissivity, solar absorptance, and resistance were chosen as they were the default values from IES VE. The type of material and the number of material layers, such as insulation, cavity, particle board and plasterboard, could be modified. The values of conductivity, density, and resistance came with the material chosen from IES VE. The important features of the window performance, such as U-value, SHGC, and visible transmittance can be modified in Project Construction (Glazed: External Window) dialogue box (Figure 3-13). 92 Figure 3-13 Window construction database The net U-value (including frame), U-value (glass only), total shading coefficient, SHGC (center pane), net R-value and g-value cannot be modified by just changing the numbers in the column. They can only be modified by changing the materials, material thickness, and properties of the materials of the construction layers. The emissivity of a material surface refers to its effectiveness in emitting energy as thermal radiation. The resistance of the material is the ability to resist the heat transfer through the material. The base model was built compliant to the building energy codes Title 24 and ASHRAE 90.1 as mentioned previously. Thus, the requirements of the codes were the minimum and maximum parameters of the base model. For example, the maximum requirement U-factor of external wall insulation is 0.069 Btu/(h ⋅ft2 ⋅°F) (Title 24, Table 3-8). And the U-factor of a default external wall was 0.046 Btu/(h ⋅ft2 ⋅°F), it was used as the base model external wall. This was the same technique applied to the roof; the requirement is 0.041 Btu/(h ⋅ft2 ⋅°F) (Title 24, Table 3-6). Then the default roof with insulation U 0.032 was adopted to the base model. 93 3.2.2.3 Internal Heat Gains Internal heat gains are created by the occupant activity of metabolic heat, thermal emission of artificial light, the utilization of electrical appliances, and other items that increase the indoor air temperature. The internal heat gains of people, computers, and fluorescent lighting were considered in the base model (Figure 3-14). Figure 3-14 Internal heat gains The details of each feature could be modified in the interface based on the building energy code (Figure 3-15). In addition, those internal heat gains do not happen when people are not working there, so the variation profile set up as “8-6 workday” as mentioned previously. Maximum Sensible Gain represents the unit sensible loads (W/ft 2 ) that caused by fluorescent lighting, computers; Maximum Power Consumption is the unit energy (W/ft 2 ) that lighting and computers that consumed. The Diversity Factor is a multiple (range from 0 to 1) applied to the gain during the process of simulation (default value is 1). Radiant Fraction represents the proportion of sensible gain that is released as radiant heat, the default value for fluorescent lighting and computers are 0.45 and 0.22 in IES VE. Maximum Latent Gain is the unit latent gain caused by people. The 94 default values of Occupancy Density, Maximum Sensible Gain, and Maximum Latent Gain from IES VE were used. Figure 3-15 Fluorescent lighting, computers and people heat gain setup details 3.2.2.4 Air Exchange Air exchange refers to the air flow in the space. The source of the air could be outdoor space, adjacent space, or the supply air at a specific temperature. Infiltration refers to the outside air unintentionally gets in the building through cracks or the usage of door which cannot be avoided (Figure 3-16). Figure 3-16 Air Exchange 95 Different air flow rates could be set up based on the requirement of building energy codes. 0.2500 ach is the default value of IES VE (Figure 3-17). As the infiltration is happening all the time, the variation profile should be on continuously. Figure 3-17 Air exchange details setup 3.2.3 Assign Thermal Template and Construction Materials to Base Model After assigning the thermal template and construction, the model was run first in SunCast first and then dynamic simulation with ApacheSim to get heating and cooling load, mechanical ventilation and EUI (Figure 3-18). 96 Figure 3-18 Apache The operation profile of heating and cooling were set up through the interface of Space Conditions. The profile was set as 8-6 weekday (no lunch) (Figure 3-19). Setpoint of heating and cooling was 68 F and 74 F in constant condition in the duration of profile (Figure 3-19). Humidity control was in the range of 0 to 50%, which represents the control of humidity of the indoor environment. As the energy consumption for domestic hot water (DHW) was calculated by hand calculation in 3.2.3.3, the input for DHW consumption was 0. In addition, the internal gains (fluorescent lighting, people, and computers) and the air exchanges (infiltration, natural ventilation, and auxiliary ventilation) were assigned to the base model (Figure 3-19). 97 Figure 3-19 Space Condition and Internal Gains in IES VE 3.2.4 Calculation of Building Energy Consumption The building energy consumption of cooling, heating, mechanical ventilation, lighting and EUI were the metrics utilized to evaluate the building model performance. For the basic model, the lighting, DHW, office equipment and pump were assumed with constant energy consumption, while the cooling, heating, and fan power were calculated based on cooling and heating load from the IES VE. Each section of those energy consumption was in form of kBtu/ft 2 that consists of total EUI to test the effect on each design strategy. 98 3.2.4.1 Lighting Energy Consumption According to the requirement of 2019 Building Energy Efficiency Standards, the allowed lighting power density of office building is 0.65w/ft 2 (Title 24, Table 140.6-B). The total lighting energy consumption for the model: 0.65w/ft 2 x 3.41Btu/h x10h x 261 x 53660 / 1000 = 310,426.59kBtu. The lighting use density: 0.65w/ft 2 x 3.41Btu/h x10h x 261day / 1000 = 5.79kBtu/ft 2 3.2.4.2 Computer Energy Consumption The computer power density was set as 0.9w/ft 2 (U.S. Department of Energy (DOE), Energy Efficiency & Renewable Energy). The total computer energy consumption was set to these values: 0.9w/ft 2 x 3.41Btu/h x10h x 261 x 53660 / 1000 = 429,821.43kBtu. The lighting use density: 0.65w/ft 2 x 3.41Btu/h x10h x 261day / 1000 = 5.77kBtu/ft 2 3.2.4.3 Domestic Hot Water Energy Consumption The usage of domestic hot water was set as 1.1gallon/person/day (DOE, Energy Efficiency & Renewable Energy). For the open space workstation, there is 60-110ft 2 per employee. The total mass of water: 8.34 x 1.1gall/person/day x 53660ft 2 /80 person = 6153.46lb; the total energy consumption for DHW: 6153.46lb x 1.0Btu/lb F x 55F x 261day /1000 = 84,610.08kBtu The DHW use density: 84,610.08KBtu / 53660ft 2 = 1.58kBtu/ft 2 3.2.4.4 Cooling and Heating Load Two factors that affecting the building energy performance, cooling load and heating load, were derived in this phase to test the building performance. Internal heat gains, solar heat gains, external 99 conduction gains and infiltration gains were considered as main heat exchanges resulting in the up and down of cooling and heating load (Figure 3-20). Figure 3-20 Gains Derived from IES VE A function in IES VE provides annual heat condition of each room (space) by hourly dynamic simulation. Positive numbers mean that space needs represents a certain amount of cooling load; correspondingly, negative numbers show the needed amount of heating. The columns and rows were the hourly loads for the spaces of the building (Figure 3-21). Each column represented a room (total 15 spaces). Each row represented the load at an hour with the certain time of the year. 100 Figure 3-21 Heating and cooling load of each room in each hour during the whole year 3.2.4.5 Fan Power The fan power is the electric power that is needed to drive a fan for indoor environment mechanical ventilation. It includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.85 cfm/watt Q = air volume flow (cfm) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) 101 The calculation of fan power was conducted with Excel. Fan power is consisting of the power for bringing in the air for heating, cooling and minimum ventilation. The HVAC system is turned on when indoor air temperature was not in the range from 68 F to 74F. The energy was used to provide the air to heat and cooling the indoor space. The heating and cooling sensible load were achieved by the previous step (3.2.3.4), which were the heating and cooling demand of the base model. They consisted of the hourly load from spreadsheet (Figure 3-21). In addition, the temperature difference was the corresponding hourly difference for heating and cooling load. In the condition that the indoor air temperature was in the range 68F – 74F, the fan was set to maintain the minimum ventilation rate which is 0.15cfm/ft 2 (Title 24, Table 4-12). The ventilation load for the minimum requirement is Total area 100,000ft 2 x 0.15 = 15,000cfm. 3.2.4.6 EUI of the Building Model The energy use intensity (EUI) of the building model was used to evaluate the building energy performance. Its units are kBtu/ft 2 . The values for different types of buildings (area from 50,000ft 2 - 150,000ft 2 ) in California are: office building (1,838 buildings) 57kBtu/ft 2 , hotel (427 buildings) 60kBtu/ft 2 , college/university (83 buildings) 74kBtu/ft 2 (California Building Energy Benchmarking Program, 2020). The EUI of the model is shown in a stacked bar chart (Figure 3-22). It consists of base model energy end use, such as cooling load, heating load, lighting, equipment, DHW, fan power and Pump. In addition, it contains the EUI for the base model and the models with different design strategies. 102 Figure 3-22 EUI Stacked Bar Chart of Wall Insulation The difference of energy use for each section could be detected from the EUI stacked bat chart when the design controls are applied to the base model. It was beneficial to figuring out how design controls affect the building energy consumption. 3.3 Base Model with Passive Design Strategies The passive design strategies including roof insulation, wall insulation, window, natural ventilation, and orientation were applied to the base model as the methodology in IES VE (Figure 3-23). 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 12 11.9 12.1 0.8 0.7 0.8 8.4 8.3 8.4 0 5 10 15 20 25 30 35 40 45 Base Model Wall Insulation U=0.016 Btu/h ft2 F Wall Insulation U=0.12 Btu/h ft2 F EUI Comparison (kBtu/SF) Fan Power Heating Cooling Pump DHW Equipment Lighting -0.7% 0.2% 103 Figure 3-23 Methodology Diagram for Passive Design Strategies Application The application of only passive design strategies without an HVAC system in IES VE provides the results with higher accuracy to test the passive design controls, such as the roof and wall insulations, window factors (U-value, visible transmittance, SHGC, overhang Factor), and natural ventilation. It is not a normal method to conduct the energy simulation without HVAC system. However, the load derived from VistaPro without the application of ApacheHVAC could avoid the effect form it and provide more accurate results on cooling, heating and ventilation load. In addition, the model location and weather data were set to Los Angeles, USA and Harbin, China. 3.3.1 Roof Insulation The U-value of base model roof insulation was set as 0.049 Btu/h ft 2 F, which is similar to the minimum requirement of Title 24, Table 3-6. Roof insulation values were set at U=0.079 Btu/h ft 2 104 F, U=0.049 Btu/h ft 2 F, and U=0.019 Btu/h ft 2 F to the base model to conduct the sensitivity analysis. The U-value could be modified by changing the thickness of roof construction insulation. The same method was used to derive the EUI and specific number of cooling, heating and ventilation that consist of the EUI. The graphs included indoor air temperature, dry-bulb temperature, and external conduction in 3 rd Floor Interior on 2 nd December with U=0.079 Btu/h ft 2 F, U=0.049 Btu/h ft 2 F and U=0.019 Btu/h ft 2 F could be utilized to analyze the heat exchange between indoor and outdoor environment which is the key effect result from insulation (Figure 3- 24, 3-25, 3-26). Figure 3-24 Roof U=0.049 Btu/h ft 2 F 3 rd floor interior space 02/December External conduction 105 Figure 3-25 Roof U=0.079 Btu/h ft 2 F 3 rd floor interior space 02/December External conduction 106 Figure 3-26 Roof U=0.019 Btu/h ft 2 F 3 rd floor interior space 02/December External conduction These three graphs were utilized to analyze the relation of roof U-value and space external conduction. In those three graphs, the indoor air temperature and dry-bulb temperature were the same as they represented the results of the same day (December 2 nd ) while the external conduction gains were different because of the varied roof insulation. Indoor air temperature started to rise at 8:00 am when the HVAC system started working (set in the profile) until it got the setpoint (74 F) and kept it constant until people left work at 6:00pm. Dry-bulb temperature was the outdoor temperature of December 2 nd of a typical year, low at morning and night, went high during the daytime and got the peak point around 2:00 pm. The difference of external conduction gains of U=0.079 Btu/h ft 2 F, U=0.049 Btu/h ft 2 F and U=0.019 Btu/h ft 2 F showed the effect of roof 107 insulation on building energy consumption. The shapes of them were similar, but for the graph with lower U-value, the peak point and the whole value through the day were lower which represented the less external conduction. 3.3.2 Exterior Wall Insulation The U-value of exterior wall insulation was set as U=0.046 Btu/h ft 2 F (the requirement of Title 24 is 0.069 Btu/h ft 2 F). The U-value of the case study was set as U=0.016 Btu/h ft 2 F, U=0.069 Btu/h ft 2 F, U=0.12 Btu/h ft 2 F to get the corresponding result of cooling, heating, fan power energy use intensity and EUI. The method is similar to the change of roof insulation. The graphs included indoor air temperature, dry-bulb temperature, and external conduction in 1 st Floor North on 9 th December with wall U=0.12 Btu/h ft 2 F and U=0.016 Btu/h ft 2 F were utilized to analyze the heat exchange between indoor and outdoor environment which is the key effect result from insulation (Figure 3-27 and 3-28). 108 Figure 3-27 Wall U=0.12 Btu/h ft 2 F 1st floor North space 09/December External conduction, indoor and outdoor temperature 109 Figure 3-28 Wall U=0.016 Btu/h ft 2 F 1st Floor North Space 09/December External conduction, Indoor and Outdoor Temperature Those two graphs were utilized to analyze the relation of wall U-value and space external conduction. In the two graphs, the indoor air temperature and dry-bulb temperature were the same as they represented the results of the same day (December 9 th ) while the external conduction gains were different because of the varied wall insulation. Indoor air temperature started to rise at 8:00 am when the HVAC system started working (set in the profile) until it got the setpoint (74 F) and kept it constant until people left work at 6:00pm. Dry-bulb temperature was the outdoor temperature of December 9 th of a typical year, low at morning and night, went high during the daytime and got the peak point around 2:00 pm. The difference of external conduction gains of 110 U=0.12 Btu/h ft 2 F, U=0.069 Btu/h ft 2 F and U=0.016 Btu/h ft 2 F showed the effect of wall insulation on building energy consumption. The shapes of them were similar, but for the graph with lower U-value, the peak point and the whole value through the day were lower which represented the less external conduction. 3.3.3 Window There are some factors of the window that required by California Building Energy Code and ASHRAE 90.1 to increase the building energy performance. Each factor has different impact on the different section of EUI. The factors included window U-factor, WWR, SHGC, VT and overhang factor. 3.3.3.1 Window U-factor The U-value of window (including frame) was set as 0.46 Btu/(h ⋅ft2 ⋅°F) which is maximum allowable requirement of Title 24, Table 3-16. The Net U-value of window changed from high to low, U=0.38 Btu/h ft 2 F, U=0.3 Btu/h ft 2 F, to test the sensitivity of building performance. The net U-value of window could be reduced in IES VE by modifying the resistance of window frame and surfaces. Then the same method was used to derive the EUI. 3.3.3.2 Window Wall Ratio (WWR) The WWR of the base model was 40%, which is maximum requirement of Title 24, Table 3-16. However, some buildings are adopting WWR over 40%. In some cases, this is from the client wishing for more windows for views or the architect wanting to improve daylighting in the building. The WWR of the model was changed in the ModelIT > Edit glazing, doors, and louvers 111 (Figure 3-29). The WWR were changed to 60%, 40% (base model), and 20%. Calculations were done to derive the EUI. Figure 3-29 Interface to Change WWR 3.3.3.3 Window SHGC The U-value of window (include frame) was set as 0.22 Btu/(h ⋅ft2 ⋅°F) which is maximum requirement of Title 24, Table 3-1.6 The SHGC of window changed to 0.18, and 0.12, to test the sensitivity of building performance. The SHGC of window could be reduced in IES VE by reducing the transmittance of window outer pane and inner pane. Calculations were done to derive the EUI. The information of glazing panes was included in the interface of glazing material (pane) (Figure 3-30). 112 ` Figure 3-30 Interface to Change Transmittance of Outer Pane The emissivity of a material surface refers to its effectiveness in emitting energy as thermal radiation. The resistance of the material is the ability to resist the heat transfer through the material. Conductivity is the rate at which heat passes through a material. Transmittance is the ratio of the light energy falling on a surface to the light energy transmitted through it. The reflectance is a measure of the proportion of light striking a surface which is reflected off it. However, only the transmittance of this interface was modified to change the SHGC of window. 3.3.3.4 Window VT The VT of the base model was 0.32 which is minimum requirement of Title 24 Table 3-16. The VT of the model could be changed by editing the window features (Figure 3-31). 113 Figure 3-31 Interface to Change Visible Transmittance Then the values of VT on the base model were changed to 0.43, and 0.55. The simulations were run to derive the EUI. 3.3.4 Orientation The model floor plan is symmetrical, so the orientation was set to rotate 45 degrees, 90 degrees, and 135 degrees to examine the sensitivity of building performance based on orientation. The Site Rotation function was used to rotate the building with different angles (Figure 3-32). Then the simulations were run to derive the EUI. Figure 3-32 Site Rotation 114 3.3.5 The Integration of Passive Design Strategies The integration of passive design strategies is a progress of picking the efficient passive design in the previous steps. First, the results from all the passive strategies were applied to the base model and listed based on the EUI efficiency from high to low. Strategies that did not have an effect or only a slight effect were removed. Then the rest of strategies were analyzed to determine which had a positive or negative impact on others. For the ones which have active impact, a simple way of applying them to the base model could result in the better performance. However, for the ones that have negative effect on others, they were applied to the model together to explore the principle of how they affect each other. Then an optimized integration of them was derived including the ones with active impact to constitute the final integration of passive design strategies. 3.4 Base Model with Active Design Strategies The active design strategies (choice of HVAC system, type of smart control system, and specific lighting equipment) were applied to the base model in IES VE (Figure 3-33). 115 Figure 3-33 Methodology Diagram for Active Design Strategies Application Active design strategies that adopted in the design, construction, and operation stage are usually considered as the systems that with high efficiency. Like the HVAC system and smart control system (ventilation and lighting) applied to the base model to reduce the building energy consumption. In addition, the model location and weather data were set as Los Angeles and Harbin. 3.4.1 HVAC System 15 spaces were divided into 4 HVAC zones with 4 air handling units (AHU), AHU1, AHU2, AHU3, and AHU4 (Figure 3-34). 116 Figure 3-34 HVAC ZONES with AHU Airside system selected was VAV Reheat Chlr,HW (Figure 3-35). 117 Figure 3-35 Airside System Selection Cool water and hot water loops equipment were selected as well (Figure 3-36). Figure 3-36 Waterside and Plant Equipment Selection 118 The base model was divided into four HVAC zones with four Air Handling Units (AHU). The HVAC components of each AHU were conducted in IES VE (Figure 3-37 – Figure 3-40). Figure 3-37 AHU1 HAVC Components 119 Figure 3-38 AHU2 HAVC Components Figure 3-39 AHU3 HAVC Components 120 Figure 3-40 AHU4 HAVC Components The other HVAC system with different efficiency from low to high were added to the base model to run dynamic energy simulation in Apache and check the results of energy consumption of model in VistaPro. The efficiency of the HVAC system ranging from 9 EER to 14.0 EER. 3.5 Photovoltaic Panels The methodology for examination of how much on-stie energy was generated with the software program PVsyst (Figure 3-41). 121 Figure 3-41 Methodology Diagram of PV Panels The efficiency of PV panels and the area of PV panels were calculated. PV panels were designed to be installed in the roof area to provide on-site renewable energy generation. The calculation of effective area of PV panels on the roof (Figure 3-42). Roof area: 200ft x 125ft = 25,000ft 2 Effective roof area: 25,000 ft 2 x 90% = 22,500ft 2 (2,090 m 2 ) 122 Figure 3-42 Model Floor Plan Dimension 3.5.1 PV Panels in Los Angeles PVWatts was used to calculate the total amount of energy generation of PV panels. PV panels were set with 20 tilts have the best performance of energy generation (Figure 3-43). 123 Figure 3-43 PVWatts System Information The results of PVWatts shown that the annual energy generation of PV panels with the utilization of 90% of roof area is 513,265 kWh, which is 1,751,773 kBtu/year (Figure 3-44). 124 Figure 3-44 PV Panels Energy Generation The half of the roof area (25,000 ft 2 /2 = 12,500 ft 2 = 1,161 m 2 ) covered with PV panels could provide renewable energy generation: 292,880 kWh/year = 999,540 kBtu/year (Figure 3-45). 125 Figure 3-45 PV Half of the Roof Area Panels Energy Generation Report The roof with overhangs made of PV panels (length of overhang 6 ft) area: 22,500ft 2 + 4,044ft 2 = 26,544 ft 2 (2,466 m 2 ). The energy generation 618,154kWh/year = 2,109,635 kBtu/year. Use base model energy consumption to be the comparison: 41.4 kBtu/sf/year x 100,000 = 4,140,000 kBtu/year. The annual energy generation of PV panels with different area of covered roof area was listed to compare with base model annual energy consumption (Table 3-3). 126 Table 3-3 PV Los Angeles Panels Energy Generation and Base Model Energy Consumption PV Panels Area Energy Generation Base Model Energy Consumption Annual Peak Load 523,992kWh 4,140,000 kBtu 90% Roof Area 22,500ft 2 1,751,773 kBtu 50% Roof Area 12,500 ft 2 999,540 kBtu Roof with overhang 26,544 ft 2 2,109,635 kBtu 3.5.2 PV Panels in Harbin Roof area: 200ft x 125ft = 25,000ft 2 Effective roof area: 25,000 ft 2 x 90% = 22,500ft 2 (2,090 m 2 ) PVWatts was used to calculate the total amount of energy generation of PV panels. PV panels were set with 20 tilts have the best performance of energy generation (Figure 3-49). 127 Figure 3-49 PVWatts System Information The results of PVWatts shown that the annual energy generation of PV panels with the utilization of 90% of roof area is 438,281 kWh, which is 1,495,765 kBtu/year (Figure 3-50). 128 Figure 3-50 PV Panels Energy Generation The half of the roof area (25,000 ft 2 /2 = 12,500 ft 2 = 1,161 m 2 ) covered with PV panels could provide renewable energy generation: 246,315 kWh/year = 840,623 kBtu/year (Figure 3-51). 129 Figure 3-51 PV Half of the Roof Area Panels Energy Generation Report The roof with overhangs made of PV panels (length of overhang 6 ft) area: 22,500ft 2 + 4,044ft 2 = 26,544 ft 2 (2,466 m 2 ). The energy generation 520,432kWh/year = 1,776,130 kBtu/year. Use base model energy consumption to be the comparison: 45.4 kBtu/sf/year x 100,000 = 4,540,000 kBtu/year The annual energy generation of PV panels with different area of covered roof area was listed to compare with base model annual energy consumption (Table 3-4). 130 Table 3-4 PV Harbin Panels Energy Generation and Base Model Energy Consumption PV Panels Area Energy Generation Base Model Energy Consumption Annual Peak Load 448,765 kWh 4,540,000 kBtu 90% Roof Area 22,500ft 2 1,495,765 kBtu 50% Roof Area 12,500 ft 2 840,623 kBtu Roof with overhang 26,544 ft 2 1,776,130 kBtu 3.6 Base Model with Integration of Passive and Active Design Strategies The methodology for testing the integration of passive and active design strategies with two different locations (Los Angeles and Harbin) in IES VE was showed in Figure 3-45. The end energy use, heating, cooling, lighting, mechanical ventilation and EUI were utilized to do the comparison (Figure 3-46). 131 Figure 3-46 Methodology Diagram for Integration of Passive and Active Design Strategies While the application of only passive or active design controls could increase the building performance, the integration of passive and active design controls could provide even more energy savings. The integration of passive design strategies with the best performance was applied to the base model in Apache, RadianceIES, and MacroFlo, then applied the integration of active design strategies with better performance to the base model in ApacheHVAC. Instead of using the dynamic energy simulation in Apache and achieving results from VistaPro, the function of Parametric Batch Processor was used for the analysis of the optimized integration of passive and active design controls. It can be found in the Navigators in IES VE which showed the introduction of how to use it (Figure 3-47). In addition, the process with the sequence of the utilization of Parametric Batch Processor is included. 132 Figure 3-47 Parametric Batch Processor A computer-based parametric calculation could result in more accurate results. Many parameters with specific ranges of each strategy were set through this function, and an optimal integration was achieved by the calculation. Each design strategy could have effect on not only one building energy end use, which is the same, each energy end use could be affected by more than one design strategy. The relation between 133 design strategies and energy end use was beneficial to the analysis of building energy efficiency (Figure 3-48). Energy uses Design variables Space heating Wall insulation (U=0.016 Btu/h ft 2 F, U=0.046 Btu/h ft 2 F, U=0.12 Btu/h ft 2 F) Roof insulation (U=0.019 Btu/h ft 2 F, U=0.049 Btu/h ft 2 F, U=0.079 Btu/h ft 2 F) Window U-factor (U=0.46 Btu/h ft 2 F, U=0.38 Btu/h ft 2 F, U=0.3 Btu/h ft 2 F) Space Cooling WWR (60%, 40%, 20%) Glazing SHGC (0.22, 0.18, 0.12) Glazing VT (0.55, 0.43, 0.32) Fan power Overhang factor (1, 0.69,0.44) Orientation (0°, 45°, 90°, 135°) Radiance Lighting Natural ventilation HVAC system Figure 3-48 Relation of Building Energy End Use and Design Strategies 134 The whole process can be summarized as creating model geometry, setting up input, assigning the input to geometry, running the dynamic simulation, derive results from IES VistaPro to spreadsheet and getting results. Each run of simulation only takes few seconds as the model is small. The results in the spreadsheet shown cooling and heating load (Figure 3-49). Hourly load of the whole model is calculated throughout the year. Positive numbers represent heating load and negative numbers represent cooling load. Figure 3-49 Cooling and Heating Load in Spreadsheet 135 3.7 NZEBs with Different Integration Architects often like to have alternatives to choose from. Eight alternatives were created, for Los Angeles and four for Harbin that satisfied the requirements of a net zero energy building. The intent also to also minimize the number of PVs on the roof. Four integrations of design strategies were created with corresponding area of PV panels to do the comparison in IES VE (Figure 3-50). Figure 3-50 Methodology Diagram for Different Integration of Design Strategies According to the analysis of base model applied with different integration of design strategies, only 4 integrations could result in the balance between energy consumption and on-site energy generation. As the energy consumption of each integration, the corresponding area of PV panels is needed to generate electricity. 136 3.7.1 Base Model The integration of base model is the consisted of seven passive design strategies mentioned in this chapter, WWR, window U-factor, wall insulation, roof insulation, glazing SHGC, orientation, glazing VT; and active design strategy, HVAC system (Table 3-5 & 3-6). Table 3-5 Integration of Los Angeles Base Model Los Angeles Base Case 1 HVAC System (VAV-Reheat) 2 WWR (40%) 3 Window SHGC (0.22) 4 Window (U=0.46 Btu/h ft 2 F) 5 Wall Insulation (U=0.069 Btu/h ft 2 F) 6 Roof Insulation (U=0.049 Btu/h ft 2 F) 7 Orientation (Long side facing north) 8 Glazing VT (0.55) EUI 41.4 kBtu/ft 2 PV Panels to Achieve Net Zero 210% Roof Area 137 Table 3-6 Integration of Harbin Base Model Harbin Base Case 1 HVAC System (VAV-Reheat) 2 WWR (40%) 3 Window SHGC (0.22) 4 Window (U=0.46 Btu/h ft 2 F) 5 Wall Insulation (U=0.069 Btu/h ft 2 F) 6 Roof Insulation (U=0.049 Btu/h ft 2 F) 7 Orientation (Long side facing north) 8 Glazing VT (0.55) EUI 45.4 kBtu/ft 2 PV Panels to Achieve Net Zero 270% Roof Area All the passive design strategies with compliance to Title 24 were applied to the base model, such as WWR 40%, window SHGC 0.22, window U=0.46 Btu/h ft 2 F, wall insulation (U=0.069 Btu/h ft 2 F), roof insulation (U=0.049 Btu/h ft 2 F), orientation (long side facing north) and glazing VT 0.55 (Table 3-5 & 3-6). The active design strategy, HVAC system was chosen as VAV-Reheat system. Then run the ApacheSim to get the energy simulation with EUI. 3.7.2 Integration 1 3.7.2.1 Integration 1 in Los Angeles The integration 1 is the consisted of seven passive design strategies mentioned in this chapter, WWR, window U-factor, wall insulation, roof insulation, glazing SHGC, orientation, glazing VT; and active design strategy, HVAC system (Table 3-7). 