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Simulation-based electric lighting control algorithm: integrating daylight simulation with daylight-linked lighting control

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Simulation-based electric lighting control algorithm: integrating daylight simulation with daylight-linked lighting control

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Content SIMULATION-BASED ELECTRIC LIGHTING CONTROL ALGORITHM: INTEGRATING DAYLIGHT SIMULATION WITH DAYLIGHT-LINKED LIGHTING CONTROL by Shaobo Yang 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 Shaobo Yang ii ACKNOWLEDGMENTS First, I would like to express my deepest gratitude to my parents, who have been supported by two years of study at USC. Without their support, I won’t even be here. I would also like to express my special thanks to my committee chair, Professor Karen Kensek, who has spent most of the time with me on my thesis, provided me guidance, and supported me throughout the research. When I met difficulties, Karen always responded to me in time. Moreover, Karen’s patience towards work, dedication, and enthusiasm for research and education will always inspire me for my whole life. I would also like to express my appreciation and thanks to my committee members Professor Marc Schiler and Professor Kyle Knois provided valuable advice and great support for my thesis. I also want to express my great thanks to my girlfriend Junyi Bu, who provided me with the resource to learn advanced math, especially linear matrix programming, and guided me in abstracting the data as mathematical problems. Lastly, I would like to thank all the students and faculty in the MBS family for supporting and helping each other in the two years of the program. Mainly, I want to thank Vicky helped me with the Rhino model transformed from the Revit point cloud model, and Dejian taught me the Grasshopper data structure. iii TABLE OF CONTENTS ACKNOWLEDGMENTS ........................................................................................................... ii LIST OF TABLES .................................................................................................................... viii LIST OF FIGURES .................................................................................................................... ix ABSTRACT ............................................................................................................................ xxiii Chapter 1 ...................................................................................................................................... 1 1. Introduction .......................................................................................................................... 1 1.1. Electric Lighting ....................................................................................................... 2 1.1.1. Lighting Energy Consumption .............................................................................. 2 1.1.2. Building Lighting Code ......................................................................................... 4 1.2. Introduction to Lighting Controls ........................................................................... 11 1.2.1. Smart Lighting Control Systems ......................................................................... 11 1.2.2. Light Sensor ........................................................................................................ 13 1.2.3. Building Automation System .............................................................................. 21 1.3. Daylighting ............................................................................................................. 24 1.3.1. Daylighting Impacts on Well-Being and Performance ....................................... 25 1.3.2. Daylighting as an alternative to electric lighting ................................................ 27 1.3.3. How Daylighting Can Work with Lighting Controls .......................................... 28 1.4. Daylight Performance Predictions .......................................................................... 29 1.4.1. Daylight Availability Metrics.............................................................................. 30 1.4.2. Daylight Simulation Software Overview ............................................................ 37 1.5. Rhinoceros 3D & Grasshopper ............................................................................... 43 1.5.1. Ladybug and Honeybee for Grasshopper ............................................................ 45 1.5.2. Python in Grasshopper ........................................................................................ 49 1.5.3. Microsoft Visual Studio ...................................................................................... 51 1.5.4. Microsoft Power BI ............................................................................................. 51 1.6. Summary ................................................................................................................. 53 Chapter 2 .................................................................................................................................... 54 2. Background and Literature Review ................................................................................... 54 2.1. Lighting System Design .......................................................................................... 54 iv 2.1.1. Light-Emitting Diode (LED) ............................................................................... 58 2.1.2. Photosensor Choice and Placement .................................................................... 62 2.1.3. Demand Response (DR) ...................................................................................... 63 2.2. Daylight-linked Lighting Controls (DLCs) ............................................................ 64 2.2.1. Daylight-linked Lighting Switching Controls ..................................................... 67 2.2.2. Daylight-linked Dimming Controls .................................................................... 70 2.2.3. Energy Savings from Daylight-linked Controls .................................................. 73 2.3. Daylight-linked Systems Based on the Algorithm of Control ................................ 75 2.3.1. Closed-loop Daylight- linked control systems .................................................... 78 2.3.2. Open-loop Daylight-linked Control Systems ...................................................... 80 2.3.3. Closed-loop and Open-loop Daylight-linked Control Comparison .................... 84 2.4. Daylighting Simulation ........................