138 Table 3-7 Integration 1 in Los Angeles Los Angeles Integration 1 1 VAV-Reheat system 2 WWR (20%) 3 Window SHGC (0.12) 4 Window (U=0.30 Btu/h ft 2 F) 5 Wall Insulation (U=0.016 Btu/h ft 2 F) 6 Roof Insulation (U=0.019 Btu/h ft 2 F) 7 Orientation (Long side facing Southwest) 8 Glazing VT (0.32) EUI 38.6 kBtu/ft 2 PV Panels to Achieve Net Zero 200% Roof Area All the passive design strategies with compliance to Title 24 were applied to the base model, such as WWR 20%, window SHGC 0.12, window U=0.30 Btu/h ft 2 F, wall insulation (U=0.016 Btu/h ft 2 F), roof insulation (U=0.019 Btu/h ft 2 F), orientation (long side facing southwest) and glazing VT 0.32 (Table 3-7). The active design strategy, HVAC system was chosen as heat recovery system. The integration 1 could be considered as the integration of all strategies mentioned previously in chapter 3 that with the most energy efficient values. Then run the ApacheSim to get the energy simulation with EUI. 139 3.7.2.2 Integration 1 in Harbin The integration 1 is the consisted of seven passive design strategies mentioned in this chapter, WWR, window U-factor, wall insulation, roof insulation, glazing SHGC, orientation, glazing VT; and active design strategy, HVAC system (Table 3-8). Table 3-8 Integration 1 in Harbin Harbin Integration 1 1 VAV-Reheat system 2 WWR (20%) 3 Window SHGC (0.12) 4 Window (U=0.30 Btu/h ft 2 F) 5 Wall Insulation (U=0.016 Btu/h ft 2 F) 6 Roof Insulation (U=0.019 Btu/h ft 2 F) 7 Orientation (Long side facing Southwest) 8 Glazing VT (0.32) EUI 41.2 kBtu/ft 2 PV Panels to Achieve Net Zero 250% Roof Area All the passive design strategies with compliance to Title 24 were applied to the base model, such as WWR 20%, window SHGC 0.12, window U=0.30 Btu/h ft 2 F, wall insulation (U=0.016 Btu/h ft 2 F), roof insulation (U=0.019 Btu/h ft 2 F), orientation (long side facing southwest) and glazing VT 0.32 (Table 3-8). The active design strategy, HVAC system was chosen as heat recovery system. The integration 1 could be considered as the integration of all strategies mentioned previously in chapter 3 that with the most energy efficient values. Then run the ApacheSim to get the energy simulation with EUI. 140 3.7.3 Integration 2 3.7.3.1 Integration 2 in Los Angeles The integration 2 is the consisted of seven passive design strategies mentioned in this chapter, WWR, window U-factor, wall insulation, roof insulation, glazing SHGC, orientation, glazing VT with the values compliance to Title 24; and active design strategy, HVAC system (Table 3-9). Table 3-9 Integration 2 in Los Angeles Los Angeles Integration 2 1 VAV-Reheat system with heat recovery 2 WWR (20%) 3 Window SHGC (0.12) 4 Window (U=0.30 Btu/h ft 2 F) 5 Wall Insulation (U=0.016 Btu/h ft 2 F) 6 Roof Insulation (U=0.019 Btu/h ft 2 F) 7 Orientation (Long side facing Southwest) 8 Glazing VT (0.32) EUI 35.0 kBtu/ft 2 PV Panels to Achieve Net Zero 180% Roof Area All the passive design strategies with compliance to Title 24 were applied to the base model, such as WWR 20%, window SHGC 0.12, window U=0.3 Btu/h ft 2 F, wall insulation (U=0.016 Btu/h 141 ft 2 F), roof insulation (U=0.019 Btu/h ft 2 F), orientation (long side facing southwest) and glazing VT 0.32 (Table 3-9). The active design strategy, HVAC system was chosen as VAV Reheat system. The integration 2 could be considered as the integration of all strategies mentioned previously in chapter 3 that with the most energy efficient values. Then run the ApacheSim to get the energy simulation with EUI. 3.7.3.2 Integration 2 in Harbin The integration 2 is the consisted of seven passive design strategies mentioned in this chapter, WWR, window U-factor, wall insulation, roof insulation, glazing SHGC, orientation, glazing VT; and active design strategy, HVAC system (Table 3-10). Table 3-10 Integration 2 in Harbin Harbin Integration 2 1 VAV-Reheat system with heat recovery 2 WWR (20%) 3 Window SHGC (0.12) 4 Window (U=0.30 Btu/h ft 2 F) 5 Wall Insulation (U=0.016 Btu/h ft 2 F) 6 Roof Insulation (U=0.019 Btu/h ft 2 F) 7 Orientation (Long side facing Southwest) 8 Glazing VT (0.32) EUI 37.4 kBtu/ft 2 PV Panels to Achieve Net Zero 230% Roof Area 142 All the passive design strategies with compliance to Title 24 were applied to the base model, such as WWR 20%, window SHGC 0.12, window U=0.3 Btu/h ft 2 F, wall insulation (U=0.016 Btu/h ft 2 F), roof insulation (U=0.019 Btu/h ft 2 F), orientation (long side facing southwest) and glazing VT 0.32 (Table 3-10). The active design strategy, HVAC system was choosing as VAV Reheat system with heat recovery. The integration 2 could be considered as the integration of all strategies mentioned previously in chapter 3 that with the most energy efficient values. Then run the ApacheSim to get the energy simulation with EUI. 3.7.4 Integration 3 3.7.4.1 Integration 3 in Los Angeles The integration 3 is the consisted of five passive design strategies mentioned in this chapter, WWR, window SHGC, window U-factor; and active design strategy, HVAC system (Table 3-11). Table 3-11 Integration 3 in Los Angeles Los Angeles Integration 3 1 VAV-Reheat system with heat recovery 2 WWR (60%) 3 Window SHGC (0.12) 4 Window (U=0.30 Btu/h ft 2 F) 5 Wall Insulation (U=0.069 Btu/h ft 2 F) 6 Roof Insulation (U=0.049 Btu/h ft 2 F) 7 Orientation (Long side facing north) 8 Glazing VT (0.55) EUI 36.5 kBtu/ft 2 PV Panels to Achieve Net Zero 190% Roof Area 143 All the passive design strategies with compliance to Title 24 were applied to the base model, such as WWR 60%, window SHGC 0.12 and window U=0.30 Btu/h ft 2 F (Table 3-11). The active design strategy, HVAC system was choosn as VAV-Reheat system with heat recovery. Then run the ApacheSim to get the energy simulation with EUI. 3.7.4.2 Integration 3 in Harbin The integration 3 is the consisted of five passive design strategies mentioned in this chapter, WWR, window SHGC, window U-factor; and active design strategy, HVAC system (Table 3-12). Table 3-12 Integration 3 in Harbin Harbin Integration 3 1 VAV-Reheat system with heat recovery 2 WWR (60%) 3 Window SHGC (0.12) 4 Window (U=0.30 Btu/h ft 2 F) 5 Wall Insulation (U=0.069 Btu/h ft 2 F) 6 Roof Insulation (U=0.049 Btu/h ft 2 F) 7 Orientation (Long side facing north) 8 Glazing VT (0.55) EUI 40.0 kBtu/ft 2 PV Panels to Achieve Net Zero 240% Roof Area All the passive design strategies with compliance to Title 24 were applied to the base model, such as WWR 60%, window SHGC 0.12 and window U=0.30 Btu/h ft 2 F (Table 3-12). The active 144 design strategy, HVAC system was chosen as VAV reheat system withheat recovery. Then run the ApacheSim to get the energy simulation with EUI. 3.7.5 Integration 4 3.7.5.1 Integration 4 in Los Angeles The integration 4 is the consisted of five passive design strategies mentioned in this chapter, WWR, window SHGC, window U-factor and overhang; and active design strategy, heat recovery system, LED lighting fixture (Table 3-13). Table 3-13 Integration 4 in Los Angeles Los Angeles Integration 4 1 VAV-Reheat system with heat recovery 2 WWR (20%) 3 Window SHGC (0.12) 4 Window (U=0.30 Btu/h ft 2 F) 5 Wall Insulation (U=0.016 Btu/h ft 2 F) 6 Roof Insulation (U=0.019 Btu/h ft 2 F) 7 Orientation (Long side facing Southwest) 8 Glazing VT (0.32) 9 LED Lighting Fixture 10 With Overhang EUI 31.7 kBtu/ft 2 PV Panels to Achieve Net Zero 160% Roof Area 145 All the passive design strategies with compliance to Title 24 were applied to the base model, such as WWR 20%, window SHGC 0.12 and window U=0.30 Btu/h ft 2 F (Table 3-11). The active design strategy, HVAC system was choosn as VAV-Reheat system with heat recovery. Then run the ApacheSim to get the energy simulation with EUI. 3.7.5.2 Integration 4 in Harbin The integration 4 is the consisted of five passive design strategies mentioned in this chapter, WWR, window SHGC, window U-factor and overhang; and active design strategy, heat recovery system, LED lighting fixture (Table 3-14). Table 3-14 Integration 4 in Harbin Harbin Integration 3 1 VAV-Reheat system with heat recovery 2 WWR (20%) 3 Window SHGC (0.12) 4 Window (U=0.30 Btu/h ft 2 F) 5 Wall Insulation (U=0.016 Btu/h ft 2 F) 6 Roof Insulation (U=0.019 Btu/h ft 2 F) 7 Orientation (Long side facing Southwest) 8 Glazing VT (0.32) 9 LED Lighting Fixture 10 With Overhang EUI 33.9 kBtu/ft 2 PV Panels to Achieve Net Zero 200% Roof Area 146 All the passive design strategies with compliance to Title 24 were applied to the base model, such as WWR 60%, window SHGC 0.12 and window U=0.30 Btu/h ft 2 F (Table 3-12). The active design strategy, HVAC system was chosen as VAV reheat system withheat recovery. Then run the ApacheSim to get the energy simulation with EUI. 3.8 Summary All the design variables with different parameters were listed (Table 3-13). Table 3-13 Design Variables Design variables Wall insulation U=0.016 Btu/h ft 2 F U=0.046 Btu/h ft 2 F U=0.012 Btu/h ft 2 F Roof insulation U=0.019 Btu/h ft 2 F U=0.049 Btu/h ft 2 F U=0.079 Btu/h ft 2 F Window U-factor U=0.46 Btu/h ft 2 F U=0.38 Btu/h ft 2 F U=0.3 Btu/h ft 2 F WWR 60% 40% 20% Glazing SHGC 0.22 0.18 0.12 Glazing VT 0.55 0.43 0.32 Overhang factor 1 0.69 0.44 Orientation 0° 45° 90° 135° Radiance Natural ventilation 0.50cfm/ ft 2 0.30cfm/ ft 2 0.10cfm/ ft 2 HVAC system 14.0 EER 10.9 EER 10.0 EER 9.0 EER The 5 integrations of strategies in Los Angeles and Harbin were listed with the exact value of parameters (Table 3-14 & 3-15). 147 Table 3-14 Integrations in Los Angeles Los Angeles (Base Case) Integration 1 Integration 2 Integration 3 Integration 4 1 HVAC System (VAV- Reheat) HVAC System (VAV- Reheat) VAV-Reheat system with heat recovery VAV-Reheat system with heat recovery VAV-Reheat system with heat recovery 2 WWR (40%) WWR (20%) WWR (40%) WWR (20%) WWR (60%) 3 Window SHGC (0.22) Window SHGC (0.12) Window SHGC (0.22) Window SHGC (0.12) Window SHGC (0.12) 4 Window (U=0.46 Btu/h ft 2 F) Window (U=0.3 Btu/h ft 2 F) Window (U=0.46 Btu/h ft 2 F) Window (U=0.3 Btu/h ft 2 F) Window (U=0.3 Btu/h ft 2 F) 5 Wall Insulation (U=0.069 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) Wall Insulation (U=0.069 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) Wall Insulation (U=0.069 Btu/h ft 2 F) 6 Roof Insulation (U=0.049 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) Roof Insulation (U=0.049 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) Roof Insulation (U=0.049 Btu/h ft 2 F) 7 Orientation (Long side facing north) Orientation (Long side facing southwest) Orientation (Long side facing north) Orientation (Long side facing north) Orientation (Long side facing north) 8 Glazing VT (0.55) Glazing VT (0.32) Glazing VT (0.55) Glazing VT (0.55) Glazing VT (0.55) EUI PV 148 Table 3-15 Integrations in Harbin Harbin (Base Case) Integration 1 Integration 2 Integration 3 Integration 4 1 HVAC System (VAV- Reheat) HVAC System (VAV- Reheat) VAV-Reheat system with heat recovery VAV-Reheat system with heat recovery VAV-Reheat system with heat recovery 2 WWR (40%) WWR (20%) WWR (40%) WWR (20%) WWR (60%) 3 Window SHGC (0.22) Window SHGC (0.12) Window SHGC (0.22) Window SHGC (0.12) Window SHGC (0.12) 4 Window (U=0.46 Btu/h ft 2 F) Window (U=0.3 Btu/h ft 2 F) Window (U=0.46 Btu/h ft 2 F) Window (U=0.3 Btu/h ft 2 F) Window (U=0.3 Btu/h ft 2 F) 5 Wall Insulation (U=0.069 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) Wall Insulation (U=0.069 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) Wall Insulation (U=0.069 Btu/h ft 2 F) 6 Roof Insulation (U=0.049 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) Roof Insulation (U=0.049 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) Roof Insulation (U=0.049 Btu/h ft 2 F) 7 Orientation (Long side facing north) Orientation (Long side facing southwest) Orientation (Long side facing north) Orientation (Long side facing north) Orientation (Long side facing north) 8 Glazing VT (0.55) Glazing VT (0.32) Glazing VT (0.55) Glazing VT (0.55) Glazing VT (0.55) EUI PV IES VE is a completed software for dynamic energy simulation. The accuracy of input could highly affect the simulation results, such as, BTM parameters setting up, operation profile, internal gains, air exchange, construction materials and so on. After the base model was set up in both Los Angeles and Harbin, each strategy was applied to the base model to examine their energy efficiency, including passive and active strategies. In addition, the energy generation of PV panels was calculated with PVsyst to see the different energy generation with the different 149 area usage of roof. Then the 5 integrations of different strategies were running in the IES to derive the EUI to gain correspondingly EUI to check the effect of each integration on building energy performance. The correspondingly amount of PV panels (roof area usage) were calculated to put in the table. 150 Chapter 4A. Los Angeles Base Model Results The weather conditions in Los Angeles, USA, are mainly mild to hot and dry through the whole year. Chapter 4B has the results for Hardin, China. Both base models were set as a base line for comparison to analyze the effect of different design strategies. The EUI of the models was the metric used to evaluate the energy performance of building with the various design controls. This chapter discusses the energy consumption of the base model, how passive design strategies affect building energy consumption, and how active design strategy affect building energy consumption. 4A.1 Energy Consumption of the Base Model Lighting Energy Consumption According to the requirement of 2019 Building Energy Efficiency Standards, the allowed lighting power density of office building is 0.65 w/ft 2 (Title 24, Table 140.6-B). The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 Computer Energy Consumption The computer power density was set as 0.9 w/ft 2 (U.S. Department of Energy (DOE), Energy Efficiency & Renewable Energy). The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8.0 kBtu/ft 2 Domestic Hot Water Energy Consumption 151 The usage of domestic hot water was set as 1.1gallon/person/day (DOE, Energy Efficiency & Renewable Energy). For the open space workstation, 60-110 ft 2 per employee. The total mass of water: 8.34 x 1.1 gall/person/day x 100,000ft 2 /80 person = 11,467.5 lb; the total energy consumption for DHW: 11,467.5 lb x 1.0 Btu/lb F x 55 F x 261 day /1000 = 164,615.96 kBtu The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 Pump Energy Consumption The energy consumption was assumed as 5% to 10% of the total energy consumption (DOE reference buildings). It was assumed as 3.8 kBtu/ft 2 (9% of the total EUI). Thus, only the fan power, cooling and heating load were derived from the calculation of IES results, which were listed in the following steps for all the passive design strategies. Cooling and heating load Cooling load and heating load were derived from the VistaPro by utilizing the sum of internal heat gains, solar heat gains, external conduction gains and infiltration gains (Figure 4A-1). 152 Figure 4A-1 Gains of Base Model Then the annual hourly cooling load (positive number) and heating load (negative number) of the building were achieved. Only the hours during the period 8am to 6pm were counted of the calculation. The sum of positive number was base model annual cooling output, and the sum of negative number was base model annual heating output (Figure 4A-2). 153 Figure 4A-2 Spreadsheet of Cooling and Heating Load Sensible cooling: 2,370,385,430 Btu Cooling ventilation load: 1,825,756,019 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (2,370,385,430 Btu + 1,825,756,019 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 12 kBtu/ft 2 154 Sensible heating: 52,595,809 Btu Heating ventilation load: 15,957,713 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (52,595,809 Btu + 15,957,713 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 0.8 kBtu/ft 2 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) (Figure 4A-2) dt = temperature difference (F) (Figure 4A-3) The calculation of fan power was conducted in the spreadsheet. 155 Temperature difference (dt) represents temperature between indoor air temperature and dry-bulb temperature. Because the indoor space is conditioned, and heating setpoint and cooling setpoint were 68F and 74F, the indoor air temperature was setting consistently on 68F in winter and 74F in summer (Figure 4A-3). Figure 4A-3 Temperature Difference Calculation in Spreadsheet Fan power was calculated with the equation Q = hs / (1.08 x dt). hs was the hourly heating and cooling load extracted from previous step (Figure 4A-2). Then hourly Q could be derived. As the 156 minimum requirement of ventilation air flow is 0.15cfm/sf, the minimum ventilation rate of base model (100,000ft 2 ) is 15,000 cfm (Title, 2019). Thus, for the hours that have ventilation air volume flow lower than 15,000 cfm should be 15,000 instead. The sum of all the air volume flow, values higher than 15,000 cfm and values lower than 15,000 cfm (use 15,000 cfm) was the total Q (Figure 4A-4). Figure 4A-4Fan Power Calculation in Spreadsheet Then the fan power was calculated by the equation: fan power= Q x 0.65 cfm/watt. 157 Q = 377,484,989 Btu Fan power = 377,484,989 Btu x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 8.4 kBtu/ft 2 Total EUI The total EUI is the sum of the EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, DHW energy consumption, pump consumption, fan power, cooling, and heating consumption (Figure 4A-5). Figure 4A-5 Base Model EUI Stacked Bar Chart 6.7 8 1.7 3.8 12 0.8 8.4 0 5 10 15 20 25 30 35 40 45 Base model EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 158 Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 12 kBtu/ft 2 + 0.8 kBtu/ft 2 + 8.4 kBtu/ft 2 = 41.4 kBtu/ft 2 4A.2 How Passive Design Strategies Affect Building Energy Consumption Nine passive design strategies were applied to the base model one at a time, which was beneficial when analyzing the efficiency on each strategy. The model’s total EUI and break down EUI were used to evaluate the strategy efficiency. Then an efficient integration of the passive design strategies was explored and applied to the base model. The passive design strategies including wall insulation, roof insulation, window U-factor, WWR, window SHGC, Window VT, natural ventilation, orientation, with the parameters as mentioned in the previous chapters and it was concluded in this chapter (Table 4A-1). Table 4A-1 Base model passive design strategies Passive Design Strategies Parameter value Wall insulation U=0.069 Btu/h ft 2 F Roof insulation U=0.049 Btu/h ft 2 F Window U-factor U=0.46 Btu/h ft 2 F WWR 40% Window SHGC 0.22 Window VT 0.32 Window overhang 1 Natural ventilation No Building orientation Long side of building facing north 159 4A.2.1 Effects of Wall Insulation on Building Energy Consumption To examine the effect of exterior wall insulation on building energy efficiency, the U-factor U=0.016 Btu/h ft 2 F and U=0.12 Btu/h ft 2 F of exterior wall insulation were applied to the base model. The EUI and break down EUI were considered as the metrics to compare the building energy efficiency. 4A.2.1.1 EUI Break Down Exterior Wall Insulation U = 0. 016 Btu/h ft 2 F The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 2,363,912,480 Btu Cooling ventilation load: 1,826,341,755 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (2,363,912,480 Btu + 1,826,341,755 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 11.9 kBtu/ft 2 160 Sensible heating: 49,765,054 Btu Heating ventilation load: 11,393,133 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (49,765,054 Btu + 11,393,133 Btu)/ 0.9/ 100,000ft 2 / 1000 = 0.7 kBtu/ft 2 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in the spreadsheet. Q = 374,432,237 Btu Fan power = 374,432,237 Btu x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 8.3 kBtu/ft 2 Total EUI 161 The total EUI the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4A-6). Figure 4A-6 Model with Exterior Wall U=0.016 Btu/h ft 2 F EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 11.9 kBtu/ft 2 + 0.7 kBtu/ft 2 + 8.3 kBtu/ft 2 = 41.1 kBtu/ft 2 6.7 8 1.7 3.8 11.9 0.7 8.3 0 5 10 15 20 25 30 35 40 45 Wall Insulation U=0.016 Btu/h ft2 F EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 162 4A.2.1.2 Exterior Wall Insulation U = 0.12 Btu/h ft 2 F The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 2,374,011,609 Btu Cooling ventilation load: 1,821,610,540 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (2,374,011,609 Btu + 1,821,610,540 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 11.9 kBtu/ft 2 Sensible heating: 55,709,919 Btu Heating ventilation load: 19,135,876 Btu Efficiency = heating output/ heating input = 0.9 163 Heating load EUI = heating output/ 0.9 = (55,709,919 Btu + 19,135,876 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 0.8 kBtu/ft 2 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 379,615,412 Btu Fan power = 379,615,412 Btu x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 8.4 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4A-7). 164 Figure 4A-7 Model with Exterior Wall U=0.012 Btu/h ft 2 F EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 11.9 kBtu/ft 2 + 0.8 kBtu/ft 2 + 8.4 kBtu/ft 2 = 41.3 kBtu/ft 2 4A.2.2 Effects of Roof Insulation on Building Energy Consumption To examine the effect of exterior roof insulation on building energy efficiency, the U-factor U=0.019 Btu/h ft 2 F and U=0.079 Btu/h ft 2 F of roof insulation were applied to the base model. 6.7 8 1.7 3.8 11.9 0.8 8.4 0 5 10 15 20 25 30 35 40 45 Wall Insulation U=0.12 Btu/h ft2 F EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 165 The EUI and break down EUI were considered as the metrics to compare the building energy efficiency. 4A.2.2.1 Roof Insulation U = 0.019 Btu/h ft 2 F The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 2,380,158,675 Btu Cooling ventilation load: 1,837,285,252 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (2,380,158,675 Btu + 1,837,285,252 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 12 kBtu/ft 2 Sensible heating: 50,572,610 Btu Heating ventilation load: 13,589,680 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (50,572,610 Btu + 13,589,680 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 0.7 kBtu/ft 2 166 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 377,536,247 Btu Fan power = 377,536,247 Btu x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 8.4 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4A-8). 167 Figure 4A-8 Model with Roof Insulation U=0.019 Btu/h ft 2 F EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 12 kBtu/ft 2 + 0.7 kBtu/ft 2 + 8.4 kBtu/ft 2 = 41.3 kBtu/ft 2 4A.2.2.2 Roof Insulation U = 0.079 Btu/h ft2 F The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). 6.7 8 1.7 3.8 12 0.7 8.4 0 5 10 15 20 25 30 35 40 45 Roof Insulation U=0.019 Btu/h ft2 F EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 168 Cooling and heating load Sensible cooling: 2,360,972,987 Btu Cooling ventilation load: 1,814,243,960 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (2,360,972,928 Btu + 1,814,243,960 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 12 kBtu/ft 2 Sensible heating: 54,699,491 Btu Heating ventilation load: 17,501,125 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (54,699,491 Btu + 17,501,125 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 0.8 kBtu/ft 2 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt 169 Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 377,100,349 Btu Fan power = 377,100,349 Btu x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 8.4 kBtu/ft 2 The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4A-9). 170 Figure 4A-9 Model with Roof Insulation U=0.079 Btu/h ft 2 F EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 12 kBtu/ft 2 + 0.8 kBtu/ft 2 + 8.4 kBtu/ft 2 = 41.4 kBtu/ft 2 6.7 8 1.7 3.8 12 0.8 8.4 0 5 10 15 20 25 30 35 40 45 Roof Insulation U=0.079 Btu/h ft2 F EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 171 4A.2.3 Effects of Window U-value on Building Energy Consumption To examine the effect of window U-value on building energy efficiency, the U-factor U=0.30 Btu/h ft 2 F and U=0.38 Btu/h ft 2 F of window were applied to the base model. The EUI and break down EUI were considered as the metrics to compare the building energy efficiency. 4A.2.3.1 Window U = 0.30 Btu/h ft2 F The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x 10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x 10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 2,362,903,860 Btu Cooling ventilation load: 1,829,463,960 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (2,360,972,928 Btu + 1,814,243,960 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 12 kBtu/ft 2 Sensible heating: 44,921,513 Btu Heating ventilation load: 4,871,377 Btu 172 Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (44,921,513 Btu + 4,871,377 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 0.6 kBtu/ft 2 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 373,696,926 Btu Fan power = 373,696,926 Btu x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 8.3 kBtu/ft 2 Total EUI 173 The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4A-10). Figure 4A-10 Model with Window U=0.30 Btu/h ft 2 F EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 12 kBtu/ft 2 + 0.6 kBtu/ft 2 + 8.3 kBtu/ft 2 = 41.1 kBtu/ft 2 4A.2.3.2 Window U = 0.38 Btu/h ft 2 F The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 6.7 8 1.7 3.8 12 0.6 8.3 0 5 10 15 20 25 30 35 40 45 Window U=0.3 Btu/h ft2 F EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 174 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 2,366,413,908 Btu Cooling ventilation load: 1,827,261,072 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (2,366,413,908 Btu + 1,827,261,072 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 12 kBtu/ft 2 Sensible heating: 48,770,236 Btu Heating ventilation load: 8,533,626 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (48,770,236 Btu + 8,533,626 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 0.6 kBtu/ft 2 Fan power 175 The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 375,586,745Btu Fan power = 375,586,745 Btu x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 8.3 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4A-11). 176 Figure 4A-11 Model with Window U=0.38 Btu/h ft 2 F EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 12 kBtu/ft 2 + 0.6 kBtu/ft 2 + 8.3 kBtu/ft 2 = 41.1 kBtu/ft 2 6.7 8 1.7 3.8 12 0.6 8.3 0 5 10 15 20 25 30 35 40 45 Window U=0.38 Btu/h ft2 F EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 177 4A.2.4 Effects of WWR on Building Energy Consumption To examine the effect of WWR on building energy efficiency, the window to wall ratio of 20% and 60% were applied to the base model. The EUI and break down EUI were considered as the metrics to compare the building energy efficiency. 4A.2.4.1 WWR 20% The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 2,248,555,223 Btu Cooling ventilation load: 1,751,925,977 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (2,248,555,223 Btu + 1,751,925,977 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 11 kBtu/ft 2 Sensible heating: 43,638,238 Btu Heating ventilation load: 899,165 Btu 178 Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (43,638,238 Btu + 899,165 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 0.5 kBtu/ft 2 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 355,066,207 Btu Fan power = 355,066,207 Btu x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 7.8 kBtu/ft 2 Total EUI 179 The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4A-12). Figure 4A-12 Model with Window WWR 20% EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 11 kBtu/ft 2 + 0.5 kBtu/ft 2 + 7.8 kBtu/ft 2 = 39.5 kBtu/ft 2 4A.2.4.2 WWR 60% The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 6.7 8 1.7 3.8 11 0.5 7.8 0 5 10 15 20 25 30 35 40 45 WWR 20% EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 180 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 2,547,082,064 Btu Cooling ventilation load: 1,951,724,139 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (2,547,082,064 Btu + 1,951,724,139 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 13 kBtu/ft 2 Sensible heating: 58,966,458 Btu Heating ventilation load: 28,670,203 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (58,966,458 Btu + 28,670,203 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 1.0 kBtu/ft 2 Fan power 181 The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 406,663,349 Btu Fan power = 406,663,349 Btu x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 9.0 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4A-13). 182 Figure 4A-13 Model with Window WWR 60% EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 13 kBtu/ft 2 + 1 kBtu/ft 2 + 9 kBtu/ft 2 = 43.2 kBtu/ft 2 4A.2.5 Effects of Window SHGC on Building Energy Consumption To examine the effect of window SHGC on building energy efficiency, the SHGC value 0.18 and 0.12 of window were applied to the base model. The EUI and break down EUI were considered as the metrics to compare the building energy efficiency. 6.7 8 1.7 3.8 13 1 9 0 5 10 15 20 25 30 35 40 45 50 WWR 60% EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 183 4A.2.5.1 Window SHGC 0.18 The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 2,310,071,204 Btu Cooling ventilation load: 1,780,613,913 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (2,310,071,204 Btu + 1,780,613,913 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 12 kBtu/ft 2 Sensible heating: 55,048,895 Btu Heating ventilation load: 16,336,872 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (55,048,895 Btu + 16,336,872 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 0.8 kBtu/ft 2 184 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 369,314,062 cfm Fan power = 369,314,062 cfm x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 8.0 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4A-14). 185 Figure 4A-14Model with Window SHGC 0.18 EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 12 kBtu/ft 2 + 0.8 kBtu/ft 2 + 8 kBtu/ft 2 = 41.0 kBtu/ft 2 4A.2.5.2 Window SHGC 0.12 The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). 6.7 8 1.7 3.8 12 0.8 8 0 5 10 15 20 25 30 35 40 45 Window SHGC 0.18 EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 186 Cooling and heating load Sensible cooling: 2,208,282,623 Btu Cooling ventilation load: 1,706,723,952 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (2,310,071,204 Btu + 1,780,613,913 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 11 kBtu/ft 2 Sensible heating: 61,951,256 Btu Heating ventilation load: 16,511,568 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (55,048,895 Btu + 16,336,872 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 0.9 kBtu/ft 2 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt 187 Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 353,617,684 cfm Fan power = 353,617,684 cfm x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 7.8 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4A-15). 188 Figure 4A-15 Model with Window WWR 20% EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 11 kBtu/ft 2 + 0.9 kBtu/ft 2 + 7.8 kBtu/ft 2 = 39.9 kBtu/ft 2 4A.2.6 Effects of Window VT on Building Energy Consumption The VT was set as 0.43 and 0.55 to examine the effect of its efficiency on the base model. However, the break down and total EUI for two different values were the same as base model. 6.7 8 1.7 3.8 11 0.9 7.8 0 5 10 15 20 25 30 35 40 45 Window SHGC 0.12 EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 189 The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8.0 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 2,385,113,445 Btu Cooling ventilation load: 1,728,352,021 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (2,385,113,445 Btu + 1,728,352,021 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 12 kBtu/ft 2 Sensible heating: 52,278,813 Btu Heating ventilation load: 16,799,745 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (52,278,813 Btu + 52,278,813 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 0.8 kBtu/ft 2 Fan power 190 The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 369,134,976 cfm Fan power = 369,134,976 cfm x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 8.4 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, DHW energy consumption, pump consumption, fan power, cooling and heating consumption. Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 12 kBtu/ft 2 + 0.8 kBtu/ft 2 + 8.4 kBtu/ft 2 = 41.4 kBtu/ft 2 191 4A.2.9 Effects of Building Orientation on Building Energy Consumption To examine the effect of orientation on building energy efficiency, the rotation of 45-degree, 90- degree and 135-degree of building were applied to the base model. The EUI and break down EUI were considered as the metrics to compare the building energy efficiency. 4A.2.9.1 Northwest (rotating 45°) The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 2,376,981,510 Btu Cooling ventilation load: 1,825,929,743 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (2,376,981,510 Btu + 1,825,929,743 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 12 kBtu/ft 2 Sensible heating: 52,742,586 Btu Heating ventilation load: 15,390,222 Btu 192 Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (52,742,586 Btu + 15,390,222 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 0.8 kBtu/ft 2 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 406,072,563Btu Fan power = 406,072,563 Btu x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 8.5 kBtu/ft 2 Total EUI 193 The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4A-16). Figure 4A-16 Model with Orientation 45° EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 12 kBtu/ft 2 + 0.8 kBtu/ft 2 + 8.5 kBtu/ft 2 = 41.5 kBtu/ft 2 4A.2.9.2 Orientation West (rotating 90°) The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 6.7 8 1.7 3.8 12 0.8 8.5 0 5 10 15 20 25 30 35 40 45 Orientation 45 Degree EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 194 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 2,382,717,909 Btu Cooling ventilation load: 1,831,282,711 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (2,382,717,909 Btu + 1,831,282,711 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 12 kBtu/ft 2 Sensible heating: 51,154,166 Btu Heating ventilation load: 15,714,708 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (51,154,166 Btu + 15,714,708 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 0.7 kBtu/ft 2 Fan power 195 The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 382,014,318cfm Fan power = 382,014,318 cfm x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 8.5 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4A-17). 196 Figure 4A-17 Model with Orientation 90° EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 12 kBtu/ft 2 + 0.7 kBtu/ft 2 + 8.5 kBtu/ft 2 = 41.4 kBtu/ft 2 4A.2.9.3 Orientation Southwest (rotating 135°) The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 6.7 8 1.7 3.8 12 0.7 8.5 0 5 10 15 20 25 30 35 40 45 Orientation 90 Degree EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 197 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 2,379,257,091 Btu Cooling ventilation load: 1,830,641,390 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (2,379,257,091 Btu + 1,830,641,390 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 12 kBtu/ft 2 Sensible heating: 51,685,023 Btu Heating ventilation load: 15,994,127 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (51,154,166 Btu + 15,714,708 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 0.7 kBtu/ft 2 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt 198 Q = air volume flow (cfm) (including the ventilation for heating, cooling, and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 380,022,790 cfm Fan power = 380,022,790 cfm x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 8.4 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4A-18). 199 Figure 4A-18 Model with Orientation 135° EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 12 kBtu/ft 2 + 0.7 kBtu/ft 2 + 8.4 kBtu/ft 2 = 41.3 kBtu/ft 2 4A.4 Summary for Los Angeles 6.7 8 1.7 3.8 12 0.7 8.5 0 5 10 15 20 25 30 35 40 45 Orientation 135 Degree EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 200 Nine passive design strategies, exterior wall insulation, roof insulation, window U-value, WWR, window SHGC, VT, orientation, were applied to the base model in Los Angeles to explore their effects on building energy consumption. For each passive design strategy, two different values were adopted in the research to be the comparisons to base model. The exterior wall insulation U-value of base model is U=0.069 Btu/h ft 2 F. The result from IES simulation and spreadsheet of total EUI of base model is 41.4 kBtu/ft 2 per year. The exterior wall insulation with U-value, U=0.12 Btu/h ft 2 F and U=0.016 Btu/h ft 2 F were the comparison groups to examine its efficiency. Correspondingly results of total EUI were 41.4 kBtu/ft 2 and 41.1 kBtu/ ft 2 (reduced by 0.7%) (Figure 4A-19). Figure 4A-19 Wall Insulation EUI Comparison Histogram (kBtu/sf) 41.4 41.1 -0.7% 41.5 0.2% 0 5 10 15 20 25 30 35 40 45 Base Model U=0.069 Btu/h ft2 F Wall Insulation U=0.016 Btu/h ft2 F Wall Insulaiton U=0.12 Btu/h ft2 F EUI Comparison Histogram (Wall Insulation) KBtu/sf 201 The roof insulation U-value of base model is U=0.049 Btu/h ft 2 F. The result from IES simulation and spreadsheet of total EUI of base model is 41.4 kBtu/ft 2 per year. The roof insulation with U- value, U=0.79 Btu/h ft 2 F and U=0.019 Btu/h ft 2 F were the comparison groups to examine its efficiency. Correspondingly results of total EUI were 41.4 kBtu/ft 2 and 41.3 kBtu/ ft 2 (reduced by 0.2%) (Figure 4A-20). Figure 4A-20 Roof Insulation EUI Comparison Histogram (kBtu/sf) The window U-value of base model is U=0.46 Btu/h ft 2 F. The result from IES simulation and spreadsheet of total EUI of base model is 41.4 kBtu/ft 2 per year. The window with U-value, U=0.38 Btu/h ft 2 F and U=0.30 Btu/h ft 2 F were the comparison groups to examine its efficiency. Correspondingly results of total EUI were 41.1 kBtu/ft 2 (reduced by 0.7%) and 41.1 kBtu/ ft 2 (reduced by 0.7%) (Figure 4A-21). 41.4 41.3 -0.2% 41.5 0.2% 0 5 10 15 20 25 30 35 40 45 Base Model U=0.049 Btu/h ft2 F Roof Insulation U=0.019 Btu/h ft2 F Roof Insulaiton U=0.79 Btu/h ft2 F EUI Comparison Histogram (Roof Insulation) KBtu/sf 202 Figure 4A-21 Window U-value EUI Comparison Histogram (kBtu/sf) The WWR of base model is 40%. The result from IES simulation and spreadsheet of total EUI of base model is 41.4 kBtu/ft 2 per year. The WWR with 60% and 20% were the comparison groups to examine its efficiency. Correspondingly results of total EUI were 43.2 kBtu/ft 2 (increased by 4.3%) and 39.5 kBtu/ ft 2 (reduced by 4.6%) (Figure 4A-22). 41.4 41.1 -0.7% 41.1 -0.7% 0 5 10 15 20 25 30 35 40 45 Base Model U=0.46 Btu/h ft2 F Window U=0.38 Btu/h ft2 F Window U=0.3 Btu/h ft2 F EUI Comparison Histogram (Window U-factor) KBtu/sf 203 Figure 4A-22 WWR EUI Comparison Histogram (kBtu/sf) The window SHGC of base model is 0.22. The result from IES simulation and spreadsheet of total EUI of base model is 41.4 kBtu/ft 2 per year. The window SHGC with 0.18 and 0.12 were the comparison groups to examine its efficiency. Correspondingly results of total EUI were 41.0 kBtu/ft 2 (reduced by 1.0%) and 39.9 kBtu/ ft 2 (reduced by 3.6%) (Figure 4A-23). 41.4 43.2 4.3% 39.5 -4.6% 0 5 10 15 20 25 30 35 40 45 50 Base Model 40% WWR 60% WWR 20% EUI Comparison Histogram (WWR) KBtu/sf 204 Figure 4A-23 Window SHGC EUI Comparison Histogram (kBtu/sf) The window VT of base model is 0.32. The result from IES simulation and spreadsheet of total EUI of base model is 41.4 kBtu/ft 2 per year. The window VT with 0.43 and 0.55 were the comparison groups to examine its efficiency. Correspondingly results of total EUI were 41.4 kBtu/ ft 2 , which was the same as base model (Figure 4A-24). 41.4 41.0 -1.0% 39.9 -3.6% 0 5 10 15 20 25 30 35 40 45 Base Model 0.22 SHGC 0.18 SHGC 0.12 EUI Comparison Histogram (Window SHGC) KBtu/sf 205 Figure 4A-24 Window VT EUI Comparison Histogram (kBtu/sf) The long side of base model is facing north (floor plan of model is symmetrical). The result from IES simulation and spreadsheet of total EUI of base model is 41.4 kBtu/ft 2 per year. The model orientation was rotating 45-degree, 90-degree and 135-degree to be the comparison groups to examine its efficiency. Correspondingly results of total EUI were 41.5 kBtu/ft 2 (increased by 0.2%), 41.4 kBtu/ft 2 and 41.3 kBtu/ ft 2 (reduced by -0.2%) (Figure 4A-25). 41.4 41.4 41.4 0 5 10 15 20 25 30 35 40 45 Base Model 0.32 VT 0.43 VT 0.55 EUI Comparison Histogram (VT) KBtu/sf 206 Figure 4A-25 Orientation EUI Comparison Histogram (kBtu/sf) The exterior wall, roof insulation and window U-value had slight effect on the building energy consumption in Los Angeles, while the WWR and window SHGC had evident influence on the building EUI. Visible transmittance did not change the building energy consumption at all, and the orientation of building had slight effect. 41.4 41.5 0.2% 41.4 41.3 -0.2% 0 5 10 15 20 25 30 35 40 45 Base Model 45 Degree 90 Degree 135 Degree EUI Comparison Histogram (Orientation) KBtu/sf 207 Chapter 4B. Harbin Base Model Results In Harbin, China, the summer is long and warm; however, winter is short, cold, and snowy. The temperature ranges from -11 °F to 81 °F. The base model was set as a base line for comparison to analyze the effect of different design strategies. The EUI of the model was the metric used to evaluate the energy performance of building with the various design controls. This chapter discusses the energy consumption of the base model, how passive design strategies affect building energy consumption, and how active design strategy affect building energy consumption. 4B.1 Energy Consumption of the Base Model Lighting Energy Consumption According to the requirement of 2019 Building Energy Efficiency Standards, the allowed lighting power density of office building is 0.65 w/ft 2 (Title 24, Table 140.6-B). The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 Computer Energy Consumption The computer power density was set as 0.9 w/ft 2 (U.S. Department of Energy (DOE), Energy Efficiency & Renewable Energy). The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8.0 kBtu/ft 2 Domestic Hot Water Energy Consumption 208 The usage of domestic hot water was set as 1.1gallon/person/day (DOE, Energy Efficiency & Renewable Energy). For the open space workstation, 60-110ft 2 per employee. The total mass of water: 8.34 x 1.1 gall/person/day x 100,000 ft 2 /80 person = 11,467.5 lb; the total energy consumption for DHW: 11,467.5 lb x 1.0 Btu/lb F x 55 F x 261 day /1000 = 164,615.96 kBtu The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 Pump Energy Consumption The energy consumption was assumed as 5% to 10% of the total energy consumption (DOE reference buildings). It was assumed as 3.8 kBtu/ft 2 (9% of the total EUI). Thus, only the fan power, cooling and heating load were derived from the calculation of IES results, which were listed in the following steps for all the passive design strategies. Cooling and heating load Then the annual hourly cooling load (positive number) and heating load (negative number) of the building were achieved. Only the hours during the period 8am to 6pm were counted of the calculation. The sum of positive number was base model annual cooling output, and the sum of negative number was base model annual heating output. Sensible cooling: 1,623,861,688 Btu Cooling ventilation load: 1,118,902,454 Btu EER = cooling output/ cooling input = 3.5 209 Cooling load EUI = cooling output/ 3.5 = (1,623,861,688 Btu + 1,118,902,454 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 7.8 kBtu/ft 2 Sensible heating: 459,670,654 Btu Heating ventilation load: 413,117,380 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (459,670,654 Btu + 413,117,380 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 9.7 kBtu/ft 2 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 345,181,436 cfm 210 Fan power = 345,181,436 cfm x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 7.7 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, DHW energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4B-1). Figure 4B-1 Base Model Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 7.8 kBtu/ft 2 + 9.7 kBtu/ft 2 + 7.7 kBtu/ft 2 = 45.4 kBtu/ft 2 6.7 8 1.7 3.8 7.8 9.7 7.7 0 5 10 15 20 25 30 35 40 45 50 Base Model EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 211 4B.2 How Passive Design Strategies Affect Building Energy Consumption Nine passive design strategies were applied to the base model one at a time, which was beneficial when analyzing the efficiency on each strategy. The model’s total EUI and break down EUI were used to evaluate the strategy efficiency. Then an efficient integration of the passive design strategies was explored and applied to the base model. The passive design strategies including wall insulation, roof insulation, window U-factor, WWR, window SHGC, Window VT, window overhang factor, natural ventilation, orientation. with the parameters as mentioned in the previous chapters and it was concluded in this chapter (Table 4B-1). Table 4B-1 Base model passive design strategies Passive Design Strategies Parameter value Wall insulation U=0.069 Btu/h ft 2 F Roof insulation U=0.049 Btu/h ft 2 F Window U-factor U=0.46 Btu/h ft 2 F WWR 40% Window SHGC 0.22 Window VT 0.32 Window overhang 1 Natural ventilation No Building orientation Long side of building facing north 4B.2.1 Effects of Wall Insulation on Building Energy Consumption To examine the effect of exterior wall insulation on building energy efficiency, the U-factor U=0.016 Btu/h ft 2 F and U=0.12 Btu/h ft 2 F of exterior wall insulation were applied to the base model. The EUI and break down EUI were considered as the metrics to compare the building energy efficiency. 212 4B.2.1.1 Exterior Wall Insulation U = 0.016 Btu/h ft 2 F The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 1,649,917,854 Btu Cooling ventilation load: 1,146,688,040 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (1,649,917,854 Btu + 1,146,688,040 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 7.9kBtu/ft 2 Sensible heating: 421,326,737 Btu Heating ventilation load: 381,541,724 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (421,326,737 Btu + 381,541,724 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 8.9 kBtu/ft 2 213 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 342,013,931 cfm Fan power = 342,013,931 cfm x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 7.6 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4B-2). 214 Figure 4B-2 Model with Wall Insulation U=0.016 Btu/h ft 2 F EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 7.9 kBtu/ft 2 + 8.9 kBtu/ft 2 + 7.6 kBtu/ft 2 = 44.6 kBtu/ft 2 4B.2.1.2 Exterior Wall Insulation U = 0.12 Btu/h ft 2 F The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 6.7 8 1.7 3.8 7.9 8.9 7.6 0 5 10 15 20 25 30 35 40 45 50 Wall Insulation U=0.016 Btu/h ft2 F EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 215 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 1,602,146,158 Btu Cooling ventilation load: 1,095,841,020 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (2,379,257,091 Btu + 1,830,641,390 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 7.7 kBtu/ft 2 Sensible heating: 503,799,104 Btu Heating ventilation load: 448,698,453 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (51,154,166 Btu + 15,714,708 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 10.5 kBtu/ft 2 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: 216 Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 348,845,486 cfm Fan power = 348,845,486 cfm x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 7.7 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling, and heating consumption (Figure 4B-3). 217 Figure 4B-3 Model with Wall Insulation U=0.12 Btu/h ft 2 F EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 7.7 kBtu/ft 2 + 10.5 kBtu/ft 2 + 7.7 kBtu/ft 2 = 46.1 kBtu/ft 2 6.7 8 1.7 3.8 7.7 10.5 7.7 0 5 10 15 20 25 30 35 40 45 50 Wall Insulation U=0.12 Btu/h ft2 F EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 218 4B.2.2 Effects of Roof Insulation on Building Energy Consumption To examine the effect of exterior roof insulation on building energy efficiency, the U-factor U=0.019 Btu/h ft 2 F and U=0.079 Btu/h ft 2 F of roof insulation were applied to the base model. The EUI and break down EUI were considered as the metrics to compare the building energy efficiency. 4B.2.2.1 Roof Insulation U = 0.019 Btu/h ft 2 F The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 1,654,869,264 Btu Cooling ventilation load: 1,148,236,968 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (1,654,869,264 Btu + 1,148,236,968 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 8 kBtu/ft 2 Sensible heating: 427,696,159 Btu 219 Heating ventilation load: 386,592,977 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (427,696,159 Btu + 386,592,977 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 9 kBtu/ft 2 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 344,568,753 Btu Fan power = 344,568,753 Btu x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 7.6 kBtu/ft 2 Total EUI 220 The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4B-4). Figure 4B-4 Model with Roof Insulation U=0.019 Btu/h ft 2 F EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 8 kBtu/ft 2 + 9 kBtu/ft 2 + 7.6 kBtu/ft 2 = 44.8 kBtu/ft 2 4B.2.2.2 Roof Insulation U = 0.079 Btu/h ft 2 F The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 6.7 8 1.7 3.8 8 9 7.6 0 5 10 15 20 25 30 35 40 45 50 Roof Insulation U=0.019 Btu/h ft2 F EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 221 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 1,596,908,833.00 Btu Cooling ventilation load: 1,094,635,630 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (1,596,908,833 Btu + 1,814,243,960 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 9.7 kBtu/ft 2 Sensible heating: 493,659,641 Btu Heating ventilation load: 440,572,204 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (493,659,641 Btu + 440,572,204 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 10.4 kBtu/ft 2 Fan power 222 The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 346,222,941 Btu Fan power = 346,222,941 Btu x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 7.7 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4B-5). 223 Figure 4B-5 Model with Roof Insulation U=0.079 Btu/h ft 2 F EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7kBtu/ft 2 + 8kBtu/ft 2 + 1.7kBtu/ft 2 + 3.8kBtu/ft 2 + 9.7kBtu/ft 2 + 10.4kBtu/ft 2 + 7.7kBtu/ft 2 = 48kBtu/ft 2 6.7 8 1.7 3.8 9.7 10.4 7.7 0 5 10 15 20 25 30 35 40 45 50 Roof Insulation U=0.079 Btu/h ft2 F EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 224 4B.2.3 Effects of Window U-value on Building Energy Consumption To examine the effect of window U-value on building energy efficiency, the U-factor U=0.30 Btu/h ft 2 F and U=0.38 Btu/h ft 2 F of window were applied to the base model. The EUI and break down EUI were considered as the metrics to compare the building energy efficiency. 4B.2.3.1 Window U = 0.30 Btu/h ft 2 F The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 1,681,804,171 Btu Cooling ventilation load: 1,178,789,552 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (1,681,804,171 Btu + 1,178,789,552 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 8.2 kBtu/ft 2 Sensible heating: 390,336,581 Btu Heating ventilation load: 354,955,912 Btu 225 Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (390,336,581 Btu + 354,955,912 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 8.3 kBtu/ft 2 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 341,019,570 Btu Fan power = 341,019,570 Btu x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 7.6 kBtu/ft 2 Total EUI 226 The total EUI is the sum from EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4B-6). Figure 4B-6 Model with Window U=0.30 Btu/h ft 2 F EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 8.2 kBtu/ft 2 + 8.3 kBtu/ft 2 + 7.6 kBtu/ft 2 = 44.3 kBtu/ft 2 4B.2.3.2 Window U = 0.38 Btu/h ft 2 F The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 6.7 8 1.7 3.8 8.2 8.3 7.6 0 5 10 15 20 25 30 35 40 45 50 Window U=0.3 Btu/h ft2 F EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 227 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 1,650,391,552 Btu Cooling ventilation load: 1,146,980,078 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (1,650,391,552 Btu + 1,146,980,078 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 8 kBtu/ft 2 Sensible heating: 424,102,341 Btu Heating ventilation load: 384,435,088 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (424,102,341 Btu + 384,435,088 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 9 kBtu/ft 2 Fan power 228 The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 342,944,814 Btu Fan power = 342,944,814 Btu x 0.65cfm/watt x 3.41/ 100,000ft 2 / 1000 = 7.6kBtu/ft 2 Total EUI The total EUI is the sum from EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4B-7). 229 Figure 4B-7 Model with Window U=0.38 Btu/h ft 2 F EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 8 kBtu/ft 2 + 9 kBtu/ft 2 + 7.6 kBtu/ft 2 = 44.8 kBtu/ft 2 6.7 8 1.7 3.8 8 9 7.6 0 5 10 15 20 25 30 35 40 45 50 Window U=0.38 Btu/h ft2 F EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 230 4B.2.4 Effects of WWR on Building Energy Consumption To examine the effect of WWR on building energy efficiency, the window to wall ratio of 20% and 60% were applied to the base model. The EUI and break down EUI were considered as the metrics to compare the building energy efficiency. 4B.2.4.1 WWR 20% The lighting use density: 0.75w/ft 2 x 3.41Btu/h x10h x 261day / 1000 = 6.7kBtu/ft 2 The computer use density: 0.9w/ft 2 x 3.41Btu/h x10h x 261day / 1000 = 8kBtu/ft 2 The DHW use density: 164,615.96KBtu / 100,000ft 2 = 1.7kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 1,581,657,259 Btu Cooling ventilation load: 1,102,160,273 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (2,248,555,223 Btu + 1,751,925,977 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 7.7 kBtu/ft 2 Sensible heating: 395,143,360 Btu Heating ventilation load: 359,380,814 Btu 231 Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (43,638,238 Btu + 899,165 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 8.4 kBtu/ft 2 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 324,895,355 Btu Fan power = 324,895,355 Btu x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 7.2 kBtu/ft 2 Total EUI 232 The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4B-8). Figure 4B-8 Model with Window WWR 20% EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 7.7 kBtu/ft 2 + 8.4 kBtu/ft 2 + 7.2 kBtu/ft 2 = 43.5 kBtu/ft 2 4B.2.4.2 WWR 60% The lighting use density: 0.75w/ft 2 x 3.41Btu/h x10h x 261day / 1000 = 6.7kBtu/ft 2 6.7 8 1.7 3.8 7.7 8.4 7.2 0 5 10 15 20 25 30 35 40 45 50 WWR 20% EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 233 The computer use density: 0.9w/ft 2 x 3.41Btu/h x10h x 261day / 1000 = 8kBtu/ft 2 The DHW use density: 164,615.96KBtu / 100,000ft 2 = 1.7kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 1,682,913,612 Btu Cooling ventilation load: 1,154,450,992 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (1,682,913,612 Btu + 1,154,450,992 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 8.1 kBtu/ft 2 Sensible heating: 544,292,516 Btu Heating ventilation load: 482,818,690 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (544,292,516 Btu + 482,818,690 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 11.4 kBtu/ft 2 Fan power 234 The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 367,417,185 Btu Fan power = 367,417,185 Btu x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 8.1 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4B-9). 235 Figure 4B-9 Model with Window WWR 60% EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 8.1 kBtu/ft 2 + 11.4 kBtu/ft 2 + 8.1 kBtu/ft 2 = 47.8 kBtu/ft 2 4B.2.5 Effects of Window SHGC on Building Energy Consumption To examine the effect of window SHGC on building energy efficiency, the SHGC value 0.18 and 0.12 of window were applied to the base model. The EUI and break down EUI were considered as the metrics to compare the building energy efficiency. 6.7 8 1.7 3.8 8.1 11.4 8.1 0 5 10 15 20 25 30 35 40 45 50 WWR 60% EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 236 4B.2.5.1 Window SHGC 0.18 The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 1,582,757,806 Btu Cooling ventilation load: 1,088,041,793 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (1,582,757,806 Btu + 1,088,041,793 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 7.6 kBtu/ft 2 Sensible heating: 465,470,811 Btu Heating ventilation load: 417,094,758 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (465,470,811 Btu + 417,094,758 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 9.8 kBtu/ft 2 237 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 338,947,341 cfm Fan power = 338,947,341 cfm x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 7.5 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4B-10). 238 Figure 4B-10 Model with Window SHGC 0.18 EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 7.6 kBtu/ft 2 + 9.8 kBtu/ft 2 + 7.5 kBtu/ft 2 = 45.1 kBtu/ft 2 4B.2.5.2 Window SHGC 0.12 The lighting use density: 0.75w/ft 2 x 3.41Btu/h x10h x 261day / 1000 = 6.7kBtu/ft 2 The computer use density: 0.9w/ft 2 x 3.41Btu/h x10h x 261day / 1000 = 8kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). 6.7 8 1.7 3.8 7.6 9.8 7.5 0 5 10 15 20 25 30 35 40 45 50 Window SHGC 0.18 EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 239 Cooling and heating load Sensible cooling: 1,513,971,873 Btu Cooling ventilation load: 1,033,720,147 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (1,513,971,873 Btu + 1,033,720,147 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 7.3 kBtu/ft 2 Sensible heating: 483,752,242 Btu Heating ventilation load: 432,241,549 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (483,752,242 Btu + 432,241,549 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 10.2 kBtu/ft 2 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt 240 Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 330,088,520 cfm Fan power = 330,088,520 cfm x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 7.3 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4B-11). 241 Figure 4B-11 Model with Window SHGC 0.12 EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 7.3 kBtu/ft 2 + 10.2 kBtu/ft 2 + 7.3 kBtu/ft 2 = 45 kBtu/ft 2 4B.2.6 Effects of Window VT on Building Energy Consumption The VT was set as 0.43 and 0.55 to examine the effect of its efficiency on the base model. However, the break down and total EUI for two different values were the same as base model. 6.7 8 1.7 3.8 7.3 10.3 7.3 0 5 10 15 20 25 30 35 40 45 50 Window SHGC 0.12 EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 242 The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8.0 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 1,623,861,688 Btu Cooling ventilation load: 1,118,902,454 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (1,623,861,688 Btu + 1,118,902,454 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 7.8 kBtu/ft 2 Sensible heating: 459,670,654 Btu Heating ventilation load: 413,117,380 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (459,670,654 Btu + 413,117,380 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 9.7 kBtu/ft 2 Fan power 243 The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 345,181,436 cfm Fan power = 345,181,436 cfm x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 7.7 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, DHW energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4B-12). 244 Figure 4B-12 Base Model Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 7.8 kBtu/ft 2 + 9.7 kBtu/ft 2 + 7.7 kBtu/ft 2 = 45.41 kBtu/ft 2 4B.2.9 Effects of Building Orientation on Building Energy Consumption To examine the effect of orientation on building energy efficiency, the rotation of 45-degree, 90- degree and 135-degree of building were applied to the base model. The EUI and break down EUI were considered as the metrics to compare the building energy efficiency. 6.7 8 1.7 3.8 7.8 9.7 7.7 0 5 10 15 20 25 30 35 40 45 50 Base Model EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 245 4B.2.9.1 Northwest (rotating 45°) The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 1,625,664,950 Btu Cooling ventilation load: 1,117,631,207 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (1,625,664,950 Btu + 1,117,631,207 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 7.8 kBtu/ft 2 Sensible heating: 460,293,400 Btu Heating ventilation load: 412,378,497 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (460,293,400 Btu + 412,378,497 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 9.7 kBtu/ft 2 246 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 346,313,915 Btu Fan power = 346,313,915 Btu x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 7.7 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4B-13). 247 Figure 4B-13 Model with Orientation 45° EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 7.8 kBtu/ft 2 + 9.7 kBtu/ft 2 + 7.7 kBtu/ft 2 = 45.4 kBtu/ft 2 4B.2.9.2 Orientation West (rotating 90°) The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). 6.7 8 1.7 3.8 7.8 8.7 7.7 0 5 10 15 20 25 30 35 40 45 50 Orientation 45 Degree EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 248 Cooling and heating load Sensible cooling: 1,622,830,690 Btu Cooling ventilation load: 1,116,000,817 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (1,622,830,690 Btu + 1,116,000,817 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 7.8 kBtu/ft 2 Sensible heating: 461,541,221 Btu Heating ventilation load: 414,172,923 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (51,154,166 Btu + 15,714,708 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 9.7 kBtu/ft 2 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt 249 Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 346,220,421 cfm Fan power = 346,220,421 cfm x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 7.7 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4B-14). 250 Figure 4B-14 Model with Orientation 90° EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 7.8 kBtu/ft 2 + 9.7 kBtu/ft 2 + 7.7 kBtu/ft 2 = 45.4 kBtu/ft 2 4B.2.9.3 Orientation Southwest (rotating 135°) The lighting use density: 0.75 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 6.7 kBtu/ft 2 The computer use density: 0.9 w/ft 2 x 3.41 Btu/h x10 h x 261 day / 1000 = 8 kBtu/ft 2 The DHW use density: 164,615.96 kBtu / 100,000 ft 2 = 1.7 kBtu/ft 2 6.7 8 1.7 3.8 7.8 9.7 7.7 0 5 10 15 20 25 30 35 40 45 50 Orientation 90 Degree EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 251 The pump use density: 3.8 kBtu/ft 2 (9% of the total EUI) (DOE reference buildings). Cooling and heating load Sensible cooling: 1,629,296,196 Btu Cooling ventilation load: 1,123,772,910 Btu EER = cooling output/ cooling input = 3.5 Cooling load EUI = cooling output/ 3.5 = (1,629,296,196 Btu + 1,123,772,910 Btu)/ 3.5 / 100,000 ft 2 / 1000 = 7.8 kBtu/ft2 Sensible heating: 454,116,236 Btu Heating ventilation load: 408,028,649 Btu Efficiency = heating output/ heating input = 0.9 Heating load EUI = heating output/ 0.9 = (454,116,236 Btu + 408,028,649 Btu)/ 0.9/ 100,000 ft 2 / 1000 = 9.6 kBtu/ft 2 Fan power The fan power includes the energy consumption for heating and cooling air supply and air ventilation. The fan power was calculated with the equations: Q = hs / (1.08 x dt) & fan power = Q x 0.65 cfm/watt 252 Q = air volume flow (cfm) (including the ventilation for heating, cooling and minimum requirement of air flow) hs = sensible heat (heating and cooling, Btu/hr) dt = temperature difference (F) The calculation of fan power was conducted in spreadsheet. Q = 345,019,955 cfm Fan power = 345,019,955 cfm x 0.65 cfm/watt x 3.41/ 100,000 ft 2 / 1000 = 7.7 kBtu/ft 2 Total EUI The total EUI is the sum of EUI break downs calculated in the previous paragraphs, such as lighting energy consumption, computer energy consumption, domestic hot water energy consumption, pump consumption, fan power, cooling and heating consumption (Figure 4B-15). 253 Figure 4B-15 Model with Orientation 135° EUI Stacked Bar Chart Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 12 kBtu/ft 2 + 0.7 kBtu/ft 2 + 8.4 kBtu/ft 2 = 45.3 kBtu/ft 2 4B.4 Summary for Harbin 6.7 8 1.7 3.8 7.8 9.7 7.7 0 5 10 15 20 25 30 35 40 45 50 Orientation 135 Degree EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 254 Nine passive design strategies, exterior wall insulation, roof insulation, window U-value, WWR, window SHGC, VT, orientation, were applied to the base model in Harbin to explore their effects on building energy consumption. For each passive design strategy, two different values were adopted in the research to be the comparisons to base model. The exterior wall insulation U-value of base model is U=0.069 Btu/h ft 2 F. The result from IES simulation and spreadsheet of total EUI of base model is 45.4 kBtu/ft 2 per year. The exterior wall insulation with U-value, U=0.12 Btu/h ft 2 F and U=0.016 Btu/h ft 2 F were the comparison groups to examine its efficiency. Correspondingly results of total EUI were 46.1 kBtu/ft 2 (increased by 1.5%) and 44.6 kBtu/ ft 2 (reduced by 1.8%) (Figure 4B-16). Figure 4B-16 Wall Insulation EUI Comparison Histogram (kBtu/sf) The roof insulation U-value of base model is U=0.049 Btu/h ft 2 F. The result from IES simulation and spreadsheet of total EUI of base model is 45.4 kBtu/ft 2 per year. The roof insulation with U- 45.4 44.6 -1.8% 46.1 1.5% 0 5 10 15 20 25 30 35 40 45 50 Base Model U=0.069 Btu/h ft2 F Wall Insulation U=0.016 Btu/h ft2 F Wall Insulaiton U=0.12 Btu/h ft2 F EUI Comparison Histogram (Wall Insulation) KBtu/sf 255 value, U=0.79 Btu/h ft 2 F and U=0.019 Btu/h ft 2 F were the comparison groups to examine its efficiency. Correspondingly results of total EUI were 48 kBtu/ft 2 (increased by 5.7%) and 44.8 kBtu/ ft 2 (reduced by 1.3%) (Figure 4B-17). Figure 4B-17 Roof Insulation EUI Comparison Histogram (kBtu/sf) The window U-value of base model is U=0.46 Btu/h ft 2 F. The result from IES simulation and spreadsheet of total EUI of base model is 45.4 kBtu/ft 2 per year. The window with U-value, U=0.38 Btu/h ft 2 F and U=0.30 Btu/h ft 2 F were the comparison groups to examine its efficiency. Correspondingly results of total EUI were 44.8 kBtu/ft 2 and 44.3 kBtu/ ft 2 (Figure 4B-18). 45.4 44.8 -1.3% 48 5.7% 0 5 10 15 20 25 30 35 40 45 50 Base Model U=0.049 Btu/h ft2 F Roof Insulation U=0.019 Btu/h ft2 F Roof Insulaiton U=0.79 Btu/h ft2 F EUI Comparison Histogram (Roof Insulation) KBtu/sf 256 Figure 4B-18Window U-value EUI Comparison Histogram (kBtu/sf) The WWR of base model is 40%. The result from IES simulation and spreadsheet of total EUI of base model is 45.4 kBtu/ft 2 per year. The WWR with 60% and 20% were the comparison groups to examine its efficiency. Correspondingly results of total EUI were 47.8 kBtu/ft 2 (increased by 5.3%) and 43.5 kBtu/ ft 2 (reduced by 4.2%) (Figure 4B-19). 45.4 44.8 -1.3% 44.3 -2.4% 0 5 10 15 20 25 30 35 40 45 50 Base Model U=0.46 Btu/h ft2 F Window U=0.38 Btu/h ft2 F Window U=0.3 Btu/h ft2 F EUI Comparison Histogram (Window U-factor) KBtu/sf 257 Figure 4B-19 WWR EUI Comparison Histogram (kBtu/sf) The window SHGC of base model is 0.22. The result from IES simulation and spreadsheet of total EUI of base model is 45.4 kBtu/ft 2 per year. The window SHGC with 0.18 and 0.12 were the comparison groups to examine its efficiency. Correspondingly results of total EUI were 45.1 kBtu/ft 2 (reduced by 0.7%) and 45.0 kBtu/ ft 2 (reduced by 0.9%) (Figure 4B-20). 45.4 47.8 5.3% 43.5 -4.2% 0 5 10 15 20 25 30 35 40 45 50 Base Model 40% WWR 60% WWR 20% EUI Comparison Histogram (WWR) KBtu/sf 258 Figure 4B-20 Window SHGC EUI Comparison Histogram (kBtu/sf) The window VT of the base model is 0.32. The result from IES simulation and spreadsheet of total EUI of the base model is 45.4 kBtu/ft 2 per year. The window VT with 0.43 and 0.55 were the comparison groups to examine its efficiency. Correspondingly results of total EUI were 45.4 kBtu/ ft 2 , which was the same as the base model (Figure 4B-21). 45.4 45.1 -0.7% 45 -0.9% 0 5 10 15 20 25 30 35 40 45 50 Base Model 0.22 SHGC 0.18 SHGC 0.12 EUI Comparison Histogram (Window SHGC) KBtu/sf 259 Figure 4-21 Window VT EUI Comparison Histogram (kBtu/sf) The long side of the base model is facing north (the floor plan of the base model is symmetrical). The result from IES simulation and spreadsheet of total EUI of the base model is 45.4 kBtu/ft 2 per year. The model with orientation was rotating 45-degree, 90-degree, and 135-degree to be the comparison groups to examine its efficiency. Correspondingly results of total EUI were 45.4 kBtu/ft 2 , 45.4 kBtu/ft 2 and 45.3 kBtu/ ft 2 (reduced by 0.2%) (Figure 4B-22). 45.4 45.4 45.4 0 5 10 15 20 25 30 35 40 45 50 Base Model 0.32 VT 0.43 VT 0.55 EUI Comparison Histogram (VT) KBtu/sf 260 Figure 4B-22 Orientation EUI Comparison Histogram (kBtu/sf) Which is different from the model in Los Angeles, U-value of exterior wall, roof insulation and window had evident effect on the building energy consumption in Harbin, while the window SHGC had slight influence on the building EUI. Visible transmittance did not change the building energy consumption at all. WWR and the orientation of building had obvious influence on the model too. 45.4 45.4 45.4 45.3 -0.2% 0 5 10 15 20 25 30 35 40 45 50 Base Model 45 Degree 90 Degree 135 Degree EUI Comparison Histogram (Orientation) KBtu/sf 261 Chapter 5A. Los Angeles Base Model Analysis The analysis of the base model and models with passive and active design strategies is based on the EUI of models, the graphs of which were shown in the chapter 4 and will be shown again in this chapter. This chapter describes the base model analysis, analysis of model applied with passive design strategies and analysis of model applied with passive design strategies in Los Angeles. 5A.1 Energy Consumption of Base Model Analysis The lighting energy consumption, computer consumption, DHW consumption and pump consumption were calculated with the certain energy intensity which were based on the California building energy codes. Thus, it was assumed that the amount of energy consumption of them were not affected by application of passive and active strategies. Only the energy consumption for cooling, heating and ventilation were considered as energy break down that were affected by the design strategies. The total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 12 kBtu/ft 2 + 0.8 kBtu/ft 2 + 8.4 kBtu/ft 2 = 41.4 kBtu/ft 2 (Figure 5A- 1). 262 Figure 5A-1 Los Angeles Base Model EUI Stacked Bar Chart Cooling and heating load of the building are highly depending on the local weather condition. As the dry-bulb temperature is relatively high through the year, the cooling season is much longer than heating season, which result in the cooling load is extremely high compared to its heating load (Figure 5A-2). 6.7 8 1.7 3.8 12 0.8 8.4 0 5 10 15 20 25 30 35 40 45 Base model EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 263 Figure 5A-2 Average High and Low Temperature in Los Angeles (Weather Spark, 2022) In addition, the base model, an office building, is an internal gain dominant model, because of the large amount of internal heat gain during the working time. Internal heat gain includes lighting heat gain, computers heat gain and people heat gain in this model and their values of sensible gain and latent gain were mentioned in chapter 3, which correspondingly maximum sensible gain 0.9W/ft 2 , maximum sensible gain 1W/ft 2 , maximum sensible gain 307.093 Btu/h/person and maximum latent gain 204.728 Btu/h/person. Cooling load increased because of those internal heat gain form 8 am in the morning to 6 pm in the evening to keep the indoor air temperature at setpoint 74°F. The EUI of the base model Heating energy consumption is less than 1 kBtu/ft 2 which is much lower than the cooling load (EUI 12 kBtu/ft 2 ). It is because the duration of dry-bulb temperature 264 lower than 68 °F (heating setpoint) is really short. Also, in that duration, most of the time is at night when the system is not on to condition the space. In addition, the internal heat gain contributes to the indoor air temperature increasing, so the indoor air temperature could be higher than 68 °F without utilizing the HVAC system. All those reasons resulted in the extreme low heating energy consumption Fan power is the energy consumption that consists of ventilation for cooling air flow, ventilation for heating air flow and minimum ventilation air flow. Thus, the ventilation for cooling and heating air flow are highly depending on the cooling and heating load of model. As mentioned in chapter 4, the minimum ventilation rate is 0.15 cfm/ft 2 and the total area of the base model is 100,000 ft 2 , so the total minimum ventilation air flow for the base model is 15,000 cfm. For the time HVAC system not providing cooling and heating or the ventilation air flow is lower than 15,000 cfm, the system should provide air ventilation to keep minimum air flow at 15,000 cfm. 5A.2 Analysis of Model Applied with Passive Design Strategies To test the efficiency of passive design strategies, nine passive controls were chosen and applied to the base model, exterior wall insulation, roof insulation, window u-value, WWR, window SHGC, VT, overhang, natural ventilation, and orientation. In addition, the result of an integration of all the effective passive design strategies was analyzed. 5A.2.1 Analysis of Wall Insulation on the Model The exterior wall insulation U-value of the base model was set as U=0.069 Btu/h ft 2 F. The U- value of the two test cases was set as U=0.12 Btu/h ft 2 F and U=0.016 Btu/h ft 2 F to get the corresponding result of cooling, heating, fan power energy use intensity and EUI. The graphs were 265 chosen on two days of a year, July 1 st in hot summer and December 1 st , as the representative days. They included indoor air temperature, dry-bulb temperature, and external conduction were utilized to analyze the energy consumption difference, as the heat exchange between indoor and outdoor environment is the key effect result from insulation (Figure 5A-3 & Figure 5A-4). Figure 5A-3 Los Angeles Base Model December 1 st External Conduction, Indoor and Outdoor Temperature 266 Figure 5A-4 Los Angeles Base Model July 1 st External Conduction, Indoor and Outdoor Temperature 5A.2.1.1 Exterior Wall Insulation U=0.016 Btu/h ft 2 F The EUI stacked bar chart comparison of the base model and model with U=0.016 Btu/h ft 2 F wall insulation is beneficial to analyzing effect of wall insulation (Figure 5A-5). The difference of total EUI and break down EUI was easy to compare and see the changes. 267 Figure 5A-5 Los Angeles Base Model and Wall Insulation U=0.016 Btu/h ft 2 F EUI Comparison After replacing the wall insulation with U=0.016 Btu/h ft 2 F, model cooling, heating and fan power were correspondingly reduced 0.1 kBtu/sf. The graphs included indoor air temperature, dry-bulb temperature, and external conduction (on two days of a year, July 1 st in hot summer and December 1 st ) were utilized to analyze the energy consumption difference, as the heat exchange between indoor and outdoor environment is the key effect result from insulation (Figure 5A-6 & Figure 5A-7). 6.7 6.7 8 8 1.7 1.7 3.8 3.8 12 11.9 0.8 0.7 8.4 8.3 0 5 10 15 20 25 30 35 40 45 Base Model Wall Insulation U=0.016 Btu/h ft2 F EUI Comparison Fan Power Heating Cooling Pump DHW Equipment Lighting 268 Figure 5A-6 Los Angeles Wall Insulation U=0.016 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature 269 Figure 5A-7 Los Angeles Wall Insulation U=0.016 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature The graph was utilized to analyze the relation of wall U-value and space external conduction (Figure 5A-6 & Figure 5A-7). In the graph, indoor air temperature and dry-bulb temperature were the same as they represented the results of the same day (December 1st) the base model while the external conduction gains were different because of the varied wall insulation. Indoor air temperature started to rise at 8:00 am when the HVAC system started working (set in the profile) until it got the setpoint (74 °F) and kept it constant until people left work at 6:00pm. Dry-bulb temperature was the outdoor temperature of December 1st of a typical year, low at morning and night, went high during the daytime and got the peak point around 2:00 pm. Heat loss ranging as 270 the indoor temperature and dry-bulb temperature vary along the daytime. The graph of July 1 st is different from the graph of December 1 st as the higher dry-bulb temperature. Less heat loss at the first peak and second peak comparing to the base model (Figure 5A-7). The difference of external conduction gains of U=0.069 Btu/h ft 2 F and U=0.016 Btu/h ft 2 F showed the effect of wall insulation on building energy consumption. The shapes of them were similar, but for the graph with lower U-value, the peak point and the whole value through the day were lower which represented the less external conduction. More heat loss from the base model comparing to model with wall insulation U=0.016 Btu/h ft 2 F could be detected by the graphs which showing more external conduction (with negative numbers). Less heat transferred to outdoor space from indoor space in the model with wall insulation U=0.016 Btu/h ft 2 F proved that lower U-value could reduce heat conduction. 5A.2.1.2 Exterior Wall Insulation U=0.12 Btu/h ft 2 F The EUI stacked bar chart comparison of the base model and model with U=0.12 Btu/h ft 2 F wall insulation is beneficial to analyzing effect of wall insulation (Figure 5A-8). The difference of total EUI and break down EUI was easy to compare and see the changes. 271 Figure 5-8 Los Angeles Base Model and Wall Insulation U=0.12 Btu/h ft 2 F EUI Comparison After replaced the wall insulation with U=0.12 Btu/h ft 2 F, model total EUI was increased by 0.1 kBtu/sf. The graphs included indoor air temperature, dry-bulb temperature, and external conduction (on two days of a year, July 1 st in hot summer and December 1 st ) were utilized to analyze the energy consumption difference, as the heat exchange between indoor and outdoor environment is the key effect result from insulation (Figure 5A-9 & Figure 5A-10). 6.7 6.7 8 8 1.7 1.7 3.8 3.8 12 12 0.8 0.8 8.4 8.4 0 5 10 15 20 25 30 35 40 45 Base Model Wall Insulation U=0.12 Btu/h ft2 F EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 272 Figure 5-9 Los Angeles Wall Insulation U=0.12 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature 273 Figure 5A-10 Los Angeles Wall Insulation U=0.12 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature The graph was utilized to analyze the relation of wall U-value and space external conduction (Figure 5A-9 & Figure 5A-10). In the graph, indoor air temperature and dry-bulb temperature were the same as they represented the results of the same day (December 1st) base model while the external conduction gains were different because of the varied wall insulation. Indoor air temperature started to rise at 8:00 am when the HVAC system started working (set in the profile) until it got the setpoint (74 °F) and kept it constant until people left work at 6:00 pm. Dry-bulb temperature was the outdoor temperature of December 1st of a typical year, low at morning and night, went high during the daytime and got the peak point around 2:00 pm. Heat loss ranging as the indoor temperature and dry-bulb temperature vary along the daytime. The graph of July 1 st is 274 different from the graph of December 1 st as the higher dry-bulb temperature. More heat loss at the first peak and second peak compared to the base model (Figure 5A-10). The difference of external conduction gains of U=0.069 Btu/h ft 2 F and U=0.12 Btu/h ft 2 F showed the effect of wall insulation on building energy consumption. The shapes of them were similar, but for the graph with lower U-value, the peak point and the whole value through the day were lower which represented the less external conduction. 5A.2.1.3 Wall Insulation Summary The EUI bar chart comparison of the base model, model with U=0.016 Btu/h ft 2 F wall insulation and model with U=0.12 Btu/h ft 2 F wall insulation is beneficial to analyzing effect of wall insulation (Figure 5A-11). Figure 5A-11 Los Angeles Wall Insulation EUI Comparison Bar Chart Comparing to the base model, the total EUI of model with U=0.016 Btu/h ft 2 F wall insulation was reduced by 0.3 kBtu/sf (0.7%); the total EUI of model with U=0.12 Btu/h ft 2 F wall insulation was 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 12 11.9 12.1 0.8 0.7 0.8 8.4 8.3 8.4 0 5 10 15 20 25 30 35 40 45 Base Model Wall Insulation U=0.016 Btu/h ft2 F Wall Insulation U=0.12 Btu/h ft2 F EUI Comparison (kBtu/SF) Fan Power Heating Cooling Pump DHW Equipment Lighting -0.7% 0.2% 275 increased by 0.1 kBtu/sf (0.2%). The results showed that wall insulation could affect building energy consumption, however, the change of it was very slight. 5A.2.2 Analysis of Roof Insulation on the Model The roof insulation U-value of the base model was set as U=0.049 Btu/h ft 2 F. The U-value was set as U=0.019 Btu/h ft 2 F and U=0.079 Btu/h ft 2 F to get the corresponding result of cooling, heating, fan power energy use intensity and EUI. 5A.2.2.1 Roof Insulation U=0.019 Btu/h ft 2 F The EUI stacked bar chart comparison of the base model and model with U=0.016 Btu/h ft 2 F roof insulation is beneficial to analyzing effect of roof insulation (Figure 5A-12). The difference of total EUI and break down EUI was easy to compare and see the changes. Figure 5A-12 Los Angeles Base Model and Roof Insulation U=0.019 Btu/h ft 2 F EUI Comparison 6.7 6.7 8 8 1.7 1.7 3.8 3.8 12 12 0.8 0.7 8.4 8.4 0 5 10 15 20 25 30 35 40 45 Base Model Roof Insulation U=0.019 Btu/h ft2 F EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 276 After replacing the roof insulation with U=0.019 Btu/h ft 2 F, model cooling, fan power EUI were the same and heating was reduced 0.1 kBtu/sf. The graphs included indoor air temperature, dry-bulb temperature, and external conduction (on two days of a year, July 1 st in hot summer and December 1 st ) were utilized to analyze the energy consumption difference, as the heat exchange between indoor and outdoor environment is the key effect result from insulation (Figure 5A-13 & Figure 5A-14). Figure 5A-13 Los Angeles Roof Insulation U=0.019 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature 277 Figure 5A-14 Los Angeles Roof Insulation U=0.079 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature The graph was utilized to analyze the relation of roof U-value and space external conduction (Figure 5A-13 & Figure 5A-14). In the graph, indoor air temperature and dry-bulb temperature were the same as they represented the results of the same day (December 1st) the base model while the external conduction gains were different because of the varied roof insulation. Indoor air temperature started to rise at 8:00 am when the HVAC system started working (set in the profile) until it got the setpoint (74 °F) and kept it constant until people left work at 6:00pm. Dry-bulb temperature was the outdoor temperature of December 1st of a typical year, low at morning and night, went high during the daytime and got the peak point around 2:00 pm. Heat loss ranging as 278 the indoor temperature and dry-bulb temperature vary along the daytime. The graph of July 1 st is different from the graph of December 1 st as the higher dry-bulb temperature. Less heat loss at the first peak and second peak compared to the base model (Figure 5A-14). The difference of external conduction gains of U=0.049 Btu/h ft 2 F and U=0.019 Btu/h ft 2 F showed the effect of roof insulation on building energy consumption. The shapes of them were similar, but for the graph with lower U-value, the peak point and the whole value through the day were lower which represented the less external conduction. More heat loss from the base model comparing to model with wall insulation U=0.019 Btu/h ft 2 F could be detected by the graphs which showing more external conduction (with negative numbers). Less heat transferred to outdoor space from indoor space in the model with wall insulation U=0.019 Btu/h ft 2 F proved that lower U-value could reduce heat conduction. 5A.2.2.2 Roof Insulation U=0.079 Btu/h ft 2 F The EUI stacked bar chart comparison of the base model and model with U=0.079 Btu/h ft 2 F roof insulation is beneficial to analyzing effect of roof insulation (Figure 5A-15). The difference of total EUI and break down EUI was easy to compare and see the changes. 279 Figure 5A-15 Los Angeles Base Model and Roof Insulation U=0.12 EUI Comparison After replacing the wall insulation with U=0.079 Btu/h ft 2 F, model cooling, fan power was the same and heating increased by 0.1 kBtu/sf. The graphs included indoor air temperature, dry-bulb temperature, and external conduction (on two days of a year, July 1 st in hot summer and December 1 st ) were utilized to analyze the energy consumption difference, as the heat exchange between indoor and outdoor environment is the key effect result from insulation (Figure 5A-16 & Figure 5A-17). 6.7 6.7 8 8 1.7 1.7 3.8 3.8 12 12 0.8 0.9 8.4 8.4 0 5 10 15 20 25 30 35 40 45 Base Model Roof Insulation U=0.079 Btu/h ft2 F EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 280 Figure 5A-16 Los Angeles Roof Insulation U=0.079 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature 281 Figure 5A-17 Los Angeles Roof Insulation U=0.079 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature The graph was utilized to analyze the relation of roof U-value and space external conduction (Figure 5A-16 & Figure 5A-17). In the graph, indoor air temperature and dry-bulb temperature were the same as they represented the results of the same day (December 1st) the base model while the external conduction gains were different because of the varied roof insulation. Indoor air temperature started to rise at 8:00 am when the HVAC system started working (set in the profile) until it got the setpoint (74 °F) and kept it constant until people left work at 6:00 pm. Dry-bulb temperature was the outdoor temperature of December 1st of a typical year, low at morning and night, went high during the daytime and got the peak point around 2:00 pm. Heat loss ranging as the indoor temperature and dry-bulb temperature vary along the daytime. The graph of July 1 st is 282 different from the graph of December 1 st as the higher dry-bulb temperature. More heat loss at the first peak and second peak compared to the base model (Figure 5A-17). The difference of external conduction gains of U=0.049 Btu/h ft 2 F and U=0.079 Btu/h ft 2 F showed the effect of roof insulation on building energy consumption. The shapes of them were similar, but for the graph with lower U-value, the peak point and the whole value through the day were lower which represented the less external conduction. 5A.2.2.3 Roof Insulation Summary The EUI bar chart comparison of the base model, model with U=0.019 Btu/h ft 2 F roof insulation and model with U=0.79 Btu/h ft 2 F roof insulation is beneficial to analyzing effect of roof insulation (Figure 5A-18). Figure 5A-18 Los Angeles Roof Insulation EUI Comparison Bar Chart Comparing to the base model, the total EUI of model with U=0.019 Btu/h ft 2 F roof insulation was reduced by 0.1 kBtu/sf (0.2%); the total EUI of model with U=0.79 Btu/h ft 2 F roof insulation was 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 12 12 12 0.8 0.7 0.9 8.4 8.4 8.4 0 5 10 15 20 25 30 35 40 45 Base Model Roof Insulation U=0.019 Btu/h ft2 F Roof Insulation U=0.079 Btu/h ft2 F EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -0.2% 0.2% 283 increased by 0.1 kBtu/sf (0.2%). The results showed that roof insulation could affect building energy consumption, however, the change of it was very slight. 5A.2.3 Analysis of Window U-value on the Model The window U-value of the base model was set as U=0.46 Btu/h ft 2 F. The U-value was set as U=0.38 Btu/h ft 2 F and U=0.30 Btu/h ft 2 F to get the corresponding result of cooling, heating, fan power energy use intensity and EUI. 5A.2.3.1 Window U=0.38 Btu/h ft 2 F The EUI stacked bar chart comparison of the base model and model with window U=0.38 Btu/h ft 2 F is beneficial to analyzing effect of window U-factor (Figure 5A-19). The difference of total EUI and break down EUI was easy to compare and see the changes. Figure 5A-19 Los Angeles Base Model and Window U=0.38 Btu/h ft 2 F EUI Comparison After replacing the window with U=0.38 Btu/h ft 2 F, model cooling was the same, but fan power and heating were reduced by 0.1 kBtu/sf and 0.2 kBtu/sf. 6.7 6.7 8 8 1.7 1.7 3.8 3.8 12 12 0.8 0.6 8.4 8.3 0 5 10 15 20 25 30 35 40 45 Base Model Window U=0.38 Btu/h ft2 F EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 284 The graphs included indoor air temperature, dry-bulb temperature, and external conduction (on two days of a year, July 1 st in hot summer and December 1 st ) were utilized to analyze the energy consumption difference, as the heat exchange between indoor and outdoor environment is the key effect result from window U-value (Figure 5A-20 & Figure 5A-21). Figure 5A-20 Los Angeles Window U=0.38 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature 285 Figure 5-21 Los Angeles Window U=0.38 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature The graph was utilized to analyze the relation of window U-value and space external conduction (Figure 5A-20 & Figure 5A-21). In the graph, indoor air temperature and dry-bulb temperature were the same as they represented the results of the same day (December 1st) the base model while the external conduction gains were different because of the varied window U-value. Indoor air temperature started to rise at 8:00 am when the HVAC system started working (set in the profile) until it got the setpoint (74 °F) and kept it constant until people left work at 6:00pm. Dry-bulb temperature was the outdoor temperature of December 1st of a typical year, low at morning and night, went high during the daytime and got the peak point around 2:00 pm. Heat loss ranging as 286 the indoor temperature and dry-bulb temperature vary along the daytime. The graph of July 1 st is different from the graph of December 1 st as the higher dry-bulb temperature. Less heat loss at the first peak and second peak compared to the base model (Figure 5A-21). The difference of external conduction gains of U=0.46 Btu/h ft 2 F and U=0.38 Btu/h ft 2 F showed the effect of window U-value on building energy consumption. The shapes of them were similar, but for the graph with lower U-value, the peak point and the whole value through the day were lower which represented the less external conduction. More heat loss from the base model comparing to model with window U=0.49 Btu/h ft 2 F could be detected by the graphs which showing more external conduction (with negative numbers). Less heat transferred to outdoor space from indoor space in the model with window U=0.38 Btu/h ft 2 F proved that lower U-value could reduce heat conduction. 5A.2.2.2 Window U=0.30 Btu/h ft 2 F The EUI stacked bar chart comparison of the base model and model with window U=0.30 Btu/h ft 2 F is beneficial to analyzing effect of window U-value (Figure 5A-22). The difference of total EUI and break down EUI was easy to compare and see the changes. 287 Figure 5-22 Los Angeles Base Model and Window U=0.30 Btu/h ft 2 F EUI Comparison After replacing the window with U=0.30 Btu/h ft 2 F, model cooling, fan power was the same and heating increased by 0.1 kBtu/sf. The graphs included indoor air temperature, dry-bulb temperature, and external conduction (on two days of a year, July 1 st in hot summer and December 1 st ) were utilized to analyze the energy consumption difference, as the heat exchange between indoor and outdoor environment is the key effect result from insulation (Figure 5A-23 & Figure 5A-24). 6.7 6.7 8 8 1.7 1.7 3.8 3.8 12 12 0.8 0.6 8.4 8.3 0 5 10 15 20 25 30 35 40 45 Base Model Window U=0.30 Btu/h ft2 F EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 288 Figure 5A-23 Los Angeles Window U=0.30 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature 289 Figure 5A-24 Los Angeles Window U=0.30 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature The graph was utilized to analyze the relation of roof U-value and space external conduction (Figure 5A-23 & Figure 5A-24). In the graph, indoor air temperature and dry-bulb temperature were the same as they represented the results of the same day (December 1st) the base model while the external conduction gains were different because of the varied roof insulation. Indoor air temperature started to rise at 8:00 am when the HVAC system started working (set in the profile) until it got the setpoint (74 °F) and kept it constant until people left work at 6:00 pm. Dry-bulb temperature was the outdoor temperature of December 1st of a typical year, low at morning and night, went high during the daytime and got the peak point around 2:00 pm. Heat loss ranging as the indoor temperature and dry-bulb temperature vary along the daytime. The graph of July 1 st is 290 different from the graph of December 1 st as the higher dry-bulb temperature. More heat loss at the first peak and second peak compared to the base model (Figure 5A-24). The difference of external conduction gains of U=0.49 Btu/h ft 2 F and U=0.30 Btu/h ft 2 F showed the effect of window U-value on building energy consumption. The shapes of them were similar, but for the graph with lower U-value, the peak point and the whole value through the day were lower which represented the less external conduction. 5A.2.3.3 Window U-value Summary The EUI bar chart comparison of the base model, model with window U=0.38 Btu/h ft 2 F and model with window U=0.30 Btu/h ft 2 F is beneficial to analyzing effect of window U-value (Figure 5A-25). Figure 5A-25 Los Angeles Window U-value EUI Comparison Bar Chart 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 12 12 12 0.8 0.6 0.6 8.4 8.3 8.3 0 5 10 15 20 25 30 35 40 45 Base Model Window U=0.38 Btu/h ft2 F Window U=0.30 Btu/h ft2 F EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -0.7% -0.7% 291 Comparing to the base model, the total EUI of model with window U=0.38 Btu/h ft 2 F and window U=0.30 Btu/h ft 2 F was the same, which was reduced by 0.3 kBtu/sf (0.7%). The results showed that window U-value could affect building energy consumption more than the wall U-values, however, the change of it was very slight. 5A.2.4 Analysis of WWR on the Model The WWR of the base model was set as 40%. The WWR was set as 60% and 20% to get the corresponding result of cooling, heating, fan power energy use intensity and EUI. The EUI stacked bar chart comparison of the base model, the model with WWR 60% and the model WWR 20% are beneficial to analyzing effect of WWR (Figure 5A-26). The difference of total EUI and break down EUI was easy to compare and see the changes. Figure 5A-26 Los Angeles Base Model, Model with WWR 60% and Model with WWR 20% EUI Comparison 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 12 13 11 0.8 1 0.5 8.4 9 7.8 0 5 10 15 20 25 30 35 40 45 50 Base Model WWR 60% WWR 20% EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 292 Comparing to the base model, model with WWR 60%, cooling EUI increased by 1 kBtu/sf, heating EUI increased by 0.2 kBtu/sf and fan power increased by 0.6 kBtu/sf; model with WWR 20%, cooling EUI reduced by 1 kBtu/sf, heating EUI reduced by 0.3 kBtu/sf and fan power increased by 0.6 kBtu/sf. The results represented that model with higher WWR consumed more energy in break down EUI of cooling, heating, and fan power, and vice versa. WWR could have an effect on the building solar heat gain and the externa l conduction because of the area of window. Comparing to the wall, window transmits more sunlight into the indoor space and resist less heat transferring between the indoor and outdoor space, so more area of window resulted in more penetration of sunlight and more heat transferred through the building. The graphs included solar gain and external conduction on the day July 1 st in summer were utilized to analyze the energy consumption difference, as the solar heat gain and heat exchange are the key effects result from WWR (Figure 5A-27 & Figure 5A-28 & Figure 5A-29). 293 Figure 5A-27 Los Angeles WWR 60% July 1 st External Conduction and Solar Gain 294 Figure 5A-28 Los Angeles Base Model WWR 40% July 1 st External Conduction and Solar Gain 295 Figure 5A-29 Los Angeles WWR 20% July 1 st External Conduction and Solar Gain Graphs showed that models with lower WWR had less solar heat gain. The two peak points of WWR 20% model were around 60,000 Btu/h while base model was 130,000 Btu/h and the WWR 60% model was 190,000 Btu/h (Figure 5A-27 & Figure 5A-28 & Figure 5A-29). Not only the peak points, the total solar heat gain also increased as the WWR increased from 20% to 60%. Less solar heat gain results in less cooling load needed to condition the space, especially for the cities like Los Angeles, dry-bulb temperature is higher than the cooling setpoint through much of the year. Graphs also showed that model with lower WWR resulted in the less heat loss. The peak point of WWR 20% model were around 138,000 Btu/h while base model was 148,000 Btu/h and WWR 60% model was 151,000 (Figure 5A-27 & Figure 5A-28 & Figure 5A-29). There was slight 296 difference between the models with different WWR, thus the effect of WWR on heat loss was not that evident. 5A.2.4.1WWR Summary The EUI bar chart comparison of the base model, model with WWR 60% and model with WWR 20% is beneficial to analyzing effect of WWR (Figure 5A-30). Figure 5A-30 Los Angeles WWR EUI Comparison Bar Chart Comparing to the base model, the total EUI of model with WWR 60% was increased by 1.8 kBtu/sf (4.3%); the total EUI of model with WWR 20% was reduced by 1.9 kBtu/sf (4.6%). The results showed that WWR could affect building energy consumption. 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 12 13 11 0.8 1 0.5 8.4 9 7.8 0 5 10 15 20 25 30 35 40 45 50 Base Model WWR 60% WWR 20% EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 4.3% -4.6% 297 5A.2.5 Analysis of Window SHGC on the Model The SHGC of the base model was set as 0.22. The SHGC was set as 0.18 and 0.12 to get the corresponding result of cooling, heating, fan power energy use intensity and EUI. The EUI stacked bar chart comparison of the base model, the model with window SHGC 0.18 and the model window SHGC 0.12 are beneficial to analyzing effect of window SHGC (Figure 5A- 31). The difference of total EUI and break down EUI was easy to compare and see the changes. Figure 5A-31 Los Angeles Base Model, model with window SHGC 0.18 and model with window SHGC 0.12 EUI Comparison Comparing to the base model, model with window SHGC 0.18, fan power EUI reduced by 0.4 kBtu/sf, heating EUI were the same, cooling was reduced by 0.6 kBtu/sf; model with window SHGC 0.12, fan power increased by 0.2 kBtu/sf, cooling EUI reduced by 1.1 kBtu/sf and heating EUI increased by 0.1 kBtu/sf (Figure 5A-30). Total EUI was reduced by 1.0 kBtu/sf and 1.6 kBtu/sf correspondingly. The results represented that model with higher window SHGC consumed more energy and vice versa. 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 12 11.4 10.9 0.8 0.8 0.9 8.4 8 7.8 0 5 10 15 20 25 30 35 40 45 Base Model SHGC 0.18 SHGC 0.12 EUI Comparisom (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 298 Window SHGC represents the amount of solar heat that transmitted through the window as heat. Lower SHGC is more desirable for the building required more cooling load, as it reduces the amount of solar heat transmitted in the room. The graphs included solar gain on the day July 1 st in summer were utilized to analyze the energy consumption difference, as the solar heat gain is the key effect result from Window SHGC (Figure 5A-32 & Figure 5A-33 & Figure 5A-34). Figure 5A-32 Los Angeles Base Model SHGC 0.22 July 1 st Solar Gain 299 Figure 5A-33 Los Angeles Window SHGC 0.18 July 1 st Solar Gain 300 Figure 5A-34 Los Angeles Window SHGC 0.12 July 1 st Solar Gain Graphs showed that model with lower window SHGC had less solar heat gain. The two peak points of the base model were around 130,000 Btu/h while model with window SHGC 0.18 were 120,000 Btu/h and 125,000 and model with window SHGC were around 80,000 Btu/h (Figure 5A-32 & Figure 5A-33 & Figure 5A-34). Not only the peak points, the total amount solar heat gain (reflected in Figure 5A-32 & Figure 5A-33 & Figure 5A-34) also increased as the window SHGC increased from 0.22 to 0.18. Less solar heat gain results in less cooling load needed to condition the space, especially for the cities like Los Angeles, where dry-bulb temperature is higher than the cooling setpoint through most of the year. 301 5A.2.5.1 Window SHGC Summary The EUI bar chart comparison of the base model, model with window SHGC 0.18 and model with window SHGC 0.12 is beneficial to analyzing the effect of window SHGC (Figure 5A-35). Figure 5A-35 Los Angeles Window SHGC EUI Comparison Bar Chart Comparing to the base model, the total EUI of model with window SHGC 0.18 was reduced by 1.0 kBtu/sf (2.0%); the total EUI of model with window SHGC 0.12 was reduced by 1.6 kBtu/sf (4.0%). The results showed that building total EUI could be reduced as much as 4.0% when window SHGC was changed from 0.22 to 0.12. 5A.2.6 Analysis of VT on the Model The visible transmittance of the base model was set as 0.32. The glazing VT was set as 0.43 and 0.55 to get the corresponding result of cooling, heating, fan power energy use intensity and EUI. The EUI stacked bar chart comparison of the base model, the model with window VT 0.43 and the model with window VT 0.55 are beneficial to analyzing effect of window VT (Figure 5A-36). 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 12 11.4 10.9 0.8 0.8 0.9 8.4 8 7.8 0 5 10 15 20 25 30 35 40 45 Base Model SHGC 0.18 SHGC 0.12 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -0.2% -0.4% 302 When comparing different Visible Transmissivities and holding the SHGC constant, the primary effect is the change in daylighint and resusltant lighting loads. The difference of total EUI and break down EUI was easy to compare and see the changes. Figure 5A-36 Los Angeles Base Model, model with window VT 0.43 and model with window VT 0.55 EUI Comparison Comparing to the base model, model with window VT 0.43, and model with window VT 0.55 have the exact same total EUI results, including all the EUI break down results. It showed that in this model, the window VT did not affect the building energy efficiency at all. Window VT refers to the percentage of visible portion of sunlight that transmitted into the room through the window. The graphs included internal gain, solar gain, external conduction gain and infiltration gain on the day July 1 st and December 1 st were utilized to analyze the energy consumption difference (Figure 5A-37 & Figure 5A-38). 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 12 12 12 0.8 0.8 0.8 8.4 8.4 8.4 0 5 10 15 20 25 30 35 40 45 Base Model VT 0.43 VT 0.55 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 303 Figure 5A-37 Base Model VT July 1 st External conduction, Internal Gain, Solar Gain, and Infiltration gain 304 Figure 5A-38 Base Model VT December 1 st External conduction, Internal Gain, Solar Gain, and Infiltration gain Internal gain, solar gain, external conduction gain and infiltration gain are the four main energy gains that affecting the building energy performance. The graphs showed how they varied in the certain day (July 1 st and December 1 st ). The graphs of the base model, model with window VT 0.43 and model with window VT 0.55 were the same which represented that window VT had no influence on the model energy consumption. 5A.2.6.1 Glazing VT Summary The EUI bar chart comparison of the base model, model with glazing VT 0.43 and model with glazing VT 0.55 is beneficial to analyzing effect of glazing VT (Figure 5A-39). 305 Figure 5A-39 Los Angeles Glazing VT EUI Comparison Bar Chart Comparing to the base model, the total EUI of model with glazing VT 0.43 and model with glazing VT 0.55 was the same. The results showed that glazing VT did not affect the energy performance of this model. Glazing VT could affect the sunlight penetration, which is effective on the indoor lighting load, However, glazing VT was not an effective study as the lighting load was not considered with it. 5A.2.7 Analysis of Building Orientation on the Model The long side of the base model orientation was set as facing north. The orientation was set as Northwest (45 degree), West (90 degree) and Southwest (135 degree) to get the corresponding result of cooling, heating, fan power energy use intensity and EUI. The EUI stacked bar chart comparison of base model, the model rotated 45 degree, the model rotated 90 degree, and the model rotated 135 degree are beneficial to analyzing effect of building orientation (Figure 5A-40). The results of cooling, heating, fan power energy use intensity and 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 12 12 12 0.8 0.8 0.8 8.4 8.4 8.4 0 5 10 15 20 25 30 35 40 45 Base Model VT 0.43 VT 0.55 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 0.0% 0.0% 306 total EUI were derived and included to conduct the analysis of how orientation affects building energy performance. Figure 5A-40 Los Angeles Model Rotating 45°, Base Model, Model Rotating 90° and Model Rotating 135° Total EUI and break down EUI of model with orientation 90 degree was the same to the base model; comparing to the base model, total EUI of model with orientation 135 degree was reduced by 0.1 kBtu/sf, which was reflected in the reduction of heating; total EUI of model with orientation 45 degree was increased by 0.1 kBtu/sf in fan power EUI break down (Figure 5A-40). The building orientation could affect the building cooling and heating load by affecting the solar heat gain. However, the floor plan of the model is symmetrical and the long side is not very different from the short side of the building. In addition, the WWR of each suface of the model is 6.7 6.7 6.7 6.7 8 8 8 8 1.7 1.7 1.7 1.7 3.8 3.8 3.8 3.8 12 12 12 12 0.8 0.8 0.7 0.7 8.4 8.5 8.5 8.4 0 5 10 15 20 25 30 35 40 45 Base Model 45 Degree 90 Degree 135 Degree EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 307 the same which is 40%. Thus, rotating the model to change the solar penertration did not have siginificant influence on building energy consumption. 5A.2.7.1 Model Orientation Summary The EUI bar chart comparison of the base model, model rotating 45 degree, model rotating 90 degree and 135 degree, is beneficial to analyzing effect of window SHGC (Figure 5A-41). Figure 5A-41 Los Angeles Model Orientation EUI Comparison Bar Chart Comparing to the base model, the total EUI of model 45 degree was increased by 0.1 kBtu/sf (0.2%); the total EUI of model 90 degree was the same; the total EUI of model 45 degree was reduced by 0.1 kBtu/sf (0.2%). The results showed that building total EUI could be by the model orientation, however, the influence is slight. 5A.4 Los Angeles Summary Seven passive design strategies were applied to the base model in Los Angeles. The results showed that passive design strategies could affect building energy consumption, but some of them could 6.7 6.7 6.7 6.7 8 8 8 8 1.7 1.7 1.7 1.7 3.8 3.8 3.8 3.8 12 12 12 12 0.8 0.8 0.7 0.7 8.4 8.5 8.5 8.4 0 5 10 15 20 25 30 35 40 45 Base Model 45 Degree 90 Degree 135 Degree EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 0.1% -0.2% 0.0% 308 only have slight or no influence on the base model. The strategies were listed in order of the energy savings (Table 5A-1) (Figure 5A-42). Table 5A-1 Los Angeles Strategies Energy Efficiency Importance Ranking Base Model – Los Angeles Final results – Los Angeles Savings and Percentage 1 WWR (40%) WWR (20%) 1.9 kBtu/sf (4.6%) 2 Window SHGC (0.22) Glazing SHGC (0.12) 1.6 kBtu/sf (4.0%) 3 Window (U=0.46 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) 0.3 kBtu/sf (0.7%) 4 Wall Insulation (U=0.069 Btu/h ft 2 F) Window U-factor (U=0.3 Btu/h ft 2 F) 0.3 kBtu/sf (0.7%) 5 Roof Insulation (U=0.049 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) 0.1 kBtu/sf (0.2%) 6 Orientation (Long side facing north) Orientation (Long side facing Northwest and Southwest) 0.1 kBtu/sf (0.2%) 7 Glazing VT (0.55) Glazing VT (0.55) 0 kBtu/sf (0.0%) 309 Figure 5A-42 Los Angeles Model Best Strategies EUI Comparison Bar Chart For lighting (represented by blue color in stacked bar chart), equipment (orange), DHW (grey), and pump (yellow), they were consumed to have consistent value (use hand calculation in spreadsheet) in the simulation. The calculation was shown in chapter 4: The lighting use density: 6.7 kBtu/ft 2 The computer use density: 8.0 kBtu/ft 2 The DHW use density:1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 Thus, the models applied with different design strategies all had the same EUI break down on lighting (blue), equipment (orange), DHW (grey) and pump (yellow). 6.7 6.7 6.7 6.7 6.7 6.7 6.7 6.7 8 8 8 8 8 8 8 8 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 12 11 10.9 11.9 12 12 12 12 0.8 0.5 0.9 0.7 0.6 0.7 0.7 0.8 8.4 7.8 7.8 8.3 8.3 8.4 8.4 8.4 0 5 10 15 20 25 30 35 40 45 Base Model WWR 20% SHGC 0.12 Wall U=0.016 Btu/h ft2 F Window U=0.30 Btu/h ft2 F Roof U=0.019 Btu/h ft2 F Orientation 135° VT 0.55 Strategies EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 0.0% 0.2% 0.2% 0.7% 0.7% 4.0% 4.6% 310 The cooling load (light blue), heating load (green) and fan power (dark blue) were derived from VistaPro in IES VE and calculate in the spreadsheet (mentioned in chapter 4). Thus, the cooling load (light blue), heating load (green) and fan power (dark blue) were changing the model energy consumption. In addition, the change of total EUI was depending on the change of them. 1. WWR was the most efficient strategy that applied to the model as it affected the amount of solar heat gain which was quite important to the location with sufficient sunlight. Lower WWR could reduce the amount of sunlight penetration, so as to the solar heat gain of building in both summer and winter. Thus, the cooling load, heating load and fan power were reduced. 2. Lower window SHGC resulted in less solar heat going through the window, so less solar heat in summer could contribute to the savings on cooling, but in winter resulted in more heating to warm the indoor space. 3. Window U-value, wall insulation U-value and roof insulation U-value are the measurement of heat transferred through the material. Lower U-value resulted less heat transferred through window, wall, and roof. However, in Los Angeles, the difference between indoor air temperature and dry-bulb temperature is not that big throughout the year. Thus, the heat flow through building envelop would be a lot, which contributed to the U-value of wall, window and roof could only had slight effect on building energy consumption. 4. Because of the symmetrical floor plan and similar length of long side and short side of building, the orientation of the model only had slight effect on model EUI. 5.VT had no effect on the model energy consumption, which was indicated by the results of simulation, as it only determined the proportion of visible sunlight transmitted in the room. In conclusion, the influence of wall and roof insulation was slight on the energy performance of model when a wide range of U-value were applied because of the Los Angeles hot climate throughout the year. For the four window related strategies, U-value of window had similar 311 influence as the wall and roof insulation; WWR was the most efficient strategy that applied to the model as it affected the amount of solar heat gain which was quite important to the location with sufficient sunlight; glazing SHGC was effective to the model energy consumption, but not as good as WWR; VT had no effect on the model energy consumption, which was indicated by the results of simulation, as it only determined the proportion of visible sunlight transmitted in the room. Because of the symmetrical floor plan and similar length of long side and short side of building, the orientation of the model only had slight effect on model EUI. As the different weather conditions, functions of buildings, methods of research and so on, the strategies could have different effect on building energy efficiency (Table 5A-2). 312 Table 5A-2 Building Energy Efficiency Importance Ranking Final results – Los Angeles Building Designer Ranking Regression Ranking Decision Tree Ranking Citation (Khan, 2021) (Khan, 2021) (Khan, 2021) 1 WWR (20%) HVAC System Cooling System Occupancy Density 2 Glazing SHGC (0.12) Occupancy Density Glass Type Floor Height 3 Wall Insulation (U=0.016 Btu/h ft 2 F) WWR Heating System Heating System 4 Window U- factor (U=0.3 Btu/h ft 2 F) Roof Material Shading Cooling System 5 Roof Insulation (U=0.019 Btu/h ft 2 F) Wall Material Orientation WWR 6 Orientation (Long side facing Northwest, Southwest) Window Glass Type Wall Insulation Stories 7 Glazing VT (0.55) Orientation Rise_N (Low/High) Shading 8 Shading Window Type Wall Material 9 Window Frame Type Occupancy Density Window Glass Type 10 Stories Roof Material Ceiling Height 11 Floor Height Building Footprint Roof Material 12 Ceiling Height Orientation 313 13 WWR Window Frame Type 14 Floor Area Size 314 Chapter 5B. Harbin Base Model Analysis The analysis of the base model and models with passive and active design strategies is based on the EUI of models, the graphs of which were showed in the chapter 4 and will be showed again in this chapter. This chapter describes the base model analysis, analysis of model applied with passive design strategies and analysis of model applied with passive design strategies in Harbin. 5B.1 Base Model Analysis The lighting energy consumption, computer consumption, DHW consumption and pump consumption were calculated with the certain energy intensity which were based on the California building energy codes. Thus, it was assumed that the amount of energy consumption of them were not affected by application of passive and active strategies. Only the energy consumption for cooling, heating and ventilation were considered as energy break down that were affected by the design strategies. The total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 7.8 kBtu/ft 2 + 9.7 kBtu/ft 2 + 7.7 kBtu/ft 2 = 45.4 kBtu/ft 2 (Figure 5B- 1). 315 Figure 5B-1 Harbin Base Model EUI Stacked Bar Chart Ventilation, cooling, and heating load of the building are highly depending on the local weather condition. The dry-bulb temperature varies significantly through the year, which could be as high as 81 °Fin summer and as low as -10 °F in winter (Figure 5B-2). 6.7 8 1.7 3.8 7.8 9.7 7.7 0 5 10 15 20 25 30 35 40 45 50 Base Model EUI (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 316 Figure 5B-2 Average High and Low Temperature in Harbin (Weather Spark, 2022) Thus, both cooling and heating load are high through the year. Also, heating load is higher than the cooling load which is different from the break down EUI of model in Los Angeles. In addition, the base model, an office building, is an internal gain dominant model, because of the large amount of internal heat gain during the working time. Internal heat gain includes lighting heat gain, computers heat gain and people heat gain in this model and their values of sensible gain and latent gain were mentioned in chapter 3, which correspondingly maximum sensible gain 0.9W/ft 2 , maximum sensible gain 1W/ft 2 , maximum sensible gain 307.093 Btu/h/person and maximum latent gain 204.728 Btu/h/person. Cooling load increased because of those internal heat gain form 8 am in the morning to 6 pm in the evening to keep the indoor air temperature at setpoint 74°F. The break down EUI of the base model heating energy consumption is 9.7 kBtu/ft 2 was contributed by the four-month hot summer and the internal gains. Cooling break down EUI is 7.8 kBtu/ft 2 was resulted from the low temperature in winter. 317 Fan power is the energy consumption that consists of ventilation for cooling air flow, ventilation for heating air flow and minimum ventilation air flow. Thus, the ventilation for cooling and heating air flow are highly depending on the cooling and heating load of model. As mentioned in chapter 4, the minimum ventilation rate is 0.15 cfm/ft 2 and the total area of the base model is 100,000 ft 2 , so the total minimum ventilation air flow for the base model is 15,000 cfm. For the time HVAC system not providing cooling and heating or the ventilation air flow is lower than 15,000 cfm, the system should provide air ventilation to keep minimum air flow at 15,000 cfm. 5B.2 Analysis of Model Applied with Passive Design Strategies To test the efficiency of passive design strategies, nine passive controls were chosen and applied to the base model, exterior wall insulation, roof insulation, window u-value, WWR, window SHGC, VT, overhang, natural ventilation, and orientation. In addition, the result of an integration of all the effective passive design strategies was analyzed. 5B.2.1 Analysis of Wall Insulation on the Model The exterior wall insulation U-value of the base model was set as U=0.069 Btu/h ft 2 F. The U- value of the two test cases was set as U=0.12 Btu/h ft 2 F and U=0.016 Btu/h ft 2 F to get the corresponding result of cooling, heating, fan power energy use intensity and EUI. The graphs were chosen on two days of a year, July 1 st in hot summer and December 1 st , as the representative days. They included indoor air temperature, dry-bulb temperature, and external conduction were utilized to analyze the energy consumption difference, as the heat exchange between indoor and outdoor environment is the key effect result from insulation (Figure 5B-3 & Figure 5B-4). 318 Figure 5B-3 Harbin Base Model December 1 st External Conduction, Indoor and Outdoor Temperature 319 Figure 5B-4 Harbin Base Model July 1 st External Conduction, Indoor and Outdoor Temperature 5B.2.1.1 Exterior Wall Insulation U=0.016 Btu/h ft 2 F The EUI stacked bar chart comparison of the base model and model with U=0.016 Btu/h ft 2 F wall insulation is beneficial to analyzing effect of wall insulation (Figure 5B-5). The difference of total EUI and break down EUI was easy to compare and see the changes. 320 Figure 5B-5 Harbin Base Model and Wall Insulation U=0.016 Btu/h ft 2 F EUI Comparison After replacing the wall insulation with U=0.016 Btu/h ft 2 F, total EUI was reduced by 0.8 kBtu/sf. Break down EUI cooling was increased by 0.1 kBtu/sf, heating was reduced by 0.8 kBtu/sf, and fan power was reduced by 0.1 kBtu/sf. The graphs included indoor air temperature, dry-bulb temperature, and external conduction (on two days of a year, July 1 st in hot summer and December 1 st ) were utilized to analyze the energy consumption difference, as the heat exchange between indoor and outdoor environment is the key effect result from insulation (Figure 5B-6 & Figure 5B-7). 6.7 6.7 8 8 1.7 1.7 3.8 3.8 7.8 7.9 9.7 8.9 7.7 7.6 0 5 10 15 20 25 30 35 40 45 50 Base Model Wall U=0.016 Btu/h ft2 F EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 321 Figure 5B-6 Harbin Wall Insulation U=0.016 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature 322 Figure 5B-7 Harbin Wall Insulation U=0.016 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature The graph was utilized to analyze the relation of wall U-value and space external conduction (Figure 5B-6 & Figure 5B-7). In the graphs, indoor air temperature and dry-bulb temperature were the same as they represented the results of the same day (December 1st) the base model while the external conduction gains were different because of the varied wall insulation. Indoor air temperature started to rise at 8:00 am when the HVAC system started working (set in the profile) until it got the setpoint (74 °F) and kept it constant until people left work at 6:00pm. Dry-bulb temperature was the outdoor temperature of December 1st of a typical year, low at morning and night, went high during the daytime and got the peak point around 2:00 pm. Heat loss ranging as 323 the indoor temperature and dry-bulb temperature vary along the daytime. The graph of July 1 st is different from the graph of December 1 st as the significantly different dry-bulb temperature in summer and winter. The difference of external conduction gains of U=0.069 Btu/h ft 2 F and U=0.016 Btu/h ft 2 F showed the effect of wall insulation on building energy consumption. The shapes of them were similar, but for the graph with lower U-value, the peak point and the whole value through the day were lower which represented the less external conduction, especially in winter. More heat loss from the base model comparing to model with wall insulation U=0.016 Btu/h ft 2 F could be detected by the graphs which showing more external conduction (with negative numbers). Less heat transferred to outdoor space from indoor space in the model with wall insulation U=0.016 Btu/h ft 2 F proved that lower U-value could reduce heat conduction. 5B.2.1.2 Exterior Wall Insulation U=0.12 Btu/h ft 2 F The EUI stacked bar chart comparison of the base model and model with U=0.12 Btu/h ft 2 F wall insulation is beneficial to analyzing effect of wall insulation (Figure 5B-8). The difference of total EUI and break down EUI was easy to compare and see the changes. 324 Figure 5B-8 Harbin Base Model and Wall Insulation U=0.12 Btu/h ft 2 F EUI Comparison After replacing the wall insulation with U=0.12 Btu/h ft 2 F, model total EUI was increased by 0.7 kBtu/sf. Break down EUI cooling was reduced by 0.1 kBtu/sf, heating was increased by 0.8 kBtu/sf, and fan power was the same. The graphs included indoor air temperature, dry-bulb temperature, and external conduction (on two days of a year, July 1 st in hot summer and December 1 st ) were utilized to analyze the energy consumption difference, as the heat exchange between indoor and outdoor environment is the key effect result from insulation (Figure 5B-9 & Figure 5B-10). 6.7 6.7 8 8 1.7 1.7 3.8 3.8 7.8 7.7 9.7 10.5 7.7 7.7 0 5 10 15 20 25 30 35 40 45 50 Base Model Wall U=0.12 Btu/h ft2 F EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 325 Figure 5B-9 Harbin Wall Insulation U=0.12 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature 326 Figure 5B-10 Harbin Wall Insulation U=0.12 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature The graph was utilized to analyze the relation of wall U-value and space external conduction (Figure 5B-9 & Figure 5B-10). In the graph, indoor air temperature and dry-bulb temperature were the same as they represented the results of the same day (December 1st) the base model while the external conduction gains were different because of the varied wall insulation. Indoor air temperature started to rise at 8:00 am when the HVAC system started working (set in the profile) until it got the setpoint (74 °F) and kept it constant until people left work at 6:00 pm. Dry-bulb temperature was the outdoor temperature of December 1st of a typical year, low at morning and night, went high during the daytime and got the peak point around 2:00 pm. Heat loss ranging as the indoor temperature and dry-bulb temperature vary along the daytime. The graph of July 1 st is 327 different from the graph of December 1 st as the significantly different dry-bulb temperature in summer and winter. The difference of external conduction gains of U=0.069 Btu/h ft 2 F and U=0.12 Btu/h ft 2 F showed the effect of wall insulation on building energy consumption. The shapes of them were similar, but for the graph with lower U-value, the peak point and the whole value through the day were lower which represented the less external conduction, especially in winter. Less heat loss from the base model comparing to model with wall insulation U=0.12 Btu/h ft 2 F could be detected by the graphs which showing more external conduction (with negative numbers). More heat transferred to outdoor space from indoor space in the model with wall insulation U=0.12 Btu/h ft 2 F proved that lower U-value could reduce heat conduction. 5B.2.1.3 Wall Insulation Summary The EUI bar chart comparison of the base model, model with U=0.016 Btu/h ft 2 F wall insulation and model with U=0.12 Btu/h ft 2 F wall insulation is beneficial to analyzing effect of wall insulation (Figure 5B-11). 328 ` Figure 5B-11 Harbin Wall Insulation EUI Comparison Bar Chart Comparing to the base model, the total EUI of model with U=0.016 Btu/h ft 2 F wall insulation was reduced by 0.8 kBtu/sf (1.8%); the total EUI of model with U=0.12 Btu/h ft 2 F wall insulation was increased by 0.7 kBtu/sf (1.5%). The results showed that wall insulation could affect building energy consumption. 5B.2.2 Analysis of Roof Insulation on the Model The roof insulation U-value of the base model was set as U=0.049 Btu/h ft 2 F. The U-value was set as U=0.019 Btu/h ft 2 F and U=0.079 Btu/h ft 2 F to get the corresponding result of cooling, heating, fan power energy use intensity and EUI. 5B.2.2.1 Roof Insulation U=0.019 Btu/h ft 2 F The EUI stacked bar chart comparison of the base model and model with U=0.016 Btu/h ft 2 F roof insulation is beneficial to analyzing effect of roof insulation (Figure 5B-12). The difference of total EUI and break down EUI was easy to compare and see the changes. 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 7.8 7.9 7.7 9.7 8.9 10.5 7.7 7.6 7.7 0 5 10 15 20 25 30 35 40 45 50 Base Model Wall U=0.016 Btu/h ft2 F Wall U=0.12 Btu/h ft2 F EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -1.8% 1.5% 329 Figure 5B-12 Harbin Base Model and Roof Insulation U=0.019 Btu/h ft 2 F EUI Comparison After replacing the roof insulation with U=0.019 Btu/h ft 2 F, total EUI was reduced by 0.6 kBtu/sf. Model break down EUI cooling was increased by 0.2 kBtu/sf, fan power was reduced by 0.1 kBtu/sf, and heating was reduced 0.7 kBtu/sf. The graphs included indoor air temperature, dry-bulb temperature, and external conduction (on two days of a year, July 1 st in hot summer and December 1 st ) were utilized to analyze the energy consumption difference, as the heat exchange between indoor and outdoor environment is the key effect result from insulation (Figure 5B-13 & Figure 5B-14). 6.7 6.7 8 8 1.7 1.7 3.8 3.8 7.8 8 9.7 9 7.7 7.6 0 5 10 15 20 25 30 35 40 45 50 Base Model Roof U=0.019 Btu/h ft2 F EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 330 Figure 5B-13 Harbin Roof Insulation U=0.019 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature 331 Figure 5B-14 Harbin Roof Insulation U=0.019 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature The graph was utilized to analyze the relation of roof U-value and space external conduction (Figure 5B-13 & Figure 5B-14). In the graphs, indoor air temperature and dry-bulb temperature were the same as they represented the results of the same day (December 1st) the base model while the external conduction gains were different because of the varied roof insulation. Indoor air temperature started to rise at 8:00 am when the HVAC system started working (set in the profile) until it got the setpoint (74 °F) and kept it constant until people left work at 6:00pm. Dry-bulb temperature was the outdoor temperature of December 1st of a typical year, low at morning and night, went high during the daytime and got the peak point around 2:00 pm. Heat loss ranging as 332 the indoor temperature and dry-bulb temperature vary along the daytime. The graph of July 1 st is different from the graph of December 1 st as the significantly different dry-bulb temperature in summer and winter. The difference of external conduction gains of U=0.049 Btu/h ft 2 F and U=0.019 Btu/h ft 2 F showed the effect of roof insulation on building energy consumption. The shapes of them were similar, but for the graph with lower U-value, the peak point and the whole value through the day were lower which represented the less external conduction. More heat loss from the base model comparing to model with wall insulation U=0.019 Btu/h ft 2 F could be detected by the graphs which showing more external conduction (with negative numbers). Less heat transferred to outdoor space from indoor space in the model with wall insulation U=0.019 Btu/h ft 2 F proved that lower U-value could reduce heat conduction. 5B.2.2.2 Roof Insulation U=0.079 Btu/h ft 2 F The EUI stacked bar chart comparison of the base model and model with U=0.079 Btu/h ft 2 F roof insulation is beneficial to analyzing effect of roof insulation (Figure 5B-15). The difference of total EUI and break down EUI was easy to compare and see the changes. 333 Figure 5B-15 Harbin Base Model and Roof Insulation U=0.079 Btu/h ft 2 F EUI Comparison After replacing the wall insulation with U=0.079 Btu/h ft 2 F, total EUI was increased by 2.6 kBtu/sf. Model break down cooling EUI was increased by 0.9 kBtu/sf, heating was increased by 0.6 kBtu/sf and fan power was the same. The graphs included indoor air temperature, dry-bulb temperature, and external conduction (on two days of a year, July 1 st in hot summer and December 1 st ) were utilized to analyze the energy consumption difference, as the heat exchange between indoor and outdoor environment is the key effect result from insulation (Figure 5B-16 & Figure 5B-17). 6.7 6.7 8 8 1.7 1.7 3.8 3.8 7.8 9.7 9.7 10.4 7.7 7.7 0 5 10 15 20 25 30 35 40 45 50 Base Model Roof U=0.079 Btu/h ft2 F EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 334 Figure 5B-16 Harbin Roof Insulation U=0.079 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature 335 Figure 5B-17 Harbin Roof Insulation U=0.079 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature The graph was utilized to analyze the relation of roof U-value and space external conduction (Figure 5B-16 & Figure 5B-17). In the graphs, indoor air temperature and dry-bulb temperature were the same as they represented the results of the same day (December 1st) the base model while the external conduction gains were different because of the varied roof insulation. Indoor air temperature started to rise at 8:00 am when the HVAC system started working (set in the profile) until it got the setpoint (74 °F) and kept it constant until people left work at 6:00 pm. Dry-bulb temperature was the outdoor temperature of December 1st of a typical year, low at morning and night, went high during the daytime and got the peak point around 2:00 pm. Heat loss ranging as the indoor temperature and dry-bulb temperature vary along the daytime. The graph of July 1 st is 336 different from the graph of December 1 st as the significantly different dry-bulb temperature in summer and winter. The difference of external conduction gains of U=0.049 Btu/h ft 2 F and U=0.079 Btu/h ft 2 F showed the effect of roof insulation on building energy consumption. The shapes of them were similar, but for the graph with lower U-value, the peak point and the whole value through the day were lower which represented the less external conduction. More heat loss from model with wall insulation U=0.079 Btu/h ft 2 F comparing to the base model could be detected by the graphs which showing more external conduction (with negative numbers). Less heat transferred to outdoor space from indoor space in the model with base model proved that lower U-value could reduce heat conduction. 5B.2.2.3 Roof Insulation Summary The EUI bar chart comparison of the base model, model with U=0.019 Btu/h ft 2 F roof insulation and model with U=0.12 Btu/h ft 2 F roof insulation is beneficial to analyzing effect of roof insulation (Figure 5B-18). 337 Figure 5B-18 Harbin Roof Insulation EUI Comparison Bar Chart Comparing to the base model, the total EUI of model with U=0.019 Btu/h ft 2 F roof insulation was reduced by 0.6 kBtu/sf (1.3%); the total EUI of model with U=0.79 Btu/h ft 2 F roof insulation was increased by 2.6 kBtu/sf (5.7%). The results showed that roof insulation could affect building energy consumption, and the higher the U-value insulation applied to the model, the more effective it was. 5B.2.3 Analysis of Window U-value on the Model The window U-value of the base model was set as U=0.46 Btu/h ft 2 F. The U-value was set as U=0.38 Btu/h ft 2 F and U=0.30 Btu/h ft 2 F to get the corresponding result of cooling, heating, fan power energy use intensity and EUI. 5B.2.3.1 Window U=0.38 Btu/h ft 2 F The EUI stacked bar chart comparison of the base model and model with window U=0.38 Btu/h ft 2 F is beneficial to analyzing effect of window U-factor (Figure 5B-19). The difference of total EUI and break down EUI was easy to compare and see the changes. 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 7.8 8 9.7 9.7 9 10.4 7.7 7.6 7.7 0 5 10 15 20 25 30 35 40 45 50 Base Model Roof U=0.019 Btu/h ft2 F Roof U=0.079 Btu/h ft2 F EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -1.3% 5.7% 338 Figure 5B-19 Harbin Base Model and Window U=0.38 Btu/h ft 2 F EUI Comparison After replacing the window with U=0.38 Btu/h ft 2 F, model cooling was the same, the total EUI was reduced by 0.6 kBtu/sf. Model EUI break down cooling was increased by 0.2 kBtu/sf, heating was reduced by 0.7 kBtu/sf, and fan power was reduced by 0.1 kBtu/sf. The graphs included indoor air temperature, dry-bulb temperature, and external conduction (on two days of a year, July 1 st in hot summer and December 1 st ) were utilized to analyze the energy consumption difference, as the heat exchange between indoor and outdoor environment is the key effect result from window U-value (Figure 5B-20 & Figure 5B-21). 6.7 6.7 8 8 1.7 1.7 3.8 3.8 7.8 8 9.7 9 7.7 7.6 0 5 10 15 20 25 30 35 40 45 50 Base Model Window U=0.38 Btu/h ft2 F EUI Comparision (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 339 Figure 5B-20 Harbin Window U=0.38 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature 340 Figure 5B-21 Harbin Window U=0.38 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature The graph was utilized to analyze the relation of window U-value and space external conduction (Figure 5B-20 & Figure 5B-21). In the graphs, indoor air temperature and dry-bulb temperature were the same as they represented the results of the same day (December 1st) the base model while the external conduction gains were different because of the varied window U-value. Indoor air temperature started to rise at 8:00 am when the HVAC system started working (set in the profile) until it got the setpoint (74 °F) and kept it constant until people left work at 6:00pm. Dry-bulb temperature was the outdoor temperature of December 1st of a typical year, low at morning and night, went high during the daytime and got the peak point around 2:00 pm. Heat loss ranging as the indoor temperature and dry-bulb temperature vary along the daytime. The graph of July 1 st is 341 different from the graph of December 1 st as the significantly different dry-bulb temperature in summer and winter. The difference of external conduction gains of U=0.46 Btu/h ft 2 F and U=0.38 Btu/h ft 2 F showed the effect of window U-value on building energy consumption. The shapes of them were similar, but for the graph with lower U-value, the peak point and the whole value through the day were lower which represented the less external conduction. More heat loss from the base model comparing to model with window U=0.49 Btu/h ft 2 F could be detected by the graphs which showing more external conduction (with negative numbers). Less heat transferred to outdoor space from indoor space in the model with window U=0.38 Btu/h ft 2 F proved that lower U-value could reduce heat conduction. 5B.2.2.2 Window U=0.30 Btu/h ft 2 F The EUI stacked bar chart comparison of the base model and model with window U=0.30 Btu/h ft 2 F is beneficial to analyzing effect of window U-value (Figure 5B-22). The difference of total EUI and break down EUI was easy to compare and see the changes. 342 Figure 5B-22 Harbin Base Model and Window U=0.30 EUI Comparison After replacing the window with U=0.30 Btu/h ft 2 F, total EUI was reduced by 1.1 kBtu/sf. Model breakdown EUI cooling was increased by 0.4 kBtu/sf, heating was reduced by 1.4 kBtu/sf, and fun power was reduced by 0.1 kBtu/sf. The graphs included indoor air temperature, dry-bulb temperature, and external conduction (on two days of a year, July 1 st in hot summer and December 1 st ) were utilized to analyze the energy consumption difference, as the heat exchange between indoor and outdoor environment is the key effect result from insulation (Figure 5B-23 & Figure 5B-24). 6.7 6.7 8 8 1.7 1.7 3.8 3.8 7.8 8.2 9.7 8.3 7.7 7.6 0 5 10 15 20 25 30 35 40 45 50 Base Model Window U=0.3 Btu/h ft2 F EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 343 Figure 5B-23 Harbin Window U=0.30 Btu/h ft 2 F December 1 st External Conduction, Indoor and Outdoor Temperature 344 Figure 5B-24 Harbin Window U=0.30 Btu/h ft 2 F July 1 st External Conduction, Indoor and Outdoor Temperature The graph was utilized to analyze the relation of roof U-value and space external conduction (Figure 5B-23 & Figure 5B-24). In the graphs, indoor air temperature and dry-bulb temperature were the same as they represented the results of the same day (December 1st) the base model while the external conduction gains were different because of the varied roof insulation. Indoor air temperature started to rise at 8:00 am when the HVAC system started working (set in the profile) until it got the setpoint (74 °F) and kept it constant until people left work at 6:00 pm. Dry-bulb temperature was the outdoor temperature of December 1st of a typical year, low at morning and night, went high during the daytime and got the peak point around 2:00 pm. Heat loss ranging as the indoor temperature and dry-bulb temperature vary along the daytime. The graph of July 1 st is 345 different from the graph of December 1 st as the significantly different dry-bulb temperature in summer and winter. The difference of external conduction gains of U=0.49 Btu/h ft 2 F and U=0.30 Btu/h ft 2 F showed the effect of window U-value on building energy consumption. The shapes of them were similar, but for the graph with lower U-value, the peak point and the whole value through the day were lower which represented the less external conduction. 5B.2.3.3 Window U-value Summary The EUI bar chart comparison of the base model, model with window U=0.38 Btu/h ft 2 F and model with window U=0.30 Btu/h ft 2 F is beneficial to analyzing effect of window U-value (Figure 5B-25). Figure 5B-25 Harbin Window U-value EUI Comparison Bar Chart Comparing to the base model, the total EUI of model with window U=0.38 Btu/h ft 2 F was reduced by 0.6 kBtu/sf (1.3%); window U=0.30 was reduced by 1.1kBtu/sf (2.4%). The results showed 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 7.8 8 8.2 9.7 9 8.3 7.7 7.6 7.6 0 5 10 15 20 25 30 35 40 45 50 Base Model Window U=0.38 Btu/h ft2 F Window U=0.3 Btu/h ft2 F EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -1.3% -2.4% 346 that the lower window U-value, the lower model EUI. As the WWR was 40%, the window area was 40% of total façade area. Thus, 2.4% reduction of total EUI for the model with window U=0.3 Btu/h ft 2 F was effective. 5B.2.4 Analysis of WWR on the model The WWR of the base model was set as 40%. The WWR was set as 60% and 20% to get the corresponding result of cooling, heating, fan power energy use intensity and EUI. The EUI stacked bar chart comparison of the base model, the model with WWR 60% and the model WWR 20% are beneficial to analyzing effect of WWR (Figure 5B-26). The difference of total EUI and break down EUI was easy to compare and see the changes. Figure 5B-26 Harbin Base Model, Model with WWR 60% and Model with WWR 20% EUI Comparison Comparing to the base model, model with WWR 60%, total EUI was increased by 2.4 kBtu/sf, model break down EUI cooling was increased by 0.3 kBtu/sf, heating was increased by 1.7 kBtu/sf and fan power increased by 0.4 kBtu/sf; model with WWR 20%, total EUI was reduced by 1.9 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 7.8 8.1 7.7 9.7 11.4 8.4 7.7 8.1 7.2 0 5 10 15 20 25 30 35 40 45 50 Base Model WWR 60% WWR 20% EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 347 kBtu/sf, model break down EUI cooling was reduced by 0.1 kBtu/sf, heating EUI reduced by 0.7 kBtu/sf and fan power increased by 0.5 kBtu/sf. The results represented that model with higher WWR consumed more energy in break down EUI of cooling, heating, and fan power, and vice versa. WWR could have an effect on the building solar heat gain and the externa l conduction because of the area of window. Comparing to the wall, window transmits more sunlight into the indoor space and resist less heat transferring between the indoor and outdoor space, so more area of window resulted in more penetration of sunlight and more heat transferred through the building. The graphs included solar gain and external conduction on the day July 1 st in summer were utilized to analyze the energy consumption difference, as the solar heat gain and heat exchange are the key effects result from WWR (Figure 5B-27 & Figure 5B-28 & Figure 5B-29). 348 Figure 5B-27 Harbin WWR 60% July 1 st External Conduction and Solar Gain 349 Figure 5B-28 Harbin Base Model WWR 40% July 1 st External Conduction and Solar Gain 350 Figure 5B-29 Harbin WWR 20% July 1 st External Conduction and Solar Gain Graphs showed that models with lower WWR had less solar heat gain. The peak point of the WWR 20% model was around 65,000 Btu/h while the base model was v140,000 Btu/h and the WWR 60% model was 200,000 (Figure 5B-27 & Figure 5B-28 & Figure 5B-29). Not only the peak points, the total solar heat gain also increased as the WWR increased from 20% to 60%. Less solar heat gain results in less cooling load needed to condition the space, especially in summer, dry-bulb temperature is higher than the cooling setpoint much of the time. Graphs also showed that model with lower WWR resulted in the less heat loss. The peak point of WWR 20% model, base model, and WWR 60% model was all around 50,000 Btu/h (Figure 5B- 351 27 & Figure 5B-28 & Figure 5B-29). There was slight difference between the models with different WWR, thus the effect of WWR on heat loss was not that evident. 5B.2.4.1 WWR Summary The EUI bar chart comparison of the base model, model with WWR 60% and model with WWR 20% is beneficial to analyzing effect of WWR (Figure 5B-30). Figure 5B-30 Harbin WWR EUI Comparison Bar Chart Comparing to the base model, the total EUI of model with WWR 60% was increased by 2.4 kBtu/sf (5.3%); the total EUI of model with WWR 20% was reduced by 1.9 kBtu/sf (4.2%). The results showed that WWR is effective on building energy performance. The higher WWR, the higher model EUI. It had influence on heating, cooling and ventilation load at the same time. 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 7.8 8.1 7.7 9.7 11.4 8.4 7.7 8.1 7.2 0 5 10 15 20 25 30 35 40 45 50 Base Model WWR 60% WWR 20% EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 5.3% -4.2% 352 5B.2.5 Analysis of Window SHGC on the Model The SHGC of the base model was set as 0.22. The SHGC was set as 0.18 and 0.12 to get the corresponding result of cooling, heating, fan power energy use intensity and EUI. The EUI stacked bar chart comparison of the base model, the model with window SHGC 0.18 and the model window SHGC 0.12 are beneficial to analyzing effect of window SHGC (Figure 5B- 31). The difference of total EUI and break down EUI was easy to compare and see the changes. Figure 5B-31 Harbin Base Model, model with window SHGC 0.18 and model with window SHGC 0.12 EUI Comparison Comparing to the base model, model with window SHGC 0.18, total EUI was reduced by 0.3, model break down EUI fan power was reduced by 0.2 kBtu/sf, cooling was reduced by 0.2 kBtu/sf, and heating was increased by 0.1 kBtu/sf; model with window SHGC 0.12, total EUI was reduced by 0.3 kBtu/sf, model break down EUI fan power was reduced by 0.2 kBtu/sf, cooling was reduced by 0.3 kBtu/sf, and heating was increased by 0.2 kBtu/sf (Figure 5B-30). The results represented that model with higher window SHGC consumed more energy and vice versa. 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 7.8 7.6 7.3 9.7 9.8 10 7.7 7.5 7.3 0 5 10 15 20 25 30 35 40 45 50 Base Model SHGC 0.18 SHGC 0.12 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 353 Window SHGC represents the amount of solar heat that transmitted through the window as heat. Lower SHGC is more desirable for the building required more cooling load, as it reduces the amount of solar heat transmitted in the room. The graphs included solar gain on the day July 1 st in summer were utilized to analyze the energy consumption difference, as the solar heat gain is the key effect result from Window SHGC (Figure 5B-32 & Figure 5B-33 & Figure 5B-34). Figure 5B-32 Harbin Base Model SHGC 0.22 July 1 st Solar Gain 354 Figure 5B-33 Harbin Window SHGC 0.18 July 1 st Solar Gain 355 Figure 5B-34 Harbin Window SHGC 0.12 July 1 st Solar Gain Graphs showed that model with lower window SHGC had less solar heat gain. The peak point of the base model was around 140,000 Btu/h while model with window SHGC 0.18 was 110,000 Btu/h and model with window SHGC was around 75,000 Btu/h (Figure 5B-32 & Figure 5B-33 & Figure 5B-34). Not only the peak points, the total amount solar heat gain (reflected in Figure 5B- 32 & Figure 5B-33 & Figure 5B-34) also increased as the window SHGC increased from 0.22 to 0.18. Less solar heat gain results in less cooling load needed to condition the space, especially for the summer in Harbin, where dry-bulb temperature is higher than the cooling setpoint most of the time. 356 5B.2.5.1 Window SHGC Summary The EUI bar chart comparison of the base model, model with window SHGC 0.18 and model with window SHGC 0.12 is beneficial to analyzing the effect of window SHGC (Figure 5B-35). Figure 5B-35 Harbin Window SHGC EUI Comparison Bar Chart Comparing to the base model, the total EUI of model with window SHGC 0.18 was reduced by 0.3 kBtu/sf (0.7%); the total EUI of model with window SHGC 0.12 was reduced by 0.4 kBtu/sf (0.9%). The results showed that window SHGC was effective on the building energy consumption, however, not as much effective as the model in Los Angeles. For the model in Harbin, lower window SHGC could result in the higher heating load in winter, but lower cooling load in summer. 5B.2.6 Analysis of VT on the Model The visible transmittance of the base model was set as 0.32. The glazing VT was set as 0.43 and 0.55 to get the corresponding result of cooling, heating, fan power energy use intensity and EUI. 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 7.8 7.6 7.3 9.7 9.8 10 7.7 7.5 7.3 0 5 10 15 20 25 30 35 40 45 50 Base Model SHGC 0.18 SHGC 0.12 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -0.7% -0.9% 357 The EUI stacked bar chart comparison of the base model, the model with window VT 0.43 and the model with window VT 0.55 are beneficial to analyzing effect of window VT (Figure 5B-36). The difference of total EUI and break down EUI was easy to compare and see the changes. Figure 5B-36 Harbin Base Model, model with window VT 0.43 and model with window VT 0.55 EUI Comparison Comparing to the base model, model with window VT 0.43, and model with window VT 0.55 have the exact same total EUI results, including all the EUI break down results. It showed that in this model, the window VT did not affect the building energy efficiency at all. Window VT refers to the percentage of visible portion of sunlight that transmitted into the room through the window. The graphs included internal gain, solar gain, external conduction gain and infiltration gain on the day July 1 st and December 1 st were utilized to analyze the energy consumption difference (Figure 5B-37 & Figure 5B-38). 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 7.8 7.8 7.8 9.7 9.7 9.7 7.7 7.7 7.7 0 5 10 15 20 25 30 35 40 45 50 Base Model VT 0.43 VT 0.55 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 358 Figure 5B-37 Harbin Base Model VT July 1 st External conduction, Solar Gain, Internal Gain, Infiltration 359 Figure 5B-38 Harbin Base Model VT December 1 st External conduction, Solar Gain, Internal Gain, Infiltration Internal gain, solar gain, external conduction gain and infiltration gain are the four main energy gains that affecting the building energy performance. The graphs showed how they varied in the certain day (July 1 st and December 1 st ). The graphs of the base model, model with window VT 0.43 and model with window VT 0.55 were the same which represented that window VT had no influence on the model energy consumption. 5B2.6.1 Glazing VT Summary The EUI bar chart comparison of the base model, model with glazing VT 0.43 and model with glazing VT 0.55 is beneficial to analyzing effect of glazing VT (Figure 5B-39). 360 Figure 5B-39 Harbin Glazing VT EUI Comparison Bar Chart Comparing to the base model, the total EUI of model with glazing VT 0.43 and model with glazing VT 0.55 was the same. The results showed that glazing VT did not affect the energy performance of this model. Glazing VT could affect the sunlight penetration, which is effective on the indoor lighting load, However, glazing VT was not an effective study as the lighting load was not considered with it. 5B.2.7 Analysis of Building Orientation on the Model The long side of the base model orientation was set as facing north. The orientation was set as Northwest (45 degree), West (90 degree) and Southwest (135 degree) to get the corresponding result of cooling, heating, fan power energy use intensity and EUI. The EUI stacked bar chart comparison of base model, the model rotated 45 degree, the model rotated 90 degree, and the model rotated 135 degree are beneficial to analyzing effect of building orientation (Figure 5B-40). The results of cooling, heating, fan power energy use intensity and 6.7 6.7 6.7 8 8 8 1.7 1.7 1.7 3.8 3.8 3.8 7.8 7.8 7.8 9.7 9.7 9.7 7.7 7.7 7.7 0 5 10 15 20 25 30 35 40 45 50 Base Model VT 0.43 VT 0.55 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 0.0% 0.0% 361 total EUI were derived and included to conduct the analysis of how orientation affects building energy performance. Figure 5B-40 Harbin Base Model, model rotating 45°, model rotating 90° and model rotating 135°EUI Comparison Total EUI and break down EUI of model with orientation 45 degree and 90 degree were the same to the base model; comparing to the base model, total EUI of model with orientation 135 degree was reduced by 0.1 kBtu/sf, which was reflected in the reduction of heating (Figure 5B-40). The building orientation could affect the building cooling and heating load by affecting the solar heat gain. However, the floor plan of the model is symmitrical and the long side is not very different from the short side of the building. In addtion, the WWR of each suface of the model is the same which is 40%. Thus, rotating the model to change the solar penertration did not have siginificant influence on building energy consumption. 6.7 6.7 6.7 6.7 8 8 8 8 1.7 1.7 1.7 1.7 3.8 3.8 3.8 3.8 7.8 7.8 7.8 7.8 9.7 9.7 9.7 9.6 7.7 7.7 7.7 7.7 0 5 10 15 20 25 30 35 40 45 50 Base Model Orientation 45° Orientation 90° Orientation 135° EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 362 5B.2.7.1 Model Orientation Summary The EUI bar chart comparison of the base model, model rotating 45 degree, model rotating 90 degree and 135 degree, is beneficial to analyzing effect of window SHGC (Figure 5B-41). Figure 5B-41 Los Angeles Model Orientation EUI Comparison Bar Chart Comparing to the base model, the total EUI of model 45 degree and model 90 degree were the same; the total EUI of model 135 degree was reduced by 0.1 kBtu/sf (0.2%). The results showed that building total EUI could be by the model orientation, however, the influence is slight. 5B.4 Summary Seven passive design strategies were applied to the base model in Harbin. The results showed that passive design strategies could affect building energy consumption, but some of them could only have slight or no influence on the base model. The strategies were listed in order of the energy savings (Table 5B-1) (Figure 5B-42). 6.7 6.7 6.7 6.7 8 8 8 8 1.7 1.7 1.7 1.7 3.8 3.8 3.8 3.8 7.8 7.8 7.8 7.8 9.7 9.7 9.7 9.6 7.7 7.7 7.7 7.7 0 5 10 15 20 25 30 35 40 45 50 Base Model Orientation 45° Orientation 90° Orientation 135° EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 0.0% 0.0% -0.2% 363 Table 5B-1 Harbin Strategies Energy Efficiency Importance Ranking Base Model – Harbin Harbin Simulation Results Savings and Percentage 1 WWR (40%) WWR (20%) 1.9 kBtu/sf (4.2%) 2 Window SHGC (0.22) Window U-factor (U=0.3 Btu/h ft 2 F) 1.1kBtu/sf (2.4%) 3 Window (U=0.46 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) 0.8 kBtu/sf (1.8%) 4 Wall Insulation (U=0.069 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) 0.6 kBtu/sf (1.3%) 5 Roof Insulation (U=0.049 Btu/h ft 2 F) Glazing SHGC (0.12) 0.4 kBtu/sf (0.9%) 6 Orientation (Long side facing north) Orientation (Long side facing Southwest) 0.1 kBtu/sf (0.2%) 7 Glazing VT (0.55) Glazing VT (0.55) 0 kBtu/sf (0.0%) 364 Figure 5-42 Harbin Model Best Strategies EUI Comparison Bar Chart For lighting (represented by blue color in stacked bar chart), equipment (orange), DHW (grey), and pump (yellow), they were consumed to have consistent value (use hand calculation in spreadsheet) in the simulation. The calculation was shown in chapter 4: The lighting use density: 6.7 kBtu/ft 2 The computer use density: 8.0 kBtu/ft 2 The DHW use density:1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 Thus, the models applied with different design strategies all had the same EUI break down on lighting (blue), equipment (orange), DHW (grey) and pump (yellow). The cooling load (light blue), heating load (green) and fan power (dark blue) were derived from VistaPro in IES VE and calculate in the spreadsheet (mentioned in chapter 4). Thus, the cooling 6.7 6.7 6.7 6.7 6.7 6.7 6.7 6.7 8 8 8 8 8 8 8 8 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 7.8 7.7 8.2 7.9 8 7.3 7.8 7.8 9.7 8.4 8.3 8.9 9 10 9.6 9.7 7.7 7.2 7.6 7.6 7.6 7.3 7.7 7.7 0 5 10 15 20 25 30 35 40 45 50 Base Model WWR 20% Window U=0.3 Btu/h ft2 F Wall U=0.016 Btu/h ft2 F Roof U=0.019 Btu/h ft2 F SHGC 0.12 Orientation 135° Glazing VT EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -4.2% -2.4% -1.8% -1.3% -0.2% 0.0% -0.9% 365 load (light blue), heating load (green) and fan power (dark blue) were changing the model energy consumption. In addition, the change of total EUI was depending on the change of them. 1. WWR was the most efficient strategy that applied to the model as it affected the amount of solar heat gain which was quite important to the location with sufficient sunlight. Lower WWR could reduce the amount of sunlight penetration, so as to the solar heat gain of building in both summer and winter. Thus, the cooling load, heating load and fan power were reduced. 2. Window U-value, wall insulation U-value and roof insulation U-value are the measurement of heat transferred through the material. Lower U-value resulted less heat transferred through window, wall, and roof. Thus, in winter, heat generated to increase indoor air temperature transferred to the outdoor space was less, so the heating demand reduced; in summer, less heat transferred to the outdoor space resulted in more cooling load to cool down the temperature. 3. Lower window SHGC resulted in less solar heat going through the window, so less solar heat in summer could contribute to the savings on cooling, but in winter resulted in more heating to warm the indoor space. 4. Because of the symmetrical floor plan and similar length of long side and short side of building, the orientation of the model only had slight effect on model EUI. 5.VT had no effect on the model energy consumption, which was indicated by the results of simulation, as it only determined the proportion of visible sunlight transmitted in the room. As the different weather conditions, functions of buildings, methods of research and so on, the strategies could have different effect on building energy efficiency (Table 5B-2). 366 Table 5B-2 Building Energy Efficiency Importance Ranking Final results – Harbin Building Designer Ranking Regression Ranking Decision Tree Ranking Citation (Khan,2021) (Khan,2021) (Khan,2021) 1 WWR (20%) HVAC System Cooling System Occupancy Density 2 Glazing SHGC (0.12) Occupancy Density Glass Type Floor Height 3 Wall Insulation (U=0.016 Btu/h ft 2 F) WWR Heating System Heating System 4 Window U- factor (U=0.3 Btu/h ft 2 F) Roof Material Shading Cooling System 5 Roof Insulation (U=0.019 Btu/h ft 2 F) Wall Material Orientation WWR 6 Orientation (Long side facing Northwest, Southwest) Window Glass Type Wall Insulation Stories 7 Glazing VT (0.55) Orientation Rise_N (Low/High) Shading 8 Shading Window Type Wall Material 9 Window Frame Type Occupancy Density Window Glass Type 10 Stories Roof Material Ceiling Height 11 Floor Height Building Footprint Roof Material 12 Ceiling Height Orientation 367 13 WWR Window Frame Type 14 Floor Area Size 5B.5 Comparison between the Simulation Results of Los Angeles and Harbin The design strategies of Los Angeles and Harbin were listed in order of the energy efficiency from high to low (Table 5B-3). The strategies in the same order were marked in blue color. Table 5B-3 Los Angeles and Harbin Strategies Energy Efficiency Importance Ranking Los Angeles Simulation Results Savings and Percentage Harbin Simulation Results Savings and Percentage 1 WWR (20%) 1.9 kBtu/sf (4.6%) WWR (20%) 1.9 kBtu/sf (4.2%) 2 Glazing SHGC (0.12) 1.5 kBtu/sf (3.6%) Window U-factor (U=0.3 Btu/h ft 2 F) 1.1kBtu/sf (2.4%) 3 Wall Insulation (U=0.016 Btu/h ft 2 F) 0.3 kBtu/sf (0.7%) Wall Insulation (U=0.016 Btu/h ft 2 F) 0.8 kBtu/sf (1.8%) 4 Window U-factor (U=0.3 Btu/h ft 2 F) 0.3 kBtu/sf (0.7%) Roof Insulation (U=0.019 Btu/h ft 2 F) 0.6 kBtu/sf (1.3%) 5 Roof Insulation (U=0.019 Btu/h ft 2 F) 0.1 kBtu/sf (0.2%) Glazing SHGC (0.12) 0.4 kBtu/sf (0.9%) 6 Orientation (Long side facing Northwest and Southwest) 0.1 kBtu/sf (0.2%) Orientation (Long side facing Southwest) 0.1 kBtu/sf (0.2%) 7 Glazing VT (0.55) 0 kBtu/sf (0.0%) Glazing VT (0.55) 0 kBtu/sf (0.0%) 368 1. The active design strategy HVAC system was the most efficient strategy in both Los Angeles and Harbin. The utilization of heat recovery system highly increase the system energy efficiency and recover the waste heat that rejected by the VAV reheat system. 2. WWR was the passive design strategy that could save the most in both Los Angeles (4.6%) and Harbin (4.2%). It affected the amount of solar heat gain in Los Angeles, which is one main reason that resulted in the increase of indoor air temperature; it affected both solar heat gain in summer and external conduction in winter in Harbin. 3. Wall insulation, roof insulation and window U-value had different energy performance in Los Angeles and Harbin as the different weather condition. Those three strategies are utilized to reduce the heat transmissions. Most of working hours, the dry-bulb temperature was above 60 °F which was closing to the duration of setpoints (68 °F - 74 °F), so the amount of heat transmission was not too high because of the small temperature difference between indoor space and outdoor space. Thus, lower U-value of insulation and window was not effective for resisting heat transmission. Which is different from Los Angeles, the temperature difference between indoor and outdoor space could ranging from 80 °F to 20 °F. The high temperature difference resulted in the large amount of heat transmission in winter. Thus, lower U-value insulation and window resisted more heat transmissions. 4. Window SHGC was also affecting differently for building energy consumption in Los Angeles and Harbin. Lower window SHGC was used to reducing the unnecessary solar gain through the window was beneficial for the model in Los Angeles, as the large amount of surplus sunlight throughout the year. However, for the model in Harbin, lower window SHGC reduced the solar heat gain which reduced the cooling load in summer but increased the heating in winter because of the less heat gain. 5. Orientation was performing similarly in Los Angeles and Harbin because of the symmetrical floor plan and similar length of long sides and short sides. Therefore, the rotation of model did not affect the solar heat of model. 369 6. Glazing VT did not affect the model in Los Angeles and Harbin at all because the lighting energy consumption was assumed as constant. Thus the glazing VT that only affect the amount of visible light getting through the window did have any influence on model. The strategies performance in Los Angeles and Harbin were listed in order of energy saving percentage from high to low. They were compared to ranking list from chapter 2 (Table 5B-4). The strategies in the same order were marked in blue color. 370 Table 5B-4 Strategies Energy Efficiency Importance and Comparison Ranking Final results – Los Angeles Final results – Harbin Building Designer Ranking Regression Ranking Decision Tree Ranking City Los Angeles Harbin Los Angeles Los Angeles Los Angeles Citation (Khan,2021) (Khan,2021) (Khan,2021) 1 WWR (20%) WWR (20%) HVAC System Cooling System Occupancy Density 2 Glazing SHGC (0.12) Window U- factor (U=0.3 Btu/h ft 2 F) Occupancy Density Glass Type Floor Height 3 Wall Insulation (U=0.016 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) WWR Heating System Heating System 4 Window U- factor (U=0.3 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) Roof Material Shading Cooling System 5 Roof Insulation (U=0.019 Btu/h ft 2 F) Glazing SHGC (0.12) Wall Material Orientation WWR 6 Orientation (Long side facing Northwest and Southwest) Orientation (Long side facing Southwest) Window Glass Type Wall Insulation Stories 7 Glazing VT (0.55) Glazing VT (0.55) Orientation Rise_N (Low/High) Shading 8 Shading Window Type Wall Material 9 Window Frame Type Occupancy Density Window Glass Type 371 10 Stories Roof Material Ceiling Height 11 Floor Height Building Footprint Roof Material 12 Ceiling Height Orientation 13 WWR Window Frame Type 14 Floor Area Size The ranking list from different resources consisted of different strategies and in different orders. The same strategy could affect differently as multiple reasons, such as, local climate, building function, area, and so on. Apparently, HVAC system was the most effective one comparing to others, then following with the insulation, WWR and window parameters. The Occupancy density could not be considered as a strategy but a factor. 372 Chapter 6. Eight Net Zero Buildings Attempts Using an Integrative Approach Seven passive design strategies to the model were examined: wall insulation, roof insulation, window U-value, WWR, window SHGC, orientation and glazing VT were examined. The effect of each strategy was analyzed and listed with their energy efficiency from high to low. An integration of multiple design controls could result in better performance. Four integrations for the model for both Los Angeles and Harbin are explored in this chapter. 6.1 The Integration of Design Strategies of Model in Los Angeles Four integrations of different design strategies were applied to the base model to analyze model energy performance. Integration 1 is based on the best of all the variables and parameters shown in chapter 5A. 6.1.1 Los Angeles Integration 1 An integration of all the passive design strategies examined in the previous chapters, WWR, window SHGC, window U-value, wall insulation, roof insulation, orientation, and glazing VT, consisted of the integration 1 (Table 6-1). 373 Table 6-1 Los Angeles Base Model and Integration 1 Los Angeles Base Model Integration 1 1 HVAC System (VAV-Reheat) HVAC System (VAV-Reheat) 2 WWR (40%) WWR (20%) 3 Window SHGC (0.22) Window SHGC (0.12) 4 Window (U=0.46 Btu/h ft 2 F) Window (U=0.3 Btu/h ft 2 F) 5 Wall Insulation (U=0.069 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) 6 Roof Insulation (U=0.049 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) 7 Orientation (Long side facing north) Orientation (Long side facing Southwest) 8 Glazing VT (0.32) Glazing VT (0.55) EUI 41.4 kBtu/ft 2 38.6 kBtu/ft 2 Annual PV Peak Load 523,992kWh 523,992kWh PV Panels to reach net zero 210% Roof Area 200% Roof Area EUI breakdown The lighting use density: 6.7 kBtu/ft 2 The computer use density: 8 kBtu/ft 2 The DHW use density: 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 Cooling load EUI: 10.4 kBtu/ft 2 Heating load EUI: 0.6 kBtu/ft 2 Fan power: 7.4 kBtu/ft 2 374 Total EUI Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 10.4 kBtu/ft 2 + 0.6 kBtu/ft 2 + 7.4 kBtu/ft 2 = 38.6 kBtu/ft 2 (Figure 6-1). Figure 6-1 Los Angeles Base Model and Integration 1 EUI Comparison Total EUI was reduced by 7% which resulted from the application of seven passive design strategies, WWR 20%, window SHGC 0.12, window U=0.3 Btu/h ft 2 F, wall insulation U=0.016 Btu/h ft 2 F, roof insulation U=0.019 Btu/h ft 2 F, orientation (long side facing southwest), and glazing VT 0.32. For EUI breakdown, cooling load was reduced by 1.6 kBtu/ft 2 (13%); heating load was reduced by 0.2 kBtu/ft 2 (3%); fan power was reduced by 1 kBtu/ft 2 (12%). 6.7 6.7 8 8 1.7 1.7 3.8 3.8 12 10.4 0.8 0.6 8.4 7.4 0 5 10 15 20 25 30 35 40 45 Base Model Integration 1 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -7% 375 Compared to the base model, the lower WWR, window SHGC and window U-value could reduce the solar heat gain. As the results shown in chapter 5A, WWR 20 reduced 4.6% of total EUI, window SHGC 0.12 reduced 4.0% of total EUI and window U-value reduced 0.7% of total EUI. However, the lower WWR reduced the area of window, which decreased the effect of window SHGC and window U-value. Thus, the application of those three could not achieve the performance as the composition of them applied individually. As the increase of wall area and the lower U-value of wall insulation, less heat conduction between the indoor and outdoor spaces. Orientation could decrease 0.2% of total EUI of base model by rotating the building to reduce the solar heat gain through envelop. Nevertheless, the reduction of window area and the increase of wall insulation made it less effective on energy consumption. As the integration 1 with WWR 20%, window SHGC 0.12, window U=0.3 Btu/h ft 2 F, wall U=0.016 Btu/h ft 2 F, roof U=0.019 Btu/h ft 2 F, long side of model facing southwest and glazing VT 0.32, could not get net zero and PV panels on the roof turned out to be 200% of roof area. Therefore, the integration 2 and 3 were tried with new HVAC system, even though it was not studied previously in chapter 4 and 5. It was not possible to attain net zero for this case study without an addition of photovoltaics in excess of 200% of the roof area. Therefore, an integration of solutions 2, 3, and 4 was tried with a new HVAC system, overhang, and LED lighting fixture, even though they were not studied previously in Chapters 4 and 5. 6.1.2 Los Angeles Integration 2 The HVAC system of the base model is VAV-Reheat system. Application of heat recovery system could recover the waste heat of the system to reduce the cooling and heating demand of model. 376 Based on the application of integration 1, the replacement of HVAC system consisted of the integration 2, shown here (Table 6-2). Table 6-2 Los Angeles Base Model and Integration 2 Los Angeles Base Model Integration 2 1 HVAC System (VAV-Reheat) VAV-Reheat with Heat Recovery 2 WWR (40%) WWR (20%) 3 Window SHGC (0.22) Window SHGC (0.12) 4 Window (U=0.46 Btu/h ft 2 F) Window (U=0.3 Btu/h ft 2 F) 5 Wall Insulation (U=0.069 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) 6 Roof Insulation (U=0.049 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) 7 Orientation (Long side facing north) Orientation (Long side facing Southwest) 8 Glazing VT (0.32) Glazing VT (0.55) EUI 41.4 kBtu/ft 2 35.0 kBtu/ft 2 Annual PV Peak Load 523,992kWh 523,992kWh PV Panels to reach net zero 210% Roof Area 180% Roof Area EUI breakdown The lighting use density: 6.7 kBtu/ft 2 The computer use density: 8 kBtu/ft 2 The DHW use density: 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 377 Cooling load EUI: 8.0 kBtu/ft 2 Heating load EUI: 0.5 kBtu/ft 2 Fan power EUI: 6.3 kBtu/ft 2 Total EUI Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 8.0 kBtu/ft 2 + 0.5 kBtu/ft 2 + 6.3 kBtu/ft 2 = 35.0 kBtu/ft 2 (Figure 6- 2). Figure 6-2 Los Angeles Base Model and Integration 2 EUI Comparison 6.7 6.7 8 8 1.7 1.7 3.8 3.8 12 8 0.8 0.5 8.4 6.3 0 5 10 15 20 25 30 35 40 45 Base Model Integration 2 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -16% 378 Total EUI was reduced by 16% which resulted from the application of seven passive design strategies, WWR 20%, window SHGC 0.12, window U=0.3 Btu/h ft 2 F, wall insulation U=0.016 Btu/h ft 2 F, roof insulation U=0.019 Btu/h ft 2 F, orientation (long side facing southwest), and glazing VT 0.32; and the application of heat recovery system. For EUI breakdown, cooling load was reduced by 4.0 kBtu/ft 2 (30%); heating load was reduced by 0.3 kBtu/ft 2 (38%); fan power was reduced by 1 kBtu/ft 2 (25%). Compared to the base model, the lower WWR, window SHGC and window U-value could reduce the solar heat gain. As the results shown in chapter 5A, WWR 20 reduced 4.6% of total EUI, window SHGC 0.12 reduced 4.0% of total EUI and window U-value reduced 0.7% of total EUI. However, lower WWR reduced the area of window, which decreased the effect of window SHGC and window U-value. Thus, the application of those three could not achieve the performance as the composition of them applied individually. As the increase of wall area and the lower U-value of wall insulation, less heat conduction between the indoor and outdoor spaces. Orientation could decrease 0.2% of total EUI of base model by rotating the building to reduce the solar heat gain through envelope. Nevertheless, the reduction of window area and the increase of wall insulation made it less effective on energy consumption. But the application of heat recovery system highly reduced the energy consumption on fan power, cooling and heating load as its recovery of waste heat. 6.1.3 Los Angeles Integration 3 High WWR is always considered as an option to have better view for the occupants. However, it will increase solar heat gain for the indoor space. Integration 3 included WWR 60% to improve 379 the view of building and low window SHGC and U-value reduced the sunlight penetration (Table 6-3). Table 6-3 Los Angeles Base Model and Integration 3 Los Angeles Base Model Integration 3 1 HVAC System (VAV-Reheat) VAV-Reheat with Heat Recovery 2 WWR (40%) WWR (60%) 3 Window SHGC (0.22) Window SHGC (0.12) 4 Window (U=0.46 Btu/h ft 2 F) Window (U=0.30 Btu/h ft 2 F) 5 Wall Insulation (U=0.069 Btu/h ft 2 F) Wall Insulation (U=0.069 Btu/h ft 2 F) 6 Roof Insulation (U=0.049 Btu/h ft 2 F) Roof Insulation (U=0.049 Btu/h ft 2 F) 7 Orientation (Long side facing north) Orientation (Long side facing north) 8 Glazing VT (0.32) Glazing VT (0.32) EUI 41.4 kBtu/ft 2 36.5 kBtu/ft 2 Annual PV Peak Load 523,992kWh 523,992kWh PV Panels to reach net zero 210% Roof Area 190% Roof Area EUI breakdown The lighting use density: 6.7 kBtu/ft 2 The computer use density: 8 kBtu/ft 2 The DHW use density: 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 380 Cooling load EUI: 8.9 kBtu/ft 2 Heating load EUI: 0.6 kBtu/ft 2 Fan power: 6.8 kBtu/ft 2 Total EUI Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 8.9 kBtu/ft 2 + 0.6 kBtu/ft 2 + 6.8 kBtu/ft 2 = 36.5kBtu/ft 2 (Figure 6-3). Figure 6-3 Los Angeles Base Model and Integration 3 EUI Comparison Total EUI was reduced by 12% which resulted from integration 3. For EUI breakdown, cooling load was reduced by 3.1 kBtu/ft 2 (26%); ; heating load was reduced by 0.2 kBtu/ft 2 (25%); fan power was reduced by 1.6 kBtu/ft 2 (20%). Even through it consumes more energy, 60% of WWR 6.7 6.7 8 8 1.7 1.7 3.8 3.8 12 8.9 0.8 0.6 8.4 6.8 0 5 10 15 20 25 30 35 40 45 Base Model Integration 3 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -12% 381 is usually adopted in the design to provide better view for the occupancy. Integration 3 adopted three passive design strategies, WWR 60%, window SHGC 0.12, window U=0.3 Btu/h ft 2 F, and heat recovery system. WWR 60% increased the total EUI of base model by 4.3% by increasing fan power, cooling and heating load as mentioned in chapter 5A. Thus, low value of window SHGC and window U=0.3 Btu/h ft 2 F were kept as integration 1 and 2 to decrease solar penetration through window. Roof and wall insulation were not selected because their slight effect in climate of Los Angeles and reduced wall area. But the application of heat recovery system highly reduced the energy consumption on fan power, cooling, and heating load as its recovery of waste heat. As the integration 2 and 3 could not get net zero and PV panels on the roof turned out to be 180 % and 190% of roof area. Therefore, the integration 4 was tried with overhang and LED lighting fixtures, even though it was not studied previously in chapter 4 and 5. 6.1.4 Los Angeles Integration 4 The HVAC system of base model is VAV-Reheat system. Application with heat recover system could recover the waste heat of system to reduce the cooling and heating demand of model. Based on the application of integration 2, the replacement with LED lighting fixtures and application of heat recovery system, plus overhang consisted of the integration 4 (Table 6-4). Integration 4 shown the best result could be achieve in this thesis for Los Angeles. 382 Table 6-4 Los Angeles Base Model and Integration 4 Los Angeles Base Model Integration 4 1 HVAC System (VAV-Reheat) VAV-Reheat with Heat Recovery 2 WWR (40%) WWR (20%) 3 Window SHGC (0.22) Window SHGC (0.12) 4 Window (U=0.46 Btu/h ft 2 F) Window (U=0.3 Btu/h ft 2 F) 5 Wall Insulation (U=0.069 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) 6 Roof Insulation (U=0.049 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) 7 Orientation (Long side facing north) Orientation (Long side facing Southwest) 8 Glazing VT (0.32) Glazing VT (0.55) 9 Fluorescent Light Fixture LED Lighting Fixture 10 No Overhang With Overhang EUI 41.4 kBtu/ft 2 31.7 kBtu/ft 2 Annual PV Peak Load 523,992kWh 523,992kWh PV Panels to reach net zero 210% Roof Area 160% Roof Area EUI breakdown The LED lighting use density: 3.8 kBtu/ft 2 ((D. Li, Kun, and Gao 2018) The computer use density: 8 kBtu/ft 2 The DHW use density: 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 Cooling load EUI: 7.8 kBtu/ft 2 383 Heating load EUI: 0.5 kBtu/ft 2 Fan power EUI: 6.1 kBtu/ft 2 Total EUI Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 3.8 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 7.8 kBtu/ft 2 + 0.5 kBtu/ft 2 + 6.1 kBtu/ft 2 = 31.7 kBtu/ft 2 (Figure 6- 4). Figure 6-4 Los Angeles Base Model and Integration 4 EUI Comparison Total EUI was reduced by 23% which resulted from integration 4. For EUI breakdown, cooling load was reduced by 4.2 kBtu/ft 2 (35%); heating load was reduced by 0.3 kBtu/ft 2 (38%); fan power was reduced by 2.3 kBtu/ft 2 (27%), lighting was reduced by 2.9 kBtu/ft 2 (43%). 6.7 3.8 8 8 1.7 1.7 3.8 3.8 12 7.8 0.8 0.5 8.4 6.1 0 5 10 15 20 25 30 35 40 45 Base Model Integration 4 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -23% 384 Compared to the base model, the lower WWR, window SHGC and window U-value could reduce the solar heat gain. As the results shown in chapter 5A, WWR 20 reduced 4.6% of total EUI, window SHGC 0.12 reduced 4.0% of total EUI and window U-value reduced 0.7% of total EUI. However, lower WWR reduced the area of window, which decreased the effect of window SHGC and window U-value. Thus, the application of those three could not achieve the performance as the composition of them applied individually. As the increase of wall area and the lower U-value of wall insulation, less heat conduction between the indoor and outdoor spaces. Orientation could decrease 0.2% of total EUI of base model by rotating the building to reduce the solar heat gain through envelope. Nevertheless, the reduction of window area and the increase of wall insulation made it less effective on energy consumption. But the application of heat recovery system highly reduced the energy consumption on fan power, cooling and heating load as its recovery of waste heat. In addition, the application of overhang reduced the solar heat gain of indoor space in summer and winter, so it reduced more on cooling load, slight on heating load. The replacement of fluorescent lighting fixtures with LED fixtures could reduce almost half of lighting EUI consumption. 6.1.5 Los Angeles Integration with Best Result Based on the comparison of Los Angeles base model, integration 1, integration 2, integration 3, and integration 4, integration resulted in the lowest EUI comparing to other integrations, which is 23% reduction on total EUI (Figure 6-5). 385 Figure 6-5 Los Angeles Base Model, Integration 1, Integration 2, Integration 3, and Integration 4, PV Generation Comparison The strategies of all the integrations, EUI of each integration, and area of PV panels were listed for comparison (Table 6-5). 6.7 6.7 6.7 6.7 3.8 17.5 8 8 8 8 8 1.7 1.7 1.7 1.7 1.7 3.8 3.8 3.8 3.8 3.8 12 10.4 8 8.9 7.8 0.8 0.6 0.5 0.6 0.5 8.4 7.4 6.3 6.8 6.1 0 5 10 15 20 25 30 35 40 45 Los Angeles Base Model Los Angeles Integration 1 Los Angeles Integration 2 Los Angeles Integration 3 Los Angeles Integration 4 PV Panels Energy Gerneration EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -7% -16% -12% -23% 386 Table 6-5 Los Angeles Base Model, Integration 1, Integration 2, Integration 3, and Integration 4 Strategies Los Angeles Base Model Integration 1 Integration 2 Integration 3 Integration 4 Best of the studied variables Best of the studied variables with heat recovery system Integration with the best result 1 HVAC System (VAV- Reheat) HVAC System (VAV-Reheat) VAV-Reheat System with Heat Recovery VAV-Reheat System with Heat Recovery VAV-Reheat System with Heat Recovery 2 WWR (40%) WWR (20%) WWR (20%) WWR (60%) WWR (20%) 3 Window SHGC (0.22) Window SHGC (0.12) Window SHGC (0.12) Window SHGC (0.12) Window SHGC (0.12) 4 Window (U=0.46 Btu/h ft 2 F) Window (U=0.3 Btu/h ft 2 F) Window (U=0.3 Btu/h ft 2 F) Window (U=0.30 Btu/h ft 2 F) Window (U=0.3 Btu/h ft 2 F) 5 Wall Insulation (U=0.069 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) Wall Insulation (U=0.069 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) 6 Roof Insulation (U=0.049 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) Roof Insulation (U=0.049 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) 7 Orientation (Long side facing north) Orientation (Long side facing Southwest) Orientation (Long side facing Southwest) Orientation (Long side facing north) Orientation (Long side facing Southwest) 8 Glazing VT (0.32) Glazing VT (0.55) Glazing VT (0.55) Glazing VT (0.32) Glazing VT (0.55) 9 Fluorescent Lighting Fixture Fluorescent Lighting Fixture Fluorescent Lighting Fixture Fluorescent Lighting Fixture LED Lighting Fixture 10 No Overhang No Overhang No Overhang No Overhang With Overhang EUI 41.4 kBtu/ft 2 38.6 kBtu/ft 2 35.0 kBtu/ft 2 36.5 kBtu/ft 2 31.7 kBtu/ft 2 Annual PV Peak Load 523,992kWh 523,992kWh 523,992kWh 523,992kWh 523,992kWh PV Panels 210% Roof Area 200% Roof Area 180% Roof Area 190% Roof Area 160% Roof Area 387 to reach net zero Integration 4 consisted of all the passive design strategies which were studied in chapter 4 and 5 with the best values, WWR 20%, window SHGC 0.12, window U=0.3 Btu/h ft 2 F, wall U=0.016 Btu/h ft 2 F, roof U=0.019 Btu/h ft 2 F, long side of model facing southwest and glazing VT 0.32. Plus, overhang, the application of heat recovery system and LED lighting fixture. Compared to the base model, total EUI was reduced by 23% to 31.7 kBtu/ft 2 . Also, the area of PV panels was reduced to 160% from 210%. 6.2.1 Harbin Integration 1 An integration of all the passive design strategies examined in the previous chapters, WWR, window SHGC, window U-value, wall insulation, roof insulation, orientation, and glazing VT, consisted of the integration 1 (Table 6-6). Integration 1 is based on the best of all the variables and parameters shown in chapter 5B. 388 Table 6-6 Harbin Base Model and Integration 1 Harbin Base Model Integration 1 1 HVAC System (VAV-Reheat) VAV-Reheat system 2 WWR (40%) WWR (20%) 3 Window SHGC (0.22) Window SHGC (0.12) 4 Window (U=0.46 Btu/h ft 2 F) Window (U=0.3 Btu/h ft 2 F) 5 Wall Insulation (U=0.069 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) 6 Roof Insulation (U=0.049 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) 7 Orientation (Long side facing north) Orientation (Long side facing Southwest) 8 Glazing VT (0.55) Glazing VT (0.32) EUI 45.4 kBtu/ft 2 41.2 kBtu/ft 2 Annual PV Peak Load 448,765 kWh 448,765 kWh PV Panels to reach net zero 270% Roof Area 250% Roof Area EUI breakdown The lighting use density: 6.7 kBtu/ft 2 The computer use density: 8 kBtu/ft 2 The DHW use density: 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 Cooling load EUI: 8.1 kBtu/ft 2 Heating load EUI: 6.3 kBtu/ft 2 Fan power: 6.8 kBtu/ft 2 389 Total EUI Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 8.1 kBtu/ft 2 + 6.3 kBtu/ft 2 + 6.8 kBtu/ft 2 = 41.2 kBtu/ft 2 (Figure 6-1). Figure 6-6 Harbin Base Model and Integration 1 EUI Comparison Total EUI was reduced by 9% which resulted from the application of seven passive design strategies, WWR 20%, window SHGC 0.12, window U=0.3 Btu/h ft 2 F, wall insulation U=0.016 Btu/h ft 2 F, roof insulation U=0.019 Btu/h ft 2 F, orientation (long side facing southwest), and glazing VT 0.32. For EUI breakdown, cooling load was increased by 0.3 kBtu/ft 2 (4%); heating load was reduced by 3.6 kBtu/ft 2 (37%); fan power was reduced by 0.9 kBtu/ft 2 (12%). Compared to the base model, lower WWR, window SHGC and window U-value could reduce the solar heat gain. As the results shown in chapter 5B, WWR 20 reduced 4.2% of total EUI, window 6.7 6.7 8 8 1.7 1.7 3.8 3.8 7.8 8.1 9.7 6.1 7.7 6.8 0 5 10 15 20 25 30 35 40 45 50 Base Model Integration 1 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -9% 390 SHGC 0.12 reduced 0.9% of total EUI and window U-value reduced 2.4% of total EUI. However, lower WWR reduced the area of window, which decreased the effect of window SHGC and window U-value. Thus, the application of those three could not achieve the performance as the composition of them applied individually. As the increase of wall area and the lower U-value of wall insulation, less heat conduction between the indoor and outdoor spaces. Orientation could decrease 0.2% of total EUI of base model by rotating the building to reduce the solar heat gain through envelop. Nevertheless, the reduction of window area and the increase of wall insulation made it less effective on energy consumption. It was not possible to attain net zero for the case study building without an addition of photovoltaics in excess of 250% of the roof area. Therefore, an integration of solutions 2, 3 and 4 was tried with a new HVAC system, overhang, and LED lighting fixture, even though they wer not studied previously in Chapters 4 and 5. 6.2.2 Harbin Integration 2 The HVAC system of the base model is VAV-Reheat system. Application with heat recovery system could recover the waste heat of the system to reduce the cooling and heating demand of model. Based on the integration 1, the application of HVAC system consisted of the integration 2, shown here (Table 6-7). 391 Table 6-7 Harbin Base Model and Integration 2 Harbin Base Model Integration 2 1 HVAC System (VAV-Reheat) VAV-Reheat System with Heat Recovery 2 WWR (40%) WWR (20%) 3 Window SHGC (0.22) Window SHGC (0.12) 4 Window (U=0.46 Btu/h ft 2 F) Window (U=0.3 Btu/h ft 2 F) 5 Wall Insulation (U=0.069 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) 6 Roof Insulation (U=0.049 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) 7 Orientation (Long side facing north) Orientation (Long side facing Southwest) 8 Glazing VT (0.55) Glazing VT (0.55) EUI 45.4 kBtu/ft 2 37.4 kBtu/ft 2 Annual PV Peak Load 448,765 kWh 448,765 kWh PV Panels to reach net zero 270% Roof Area 230% Roof Area EUI breakdown The lighting use density: 6.7 kBtu/ft 2 The computer use density: 8 kBtu/ft 2 The DHW use density: 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 Cooling load EUI: 6.5 kBtu/ft 2 Heating load EUI: 4.9 kBtu/ft 2 Fan power EUI: 5.8 kBtu/ft 2 392 Total EUI Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 6.5 kBtu/ft 2 + 4.9 kBtu/ft 2 + 5.8 kBtu/ft 2 = 37.4 kBtu/ft 2 (Figure 6- 7). Figure 6-7 Harbin Base Model and Integration 2 EUI Comparison Total EUI was reduced by 18% which resulted from the application of seven passive design strategies, WWR 20%, window SHGC 0.12, window U=0.3 Btu/h ft 2 F, wall insulation U=0.016 Btu/h ft 2 F, roof insulation U=0.019 Btu/h ft 2 F, orientation (long side facing southwest), and glazing VT 0.32; and the application of heat recovery system. For EUI breakdown, cooling load was reduced by 1.3 kBtu/ft 2 (17%); heating load was reduced by 4.8 kBtu/ft 2 (38%); fan power was reduced by 1.9 kBtu/ft 2 (25%). 6.7 6.7 8 8 1.7 1.7 3.8 3.8 7.8 6.5 9.7 4.9 7.7 5.8 0 5 10 15 20 25 30 35 40 45 50 Base Model Integration 2 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -18% 393 Compared to the base model, the lower WWR, window SHGC and window U-value could reduce the solar heat gain. As the results shown in chapter 5A, WWR 20 reduced 2.4% of total EUI, window SHGC 0.12 reduced 0.9% of total EUI and window U-value reduced 0.7% of total EUI. However, lower WWR reduced the area of window, which decreased the effect of window SHGC and window U-value. Thus, the application of those three could not achieve the performance as the composition of them applied individually. As the increase of wall area and the lower U-value of wall insulation, less heat conduction between the indoor and outdoor spaces. Orientation could decrease 0.2% of total EUI of base model by rotating the building to reduce the solar heat gain through envelop. Nevertheless, the reduction of window area and the increase of wall insulation made it less effective on energy consumption. But the application of heat recovery system highly reduced the energy consumption on fan power, cooling, and heating load as its recovery of waste heat. 6.2.3 Harbin Integration 3 High WWR is always considered as an option to have better view for the occupants. However, it will increase solar heat gain for the indoor space. Integration 3 included WWR 60% to improve the view of building and low window SHGC and U-value reduce the sunlight penetration (Table 6-8). 394 Table 6-8 Harbin Base Model and Integration 3 Harbin Base Model Integration 3 1 HVAC System (VAV-Reheat) VAV-Reheat System with Heat Recovery 2 WWR (40%) WWR (60%) 3 Window SHGC (0.22) Window SHGC (0.12) 4 Window (U=0.46 Btu/h ft 2 F) Window (U=0.30 Btu/h ft 2 F) 5 Wall Insulation (U=0.069 Btu/h ft 2 F) Wall Insulation (U=0.069 Btu/h ft 2 F) 6 Roof Insulation (U=0.049 Btu/h ft 2 F) Roof Insulation (U=0.049 Btu/h ft 2 F) 7 Orientation (Long side facing north) Orientation (Long side facing north) 8 Glazing VT (0.55) Glazing VT (0.55) EUI 45.4 kBtu/ft 2 40.0 kBtu/ft 2 Annual PV Peak Load 448,765 kWh 448,765 kWh PV Panels to reach net zero 270% Roof Area 240% Roof Area EUI breakdown The lighting use density: 6.7 kBtu/ft 2 The computer use density: 8 kBtu/ft 2 The DHW use density: 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 Cooling load EUI: 6.1 kBtu/ft 2 Heating load EUI: 7.4 kBtu/ft 2 Fan power: 6.3 kBtu/ft 2 395 Total EUI Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 6.7 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 6.1 kBtu/ft 2 + 7.4 kBtu/ft 2 + 6.3 kBtu/ft 2 = 40.0kBtu/ft 2 (Figure 6-8). Figure 6-8 Harbin Base Model and Integration 3 EUI Comparison Total EUI was reduced by 11% which resulted from integration 3. For EUI breakdown, cooling load was reduced by 1.7 kBtu/ft 2 (22%); heating load was reduced by 2.3 kBtu/ft 2 (24%); fan power was reduced by 1.4 kBtu/ft 2 (18%). Even through it consumes more energy, 60% of WWR is usually adopted in the design to provide better view for the occupancy. Integration 3 adopted three passive design strategies, WWR 60%, window SHGC 0.12, window U=0.3 Btu/h ft 2 F, and heat recovery system. WWR 60% increased the total EUI of base model by 5.3% by increasing fan power, cooling and heating load as mentioned in chapter 5B. Thus, low value of window SHGC 6.7 6.7 8 8 1.7 1.7 3.8 3.8 7.8 6.1 9.7 7.4 7.7 6.3 0 5 10 15 20 25 30 35 40 45 50 Base Model Integration 3 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -11% 396 and window U=0.3 Btu/h ft 2 F were kept as integration 1 and 2 to decrease solar penetration through window. Roof and wall insulation were not selected because their slight effect in climate of Los Angeles and reduced wall area. But the application of heat recovery system highly reduced the energy consumption on fan power, cooling, and heating load as its recovery of waste heat. As the integration 2 and 3 could not get net zero and PV panels on the roof turned out to be 230 % and 240% of roof area. Therefore, the integration 4 was tried with overhang and LED lighting fixtures, even though it was not studied previously in chapter 4 and 5. 6.2.4 Harbin Integration 4 The HVAC system of base model is VAV-Reheat system. Application with heat recover system could recover the waste heat of system to reduce the cooling and heating demand of model. Based on the application of integration 2, the replacement of LED lighting fixtures, plus overhang consisted of the integration 4 (Table 6-9). Integration 4 shown the best result could be achieve in this thesis for Los Angeles. 397 Table 6-9 Harbin Base Model and Integration 4 Harbin Base Model Integration 4 1 HVAC System (VAV-Reheat) VAV-Reheat System with Heat Recovery 2 WWR (40%) WWR (20%) 3 Window SHGC (0.22) Window SHGC (0.12) 4 Window (U=0.46 Btu/h ft 2 F) Window (U=0.3 Btu/h ft 2 F) 5 Wall Insulation (U=0.069 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) 6 Roof Insulation (U=0.049 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) 7 Orientation (Long side facing north) Orientation (Long side facing Southwest) 8 Glazing VT (0.32) Glazing VT (0.32) 9 Fluorescent Light Fixture LED Lighting Fixture 10 No Overhang With Overhang EUI 45.4 kBtu/ft 2 33.9 kBtu/ft 2 Annual PV Peak Load 448,765 kWh 448,765 kWh PV Panels to reach net zero 270% Roof Area 200% Roof Area EUI breakdown The lighting use density: 3.8 kBtu/ft 2 The computer use density: 8 kBtu/ft 2 The DHW use density: 1.7 kBtu/ft 2 The pump use density: 3.8 kBtu/ft 2 Cooling load EUI: 6.3 kBtu/ft 2 Heating load EUI: 4.9 kBtu/ft 2 398 Fan power EUI: 5.4 kBtu/ft 2 Total EUI Total EUI = lighting consumption + computer consumption + DHW consumption + pump consumption + cooling consumption + heating consumption + fan power = 3.8 kBtu/ft 2 + 8 kBtu/ft 2 + 1.7 kBtu/ft 2 + 3.8 kBtu/ft 2 + 6.3 kBtu/ft 2 + 4.9 kBtu/ft 2 + 5.4 kBtu/ft 2 = 33.9 kBtu/ft 2 (Figure 6- 9). Figure 6-9 Harbin Base Model and Integration 4 EUI Comparison Total EUI was reduced by 25% which resulted from the application of seven passive design strategies, WWR 20%, window SHGC 0.12, window U=0.3 Btu/h ft 2 F, wall insulation U=0.016 Btu/h ft 2 F, roof insulation U=0.019 Btu/h ft 2 F, orientation (long side facing southwest), and glazing VT 0.32; the application of heat recovery system; the replacement of fluorescent lighting fixtures by LED lighting fixtures; plus, the application of overhang. For EUI breakdown, cooling 6.7 3.8 8 8 1.7 1.7 3.8 3.8 7.8 6.3 9.7 4.9 7.7 5.4 0 5 10 15 20 25 30 35 40 45 50 Base Model Integration 4 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -25% 399 load was reduced by 1.5 kBtu/ft 2 (20%); heating load was reduced by 4.8 kBtu/ft 2 (38%); fan power was reduced by 2.3 kBtu/ft 2 (30%); lighting consumption was reduced by 2.9 kBtu/ft 2 (43%). Compared to the base model, the lower WWR, window SHGC and window U-value could reduce the solar heat gain. As the results shown in chapter 5A, WWR 20 reduced 2.4% of total EUI, window SHGC 0.12 reduced 0.9% of total EUI and window U-value reduced 0.7% of total EUI. However, lower WWR reduced the area of window, which decreased the effect of window SHGC and window U-value. Thus, the application of those three could not achieve the performance as the composition of them applied individually. As the increase of wall area and the lower U-value of wall insulation, less heat conduction between the indoor and outdoor spaces. Orientation could decrease 0.2% of total EUI of base model by rotating the building to reduce the solar heat gain through envelop. Nevertheless, the reduction of window area and the increase of wall insulation made it less effective on energy consumption. But the application of heat recovery system highly reduced the energy consumption on fan power, cooling, and heating load as its recovery of waste heat. In addition, the application of overhang reduced the solar heat gain of indoor space in summer and winter, so it reduced more on cooling load, slight on heating load. The replacement of fluorescent lighting fixtures with LED fixtures could reduce almost half of lighting EUI consumption. 6.2.5 Harbin Integration with Best Result Based on the comparison of Harbin base model, integration 1, integration 2, integration 3, and integration 4, integration resulted in the lowest EUI comparing to other integrations, which is 25% reduction on total EUI (Figure 6-10). 400 Figure 6-10 Harbin Base Model, Integration 1, Integration 2, Integration 3, and Integration 4, PV Generation Comparison The strategies of all the integrations, EUI of each integration, and area of PV panels were listed for comparison (Table 6-10). 6.7 6.7 6.7 6.7 3.8 15.0 8 8 8 8 8 1.7 1.7 1.7 1.7 1.7 3.8 3.8 3.8 3.8 3.8 7.8 8.1 6.5 6.1 6.3 9.7 6.1 4.9 7.4 4.9 7.7 6.8 5.8 6.3 5.4 0 5 10 15 20 25 30 35 40 45 50 Harbin Base Model Harbin Integration 1 Harbin Integration 2 Harbin Integration 3 Harbin Integration 4 PV Panels Energy Gerneration EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -9% -18% -11% -25% 401 Table 6-10 Harbin Base Model, Integration 1, Integration 2, Integration 3, and Integration 4 Strategies Harbin Base Model Integration 1 Integration 2 Integration 3 Integration 4 Best of the studied variables Best of the studied variables with heat recovery system Integration with the best result 1 HVAC System (VAV-Reheat) VAV-Reheat system VAV-Reheat System with Heat Recovery VAV-Reheat System with Heat Recovery VAV-Reheat System with Heat Recovery 2 WWR (40%) WWR (20%) WWR (20%) WWR (60%) WWR (20%) 3 Window SHGC (0.22) Window SHGC (0.12) Window SHGC (0.12) Window SHGC (0.12) Window SHGC (0.12) 4 Window (U=0.46 Btu/h ft 2 F) Window (U=0.3 Btu/h ft 2 F) Window (U=0.3 Btu/h ft 2 F) Window (U=0.30 Btu/h ft 2 F) Window (U=0.3 Btu/h ft 2 F) 5 Wall Insulation (U=0.069 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) Wall Insulation (U=0.069 Btu/h ft 2 F) Wall Insulation (U=0.016 Btu/h ft 2 F) 6 Roof Insulation (U=0.049 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) Roof Insulation (U=0.049 Btu/h ft 2 F) Roof Insulation (U=0.019 Btu/h ft 2 F) 7 Orientation (Long side facing north) Orientation (Long side facing Southwest) Orientation (Long side facing Southwest) Orientation (Long side facing north) Orientation (Long side facing Southwest) 8 Glazing VT (0.32) Glazing VT (0.55) Glazing VT (0.55) Glazing VT (0.32) Glazing VT (0.55) 9 Fluorescent Lighting Fixture Fluorescent Lighting Fixture Fluorescent Lighting Fixture Fluorescent Lighting Fixture LED Lighting Fixture 10 No Overhang No Overhang No Overhang No Overhang With Overhang EUI 45.4 kBtu/ft 2 41.2 kBtu/ft 2 37.4 kBtu/ft 2 40.0 kBtu/ft 2 33.9 kBtu/ft 2 Annual PV Peak Load 448,765 kWh 448,765 kWh 448,765 kWh 448,765 kWh 448,765 kWh PV Panels to reach net zero 270% Roof Area 250% Roof Area 230% Roof Area 240% Roof Area 200% Roof Area 402 Integration 4 consisted of all the passive design strategies which were studied in chapter 4 and 5 with the best values, WWR 20%, window SHGC 0.12, window U=0.3 Btu/h ft 2 F, wall U=0.016 Btu/h ft 2 F, roof U=0.019 Btu/h ft 2 F, long side of model facing southwest and glazing VT 0.32. Plus, overhang, the application with heat recovery system and LED lighting fixture. Compared to the base model, total EUI was reduced by 25% to 33.9 kBtu/ft 2 . Also, the area of PV panels was reduced to 200% from 270%. 6.3 Summary Comparing each design strategy, an integration of multiple design controls could result in more energy savings. Even though the design controls may have conflicts between each other. For example, higher WWR decreased the effectiveness of window SHGC and window U-value; on the other hand, higher WWR decreased the effectiveness of wall insulation, especially for the model in cold climate. Thus, the correlation between different strategies should be considered before the application. Compared with the passive design strategies, active design strategy, the HVAC system could provide much more energy savings when a more efficient system was applied. Heat recovery system reduces the energy demands by collecting waste heat from exhaust air. 403 6.3.1 Los Angeles and Harbin Integration 1 For integration 1, two of the same models were applied with the same integration in different weather conditions. The percentage of energy savings to the total energy reduction was similar, 7% and 9% (Figure 6-11). Figure 6-11 Los Angeles and Harbin Base Model and Integration 1 Comparison However, the change of EUI breakdown is different of model in Los Angeles and Harbin. Los Angeles model heating load reduced 0.2 kBtu/ft 2 (3%) while Harbin model reduced by 3.6 kBtu/ft 2 (37%). High temperature difference in summer resulted in U value of envelop material with high energy efficiency of Harbin model. Los Angeles model cooling load reduced 1.6 kBtu/ft 2 (14%) while Harbin model increased 0.3 kBtu/ft 2 (4%). It is because that U-value of envelop material have more evident effect on building heat external conduction of Harbin model. Thus, less heat transferred from indoor space to outdoor space which resulted in less heating load in winter, but more cooling load in summer. Fan power of both Los Angeles and Harbin models reduced by 12%. 6.7 6.7 6.7 6.7 8 8 8 8 1.7 1.7 1.7 1.7 3.8 3.8 3.8 3.8 12 10.4 7.8 8.1 0.8 0.6 9.7 6.1 8.4 7.4 7.7 6.8 0 5 10 15 20 25 30 35 40 45 50 Los Angeles Base Model Los Angeles Integration 1 Harbin Base Model Harbin Integration 1 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -7% -9% 404 6.3.2 Los Angeles and Harbin Integration 2 For integration 2, two of the same models were applied with the same integration in different weather conditions. The percentage of energy savings to the total energy reduction was similar, 16% and 18% (Figure 6-12). Figure 6-12 Los Angeles and Harbin Base Model and Integration 2 Comparison More energy saving on cooling, heating, and fan power because of the application of heat recovery system. The passive design strategies of integration 2 and integration 1 were the same, but the HVAC system was replaced. Thus, the energy saving difference of integration 1 and 2 was caused by heat recovery system. For Los Angeles EUI breakdown, cooling load was reduced by 4.0 kBtu/ft 2 (30%); heating load was reduced by 0.3 kBtu/ft 2 (38%); fan power was reduced by 1 kBtu/ft 2 (25%); for Harbin EUI breakdown, cooling load was reduced by 1.3 kBtu/ft 2 (17%); heating load was reduced by 4.8 kBtu/ft 2 (38%); fan power was reduced by 1.9 kBtu/ft 2 (25%). Heat recovery system reduced same percentage of energy reduction on heating and fan power but 6.7 6.7 6.7 6.7 8 8 8 8 1.7 1.7 1.7 1.7 3.8 3.8 3.8 3.8 12 8 7.8 6.5 0.8 0.5 9.7 4.9 8.4 6.3 7.7 5.8 0 5 10 15 20 25 30 35 40 45 50 Los Angeles Base Model Los Angeles Integration 2 Harbin Base Model Harbin Integration 2 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -16% -18% 405 with different exact amount of savings. But as mentioned that cooling load increased by 4% because of the integration of passive strategies integration, more cooling load could be reduced if it could be revised. 6.3.3 Los Angeles and Harbin Integration 3 For integration 3, two of the same models were applied with the same integration in different weather conditions The percentage of energy savings to the total energy reduction was similar, 12% and 11% (Figure 6-13). Figure 6-13 Los Angeles and Harbin Base Model and Integration 3 Comparison For Los Angeles EUI breakdown, cooling load was reduced by 3.1 kBtu/ft 2 (26%); heating load was reduced by 0.2 kBtu/ft 2 (25%); fan power was reduced by 1.6 kBtu/ft 2 (20%); for Harbin model EUI breakdown, cooling load was reduced by 1.7 kBtu/ft 2 (22%); heating load was reduced 6.7 6.7 6.7 6.7 8 8 8 8 1.7 1.7 1.7 1.7 3.8 3.8 3.8 3.8 12 8.9 7.8 6.1 0.8 0.6 9.7 7.4 8.4 6.8 7.7 6.3 0 5 10 15 20 25 30 35 40 45 50 Los Angeles Base Model Los Angeles Integration 3 Harbin Base Model Harbin Integration 3 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -12% -11% 406 by 2.3 kBtu/ft 2 (24%); fan power was reduced by 1.4 kBtu/ft 2 (18%). As the increase of window area of integration 3, models were affected more by local climate. More cooling load of Los Angeles model in summer and more heating load of Harbin model in winter. Accordingly, more cooling energy of Los Angeles model and more heating energy of Harbin model were reduced on both percentage and exact amount. 6.3.4 Los Angeles and Harbin Integration 4 For integration 4, two of the same models were applied with the same integration in different weather conditions. The percentage of energy savings to the total energy reduction was similar, 23% and 25% (Figure 6-14). Figure 6-14 Los Angeles and Harbin Base Model and Integration 4 Comparison 6.7 3.8 6.7 3.8 8 8 8 8 1.7 1.7 1.7 1.7 3.8 3.8 3.8 3.8 12 7.8 7.8 6.3 0.8 0.5 9.7 4.9 8.4 6.1 7.7 5.4 0 5 10 15 20 25 30 35 40 45 50 Los Angeles Base Model Los Angeles Integration 4 Harbin Base Model Harbin Integration 4 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -23% -25% 407 For Los Angeles EUI breakdown, cooling load was reduced by 4.2 kBtu/ft 2 (35%); heating load was reduced by 0.3 kBtu/ft 2 (38%); fan power was reduced by 2.3 kBtu/ft 2 (27%), lighting was reduced by 2.9 kBtu/ft 2 (43%). For Harbin EUI breakdown, cooling load was reduced by 1.5 kBtu/ft 2 (20%); heating load was reduced by 4.8 kBtu/ft 2 (38%); fan power was reduced by 2.3 kBtu/ft 2 (30%); lighting consumption was reduced by 2.9 kBtu/ft 2 (43%). More energy saving on cooling, heating, and fan power because of the application of heat recovery system. All the passive design strategies were the same as integration 2 except the application of overhang. The replacement of lighting fixtures significantly reduced energy consumption. 408 Chapter 7. Discussion and Future Work The objective of creating Net Zero Energy Buildings (NZEB) has been considered as a goal for the design of buildings in the last few years. It is adopted not only in new buildings but also retrofit design to achieve better performance. The different design strategies (active and passive) and other techniques applied to the building can reduce energy consumption. On-site renewable energy generation is also crucial to achieve an annual balance, zero energy consumption. Computer-based simulation is an effective method to quantify and examine the building energy performance before the building is actually built. The building energy model (BEM) simulation can be run with local climate conditions and building construction information through software such as IES VE. Unfortunately, using the case study buildings plus the chosen strategies and a reasonable amount of PVs, net zero status was not achieved in this thesis. 7.1 Discussion There is no standard definition of NZEB in the world now, however, it is adopted by policy maker in building energy codes in many countries to be beneficial to the environment. The development of NZEB is fast since the first prototype building created in 1950 (Ferrante and Cascella 2011). In addition, it has been proved to be functioning well in many different regions and climate zones. To achieve a NZEB, different design strategies should be applied to the building in the design stage, such as passive and active design strategies. Passive design strategies utilizing natural force to reduce building energy demand, including building orientation, envelope insulation (wall and roof U-factor), window parameters, natural ventilation and so on. Active design strategies including HVAC system, control system, geothermal heat exchange pump and so on. Also, the PV panels considered as a critical method to create renewable on-site energy generation, so the 409 building could achieve a net zero energy balance throughout the year. Energy simulation is the method to quantify the performance of the buildings before construction through the computer- based software, such as IES VE. The effect of each strategy of the building could be examined by energy modeling and the results of the designed energy performance could be perfect guidance on the construction and operation stages. The building energy codes create criterion requirements and guide construction to keep the buildings to have better energy performance. The standards in California like Title 24 ,ASHRAE 90.1 and IECC are functioning individually while the in China, all the cities and provinces should follow the national laws (Hu and Qiu 2019). Also, the difference between simulation and reality should be considered seriously, which could result in the building energy performance not as expected. The effect of the factors that could contribute to the better building energy performance were explored to decide the application of design strategies in chapter 2. Many of the passive design strategies depend on local weather conditions that have a strong effect on the energy consumption of the building and its HVAC system. The façade system has many of parameters that could affect building energy consumption, for example, WWR, window SHGC, insulation, and others. Natural ventilation can bring in the wind flow to indoor space for energy savings on mechanical ventilation and heat will be brought out of the indoor space to save energy on cooling load. Orientation could be very beneficial for some floor plans by adjust the solar heat gain through rotating facing direction. For active design strategies, HVAC system usually makes up half of building energy consumption, which could be highly reduced by utilizing high efficiency system, radiance system, and a heat recovery system. In addition, the challenges and limitations cannot be neglected in the process of pursuing NZEB. For example, the PV panels have its maximum amount of energy generation and it is highly depending on the amount of available solar radiation. Also, the proper 410 design for active and passive design strategies has significant influence on the building energy consumption. As energy modeling software becomes more accurate, it can help in designing more energy efficient buildings. However, the complexity of software programs and the difference between simulation and reality can result in results that are not entirely accurate. To study the effectiveness of different design strategies, two base models (four-story office building) were created for two climate zones (Los Angeles, CA and Harbin, China) to examine how specific design strategies can be applied in different weather conditions and how those effects building energy performance. The input, such as occupancy profile, construction materials, internal gains, and air exchange and so on were set up in IES VE. Lighting, computer, DHW, and pump energy consumption were calculated through a spreadsheet. Fan power, cooling, and heat load and EUI were considered as the metrics to evaluate the model energy performance. The passive design strategies were applied to the base model each at a time to test their effect on building energy consumption. PVWatts was used to study the electrical generation of PVs on the roof for different percentages of roof coverage. Combined with active design strategies, 8 integrations of all strategies were created to see if the achievement of NZEB was possible. They were listed in the tables with total EUI and corresponding area of PV panels to show the energy simulation results. The results of each design strategy in each variable were shown in with the EUI breakdown and total EUI in chapter 4. For each strategy, the total EUI and EUI breakdown of different variables (including base model) were shown in graphs with the change of EUI percentage. For different strategies, the best performance ones were shown in a graph with the change of percentage. The difference between each variable for the same design strategy and different strategies could be detected easily through all the graphs. In chapter 5, all the results were analyzed on efficiency of 411 different strategies and the values of their better performance. The difference of fan power, cooling and heating load were analyzed and why each EUI breakdown changed was explored too. In addition, a ranking list of design strategies efficiency from high to low was created for chapter 6, eight integrations of passive and active design strategies were applied to the base models in two climate zone (Los Angeles and Harbin) to achieve the best performance. 7.2 Study Results for Los Angeles and Harbin By utilizing IES VE to conduct energy simulation and spreadsheet to derive and calculate for base models, and the application of passive strategies, such as roof insulation, wall insulation, WWR, window U-value, window SHGC, orientation, and glazing VT (Figure 7-1). Because Title 24 was used to create the base model, the EUIs of the buildings are actually pretty good to begin with. That actually made the study more difficult and applying the different design strategies for energy reduction were not as effective as they would have been on a less stringent code compliant building. Figure 7-1 Los Angeles Base Model and Harbin Base Model 6.7 6.7 8 8 1.7 1.7 3.8 3.8 12 7.8 0.8 9.7 8.4 7.7 0 5 10 15 20 25 30 35 40 45 50 Los Angeles Base Model Harbin Base Model EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 412 The total EUI and EUI breakdown are different due to the different climates in Los Angeles versus Harbin. With warmer temperatures throughout the year in Los Angeles, the cooling load is high, but heating load is extremely low. The hot summer and cold winter in Harbin, heating and cooling load are both high and heating load is higher than cooling load. Also, the fan power in Los Angeles is higher than it in Harbin. The same passive strategy had the different or same performances for the models in different cities (Figure 7-2 & Figure 7-3). Figure 7-2 Los Angeles Model Best Strategies EUI Comparison Bar Chart 6.7 6.7 6.7 6.7 6.7 6.7 6.7 6.7 8 8 8 8 8 8 8 8 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 12 11 10.9 11.9 12 12 12 12 0.8 0.5 0.9 0.7 0.6 0.7 0.7 0.8 8.4 7.8 7.8 8.3 8.3 8.4 8.4 8.4 0 5 10 15 20 25 30 35 40 45 Base Model WWR 20% SHGC 0.12 Wall U0.016 Window U0.30 Roof U0.019 Orientation 135° VT 0.55 Strategies EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting 0.0% 0.2% 0.2% 0.7% 0.7% 4.0% 4.6% 413 Figure 7-3 Harbin Model Best Strategies EUI Comparison Bar Chart 1. For Los Angeles and Harbin, WWR was the most efficient strategy comparing to other passive design strategies because it could adjust the large amount of solar heat gain of indoor space 2. Window SHGC was more energy efficient in Los Angeles than it in Harbin. 3. The envelope insulation (window U-value, roof, and wall insulation) has slight influence on model EUI in Los Angeles, while in Harbin, it had relatively high performance. The high difference between indoor air temperature in Harbin, but in Los Angeles is not. Thus, more heat transferred through envelope in Harbin than Los Angeles. 4. The orientation of the model in bother cities only have slight effect as its symmetrical floor plan and similar length of long side and short side. 5. Glazing VT did not affect model EUI at all, as it only affect the visible light transferring through the window. However, the lighting energy consumption was assumed to be constant. The best integration for the two models in Los Angeles and Harbin was integration 4, the percentage of energy savings to the total energy reduction was similar, 23% and 25% (Figure 7- 4). 6.7 6.7 6.7 6.7 6.7 6.7 6.7 6.7 8 8 8 8 8 8 8 8 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 7.8 7.7 8.2 7.9 8 7.3 7.8 7.8 9.7 8.4 8.3 8.9 9 10 9.6 9.7 7.7 7.2 7.6 7.6 7.6 7.3 7.7 7.7 0 5 10 15 20 25 30 35 40 45 50 Base Model WWR 20% Window U0.3 Wall U0.016 Roof U0.019 SHGC 0.12 Orientation 135° Glazing VT EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -4.2% -2.4% -1.8% -1.3% -0.2% 0.0% -0.9% 414 Figure 7-4 Los Angeles and Harbin Base Model and Integration 4 Comparison Integration 4 consisted of all the passive design strategies that were studied in chapter 4 and 5 with the best values, WWR 20%, window SHGC 0.12, window U=0.3 Btu/h ft 2 F, wall U=0.016 Btu/h ft 2 F, roof U=0.019 Btu/h ft 2 F, long side of model facing southwest and glazing VT 0.32. Plus, overhang, the application heat recovery system, and and LED lighting fixtures were added because Integration 1 (the best of all the parameters actually studied in chapters 4 and 5) did not produce an net zero building with a reasonable amount of PV on the roof. Compared to the base model, total EUI was reduced by 23% to 31.7 kBtu/ft 2 . Also, the area of PV panels was reduced to 160% from 210%. Compared to the Harbin base model, total EUI was reduced by 25% to 33.9 kBtu/ft 2 . Also, the area of PV panels was reduced to 200% from 270%. The EUI of the models in Los Angeles and Harbin with Integration 4 are still higher than 30 kBtu/ft 2 and need more than 200% roof area of PV panels to achieve net zero. Unfortunately, using the subset of design strategies for the specific four-story case study building did not produce any net zero building options for any of the integrations discussed in chapter 6. 6.7 3.8 6.7 3.8 8 8 8 8 1.7 1.7 1.7 1.7 3.8 3.8 3.8 3.8 12 7.8 7.8 6.3 0.8 0.5 9.7 4.9 8.4 6.1 7.7 5.4 0 5 10 15 20 25 30 35 40 45 50 Los Angeles Base Model Los Angeles Integration 4 Harbin Base Model Harbin Integration 4 EUI Comparison (kBtu/sf) Fan Power Heating Cooling Pump DHW Equipment Lighting -23% -25% 415 7.3 Future Work Because of the time limitations, only some design strategies were examined, such as HVAC system, wall and roof insulation, window U-factor, window SHGC, glazing VT, WWR and orientation. More research and simulation could be done in the future in the possible directions are introduced in this section, which could be concluded into the future in short term and long term. It was originally intended to study more design variables. In the end some were omitted (Figure 7- 5, yellow text) and the bulk of the research focused on mainly façade driven factors such as wall and roof insulation, window characteristics, and orientation plus the HVAC system (Figure 7-5, black text). Figure 7-5 The Strategies Applied (Words in Black) and Strategies Not Applied (Words in Yellow) 416 7.3.1 Near Term Research Window overhangs are utilized to prevent too much solar heat gain, especially for the buildings in climate with strong sunlight throughout the year. It is an important feature that could adjust the sunlight penetration. Because of time limitations, the overhang was not examined through IES VE. The sun’s moving position results in the solar gain and sunlight angle for the windows in each direction is different, so it should be applied to the model in different overhang factors with the consideration of orientation. Natural ventilation is a passive design strategy that could reduce the energy for ventilation load which is also a function that provided in IES. Utilizing wind to provide air exchange could reduce the energy for mechanical ventilation that keeps minimum ventilation rate at 0.15cfm/sf. In addition, natural wind blows in the indoor space could bring out the heat in summer, which is also beneficial to reducing the heating demand of building. Smart control systems are the advanced technology that could control the building system on and off, such as lighting and ventilation. By detecting the occupancy’s activity through the sensors and controlling the system on and off to save energy. It is also could be modeled in IES. Lighting is an important part of energy consumption of office. Sufficient light should be provided to protect occupancy’s health of eyes. The energy efficiency of each lighting feature could reduce a certain amount of energy when all the lighting features summed together. In addition, a well- designed lighting distribution could be beneficial to reducing the numbers of lighting features. Daylight harvesting should also be considered, and with lighting load. The window factor, glazing VT, which could affect the sunlight penetrate through the window should be considered with 417 lighting load. However, only glazing VT was studied as the time limitation. A better result of could be derived if the lighting load was conducted with glazing VT. The geothermal heat exchange system is utilizing the constant temperature of the earth to heat or cool indoor space. It could be modeled in IES by finding the water loop heat pump system, creating a heat transfer loop with the well field temperature data in Eron. It is a great natural resource for cooling and heating the indoor space by utilizing the natural force instead of the electricity. The simulations were only conducted for a rectangle shape four-floor building. More work should be done with buildings in different shapes. With the different function of building, the energy performance, operation method and operation time are different. Thus, more simulations should be conducted for other commercial buildings and residential buildings to examine how same strategies could have different influence as the different building type. 7.3.2 Long Term Research (Energy) Most of the NZEBs are connecting to the grid and receive electricity from grid when the on-site energy generation is not enough to support the building energy usage. Many aspects related to this problem are expected to be solved. First, the electricity from grid may not be from renewable resources as most of them are generated by natural gas, some are using coal. If all the energy created by renewable resources, then whole cycle could be cleaner. Second, to be a NZEB, all the equipment in the building should consume electricity instead of natural gas or coal. However, it is very expensive to electrify the buildings, especially for the existing buildings. Another limitation of the NZEB is the limited energy generation of PV panels or other renewable energy resources. Even though the amount of electricity generation of PV panels has increased a lot for the last decades, it is a big limitation of on-site energy generation. The building energy consumption does 418 not need to be reduced that much if the PV panels could create more. Although better PVs could help, it does not excuse the responsibility of the building itself to be made more energy efficient, in addition to the HVAC system, and equipment. Although entirely outside the scope of this project, more long-term research needs to be done on the correspondence of simulation to actual building results and metering of real buildings. This work is being done by others and would help in the future for energy consultants to be able to better predict through simulation the performance of the buildings, especially with regards to integration of different strategies and with the actions of the occupants. 7.4 Conclusion NZEB is a goal that can be achieved for many buildings. The key is to keep the annual energy balance of building energy demand and generation of on-site renewable energy. The integration of proper design strategies contributes to that balance, such as low U-value envelope insulation, high performance window, orientation, high efficiency HVAC system. However, for different buildings in different climate conditions, the strategies used will have different performance characteristics, and some strategies may conflict with others. Thus, it takes effort to explore and find the optimized integration of design strategies. As the concept of NZEB is widely accepted by more people and countries, the challenges, and limitations of NZEB cannot be neglected. The Bullitt Center is a successful case study as a NZEB. Different strategies are shown from creating a net zero building from heating and cooling, lighting, occupant considerations, and eventually PV on the roof and façade to make up the energy differences (Figure 7-6). The EUI was reduced from 92 kBtu/ft 2 to 42 kBtu/ft 2 by applying high performance glass, low infiltration walls, heat recovery system, ground source heat pump, radiant 419 system and so on. Then the changing of lighting fixtures resulted in a 10 kBtu/ft 2 reduction on EUI to 32 kBtu/ft 2 . The fourth stage from 32 kBtu/ft 2 to 16 kBtu/ft 2 depends on the occupants’ behavior. Thus, it could be concluded that for the stages of EUI reducing to 32 kBtu/ft 2 from typical baseline office EUI could be controlled by architects and engineers. However, for the stage from 32 kBtu/ft 2 to lower values, it is highly depending on the occupants. Figure 7-6 The Path to Net Zero Energy for The Bullitt Center (Peña, 2014) In this thesis, the emphasis was only on stage two (heating and cooling) and a preliminary study of stage 3 (lighting). For the models with integration 4 in Los Angeles and Harbin, the reduced EUI were 31.7 kBtu/ft 2 and 33.9 kBtu/ft 2 . Also, as the large amount of the internal gain, the effect of design strategies was not that evident. Additional studies are needed to take into account the role of further strategies for heating and cooling including ventilation and more aggressive lighting goals. It was learned that the building and its façade and HVAC systems, however well optimized, 420 might not be enough for a net zero building goal. For the case study building used in Los Angeles and Harbin, more study should have gone into orientation, shading, lighting and daylighting, and the role of the occupant with regards to set point changes, choice of low energy equipment, other loads in the building. Buildings may need more than just building façade optimization and PVs to reach a net zero goal. 421 References Aditya, L., T. M.I. Mahlia, B. Rismanchi, H. M. Ng, M. H. Hasan, H. S.C. Metselaar, Oki Muraza, and H. B. Aditiya. 2017. “A Review on Insulation Materials for Energy Conservation in Buildings.” Renewable and Sustainable Energy Reviews 73 (June): 1352–65. https://doi.org/10.1016/J.RSER.2017.02.034. Aelenei, Laura, Daniel Aelenei, Helder Gonçalves, Roberto Lollini, Eike Musall, Alessandra Scognamiglio, Eduard Cubi, and Massa Noguchi. 2013. “Design Issues for Net Zero-Energy Buildings.” Open House International 38 (3): 7–14. https://doi.org/10.1108/OHI-03-2013- B0002. Ayompe, L. M., and A. Duffy. 2014. “An Assessment of the Energy Generation Potential of Photovoltaic Systems in Cameroon Using Satellite-Derived Solar Radiation Datasets.” Sustainable Energy Technologies and Assessments 7 (September): 257–64. https://doi.org/10.1016/J.SETA.2013.10.002. Bot, Karol, Nuno M.M. Ramos, Ricardo M.S.F. Almeida, Pedro F. Pereira, and Cláudio Monteiro. 2019. “Energy Performance of Buildings with On-Site Energy Generation and Storage – An Integrated Assessment Using Dynamic Simulation.” Journal of Building Engineering 24 (July): 100769. https://doi.org/10.1016/J.JOBE.2019.100769. “Case Studies - Schöck Bauteile GmbH.” n.d. Accessed October 31, 2021. https://www.schoeck.com/en/case-studies/nowon-energy-zero-house-ez-house. “Commercial Buildings and Onsite Renewable Energy | ENERGY STAR Buildings and Plants | ENERGY STAR.” n.d. Accessed November 1, 2021. https://www.energystar.gov/buildings/about_us/datatrends_research/renewable_report. D’Agostino, Delia, and Livio Mazzarella. 2019. “What Is a Nearly Zero Energy Building? Overview, Implementation and Comparison of Definitions.” Journal of Building Engineering 21 (January): 200–212. https://doi.org/10.1016/J.JOBE.2018.10.019. Daaboul, Jessica, Kamel Ghali, and Nesreen Ghaddar. 2018. “Mixed-Mode Ventilation and Air Conditioning as Alternative for Energy Savings: A Case Study in Beirut Current and Future Climate.” Energy Efficiency 11 (1): 13–30. https://doi.org/10.1007/S12053-017-9546- Z/FIGURES/9. Department of Energy, Us. 2015. “AN ASSESSMENT OF ENERGY TECHNOLOGIES AND RESEARCH OPPORTUNITIES Chapter 5: Increasing Efficiency of Building Systems and Technologies.” Efficiency, Energy. 2019. “Compliance Manual for the 2019 Building,” no. December 2018. “Energy Commission Adopts Updated Building Standards to Improve Efficiency, Reduce Emissions From Homes and Businesses.” n.d. Accessed November 2, 2021. https://www.energy.ca.gov/news/2021-08/energy-commission-adopts-updated-building- standards-improve-efficiency-reduce-0. “Energy Use Intensity - SLEB.” n.d. Accessed December 9, 2021. https://www.sleb.sg/News/NewsDetails/499. Fasiuddin, M., and I. Budaiwi. 2011. “HVAC System Strategies for Energy Conservation in Commercial Buildings in Saudi Arabia.” Energy and Buildings 43 (12): 3457–66. https://doi.org/10.1016/J.ENBUILD.2011.09.004. Ferrante, A., and M. T. Cascella. 2011. “Zero Energy Balance and Zero On-Site CO2 Emission Housing Development in the Mediterranean Climate.” Energy and Buildings 43 (8): 2002– 10. https://doi.org/10.1016/J.ENBUILD.2011.04.008. 422 “File:California Electricity Generation Sources Pie Chart.Svg - Wikimedia Commons.” n.d. Accessed March 5, 2022. https://commons.wikimedia.org/wiki/File:California_Electricity_Generation_Sources_Pie_ Chart.svg. Franconi, Ellen, Blake Herrschaft -bherrschaft, rmiorg Craig Schiller -cschiller, and rmiorg Robert Hutchinson -hhutchinson. 2013. “BUILDING ENERGY MODELING FOR OWNERS AND MANAGERS A GUIDE TO SPECIFYING AND SECURING SERVICES.” “Frederick Fisher & Partners’ Addition to Santa Monica City Hall Touts Its Net-Zero Bona Fides.” n.d. Accessed November 17, 2021. https://www.archpaper.com/2020/10/frederick-fisher- partners-santa-monica-city-services-touts-its-net-zero-bona-fides/. “Geothermal Heat Pumps | Department of Energy.” n.d. Accessed November 1, 2021. https://www.energy.gov/eere/geothermal/geothermal-heat-pumps. Ghosh, Amrita, and Subhasis Neogi. 2018. “Effect of Fenestration Geometrical Factors on Building Energy Consumption and Performance Evaluation of a New External Solar Shading Device in Warm and Humid Climatic Condition.” Solar Energy 169 (July): 94–104. https://doi.org/10.1016/J.SOLENER.2018.04.025. Han, Tian, Qiong Huang, Anxiao Zhang, and Qi Zhang. 2018. “Simulation-Based Decision Support Tools in the Early Design Stages of a Green Building—A Review.” Sustainability 2018, Vol. 10, Page 3696 10 (10): 3696. https://doi.org/10.3390/SU10103696. Harish, V. S.K.V., and Arun Kumar. 2016. “A Review on Modeling and Simulation of Building Energy Systems.” Renewable and Sustainable Energy Reviews 56 (April): 1272–92. https://doi.org/10.1016/J.RSER.2015.12.040. Hasan, Ala, Mika Vuolle, and Kai Sirén. 2008. “Minimisation of Life Cycle Cost of a Detached House Using Combined Simulation and Optimisation.” Building and Environment 43 (12): 2022–34. https://doi.org/10.1016/J.BUILDENV.2007.12.003. Hashemi, Arman, and Narguess Khatami. 2017. “Effects of Solar Shading on Thermal Comfort in Low-Income Tropical Housing.” Energy Procedia 111 (March): 235–44. https://doi.org/10.1016/J.EGYPRO.2017.03.025. “Home Page-California Energy Commission.” n.d. Accessed March 5, 2022. https://www.energy.ca.gov/. Hong, Authors, T Chang, and WK Lin. 2013. “Lawrence Berkeley National Laboratory Recent Work Title A Sensitivity Study of Building Performance Using 30-Year Actual Weather Data Publication Date.” Hu, Ming, and Yueming Qiu. 2019. “A Comparison of Building Energy Codes and Policies in the USA, Germany, and China: Progress toward the Net-Zero Building Goal in Three Countries.” Clean Technologies and Environmental Policy 21 (2): 291–305. https://doi.org/10.1007/S10098-018-1636-X. Ihara, Takeshi, Arild Gustavsen, and Bjørn Petter Jelle. 2015. “Effect of Facade Components on Energy Efficiency in Office Buildings.” Applied Energy 158 (November): 422–32. https://doi.org/10.1016/J.APENERGY.2015.08.074. “International - U.S. Energy Information Administration (EIA).” n.d. Accessed November 26, 2021. https://www.eia.gov/international/analysis/country/CHN. Kaasalainen, Tapio, Antti Mäkinen, Taru Lehtinen, Malin Moisio, and Juha Vinha. 2020. “Architectural Window Design and Energy Efficiency: Impacts on Heating, Cooling and Lighting Needs in Finnish Climates.” Journal of Building Engineering 27 (January): 100996. https://doi.org/10.1016/J.JOBE.2019.100996. 423 Kämpf, Jérôme Henri, Michael Wetter, Darren Robinson, Je´roˆme Je´roˆ, Je´roˆme Henri, and Ka¨mpf Ka¨mpf. 2010. “A Comparison of Global Optimization Algorithms with Standard Benchmark Functions and Real-World Applications Using EnergyPlus.” Http://Dx.Doi.Org/10.1080/19401490903494597 3 (2): 103–20. https://doi.org/10.1080/19401490903494597. King, Jennifer, and Christopher Perry. 2017. “Smart Buildings: Using Smart Technology to Save Energy in Existing Buildings.” Li, Dahua, Zhang Kun, and Qiang Gao. 2018. “Zero Energy Consumption LED Intelligent Lighting System Based on the Technology of PoE.” Chinese Control Conference, CCC 2018- July (October): 7639–43. https://doi.org/10.23919/CHICC.2018.8483337. Li, Jun, and Bin Shui. 2015. “A Comprehensive Analysis of Building Energy Efficiency Policies in China: Status Quo and Development Perspective.” Journal of Cleaner Production 90 (March): 326–44. https://doi.org/10.1016/J.JCLEPRO.2014.11.061. Li, Xiwang, and Jin Wen. 2014. “Review of Building Energy Modeling for Control and Operation.” Renewable and Sustainable Energy Reviews 37 (September): 517–37. https://doi.org/10.1016/J.RSER.2014.05.056. Luthander, Rasmus, Annica M. Nilsson, Joakim Widén, and Magnus Åberg. 2019. “Graphical Analysis of Photovoltaic Generation and Load Matching in Buildings: A Novel Way of Studying Self-Consumption and Self-Sufficiency.” Applied Energy 250 (September): 748– 59. https://doi.org/10.1016/J.APENERGY.2019.05.058. Nasruddin, Sholahudin, Pujo Satrio, Teuku Meurah Indra Mahlia, Niccolo Giannetti, and Kiyoshi Saito. 2019. “Optimization of HVAC System Energy Consumption in a Building Using Artificial Neural Network and Multi-Objective Genetic Algorithm.” Sustainable Energy Technologies and Assessments 35 (October): 48–57. https://doi.org/10.1016/J.SETA.2019.06.002. “National Impact of ANSI/ASHRAE/IES Standard 90.1-2016 (Conference) | OSTI.GOV.” n.d. Accessed November 2, 2021. https://www.osti.gov/biblio/1797800. “Net Energy Metering (NEM) and Your Bill.” n.d. Accessed October 31, 2021. https://www.pge.com/en_US/residential/solar-and-vehicles/green-energy-incentives/solar- and-renewable-metering-and-billing/net-energy-metering-program-tracking/understand-net- energy-metering.page. “Net Zero Energy Buildings | WBDG - Whole Building Design Guide.” n.d. Accessed March 5, 2022. https://www.wbdg.org/resources/net-zero-energy-buildings. Nguyen, Anh Tuan. 2013. “Sustainable Housing in Vietnam: Climate Responsive Design Strategies to Optimize Thermal Comfort,” June. https://orbi.uliege.be/handle/2268/147530. Nguyen, Anh Tuan, Sigrid Reiter, and Philippe Rigo. 2014. “A Review on Simulation-Based Optimization Methods Applied to Building Performance Analysis.” Applied Energy 113 (January): 1043–58. https://doi.org/10.1016/J.APENERGY.2013.08.061. Pacheco, R., J. Ordóñez, and G. Martínez. 2012. “Energy Efficient Design of Building: A Review.” Renewable and Sustainable Energy Reviews 16 (6): 3559–73. https://doi.org/10.1016/J.RSER.2012.03.045. “Passive Design Strategies | Metal Architecture.” n.d. Accessed October 31, 2021. https://www.metalarchitecture.com/articles/passive-design-strategies. Pérez-Lombard, Luis, José Ortiz, and Christine Pout. 2008. “A Review on Buildings Energy Consumption Information.” Energy and Buildings 40 (3): 394–98. https://doi.org/10.1016/J.ENBUILD.2007.03.007. 424 “Photovoltaic Energy Factsheet | Center for Sustainable Systems.” n.d. Accessed November 8, 2021. https://css.umich.edu/factsheets/photovoltaic-energy-factsheet. Pisello, Anna Laura, Veronica Lucia Castaldo, John Eric Taylor, and Franco Cotana. 2016. “The Impact of Natural Ventilation on Building Energy Requirement at Inter-Building Scale.” Energy and Buildings 127 (September): 870–83. https://doi.org/10.1016/J.ENBUILD.2016.06.023. “Powerhouse Kjørbo.” n.d. Accessed October 31, 2021. https://www.powerhouse.no/en/prosjekter/powerhouse-kjorbo/. Raji, Babak, Martin J. Tenpierik, and Andy Van Den Dobbelsteen. 2015. “The Impact of Greening Systems on Building Energy Performance: A Literature Review.” Renewable and Sustainable Energy Reviews 45 (May): 610–23. https://doi.org/10.1016/J.RSER.2015.02.011. Sartori, Igor, Assunta Napolitano, and Karsten Voss. 2012. “Net Zero Energy Buildings: A Consistent Definition Framework.” Energy and Buildings 48 (May): 220–32. https://doi.org/10.1016/J.ENBUILD.2012.01.032. Sawaqed, Naseem M., Yousef H. Zurigat, and Hilal Al-Hinai. 2005. “A Step-by-Step Application of Sandia Method in Developing Typical Meteorological Years for Different Locations in Oman.” International Journal of Energy Research 29 (8): 723–37. https://doi.org/10.1002/ER.1078. Shafique, Muhammad, Xiaowei Luo, and Jian Zuo. 2020. “Photovoltaic-Green Roofs: A Review of Benefits, Limitations, and Trends.” Solar Energy 202 (May): 485–97. https://doi.org/10.1016/J.SOLENER.2020.02.101. “Simulation Versus Reality - Insights and Inspirations.” n.d. Accessed March 5, 2022. https://www.usglassmag.com/insights/2021/06/simulation-versus-reality/. Solangi, K. H., M. R. Islam, R. Saidur, N. A. Rahim, and H. Fayaz. 2011. “A Review on Global Solar Energy Policy.” Renewable and Sustainable Energy Reviews 15 (4): 2149–63. https://doi.org/10.1016/J.RSER.2011.01.007. Sun, Xiaonuan, Zhonghua Gou, and Stephen Siu Yu Lau. 2018. “Cost-Effectiveness of Active and Passive Design Strategies for Existing Building Retrofits in Tropical Climate: Case Study of a Zero Energy Building.” Journal of Cleaner Production 183 (May): 35–45. https://doi.org/10.1016/J.JCLEPRO.2018.02.137. Troup, Luke, Robert Phillips, Matthew J. Eckelman, and David Fannon. 2019. “Effect of Window- to-Wall Ratio on Measured Energy Consumption in US Office Buildings.” Energy and Buildings 203 (November): 109434. https://doi.org/10.1016/J.ENBUILD.2019.109434. “U.S. Energy System Factsheet | Center for Sustainable Systems.” n.d. Accessed November 8, 2021. https://css.umich.edu/factsheets/us-energy-system-factsheet. “What Are the Rules? – Think | Architect.” n.d. Accessed October 31, 2021. https://thinkarchitect.wordpress.com/2016/01/25/what-are-the-rules/. “Workbook: Energy Efficiency Benchmarking Dashboard.” n.d. Accessed March 5, 2022. https://tableau.cnra.ca.gov/t/CNRA_CEC/views/EnergyEfficiencyBenchmarkingDashboard /BenchmarkingDashboard?iframeSizedToWindow=true&%3Aembed=y&%3AshowAppBa nner=false&%3Adisplay_count=no&%3AshowVizHome=no&%3AshowShareOptions=fal se. Wulfinghoff, Donald R, Rawal Rajan, Vishal Garg, and Jyotirmay Mathur. n.d. “EnErgy COnSErVATIOn BUILDIng CODE TIP SHEET EnErgy SImULATIOn Credits.” Accessed March 5, 2022. www.eco3.org. Yu, Zhongqi, Zhonghua Gou, Feng Qian, Jiayan Fu, and Yiqi Tao. 2019. “Towards an Optimized 425 Zero Energy Solar House: A Critical Analysis of Passive and Active Design Strategies Used in Solar Decathlon Europe in Madrid.” Journal of Cleaner Production 236 (November): 117646. https://doi.org/10.1016/J.JCLEPRO.2019.117646. “Zero Energy Buildings | Department of Energy.” n.d. Accessed March 5, 2022. https://www.energy.gov/eere/buildings/zero-energy-buildings. “Zero Net Energy.” n.d. Accessed March 5, 2022. https://www.cpuc.ca.gov/industries-and- topics/electrical-energy/demand-side-management/energy-efficiency/zero-net-energy. Zou, Patrick X.W., Dipika Wagle, and Morshed Alam. 2019. “Strategies for Minimizing Building Energy Performance Gaps between the Design Intend and the Reality.” Energy and Buildings 191 (May): 31–41. https://doi.org/10.1016/J.ENBUILD.2019.03.013.
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Creator
Wu, Haotian
(author)
Core Title
Net zero energy building: the integration of design strategies and PVs for zero-energy consumption
School
School of Architecture
Degree
Master of Building Science
Degree Program
Building Science
Degree Conferral Date
2022-05
Publication Date
04/21/2022
Defense Date
03/09/2022
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Building Energy Model (BEM),energy consumption,energy simulation,IES VE,Net Zero Energy Building (NZEB),OAI-PMH Harvest,Sustainable Design
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Kensek, Karen (
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UC111102242
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Wu, Haotian
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Tags
Building Energy Model (BEM)
energy consumption
energy simulation
IES VE
Net Zero Energy Building (NZEB)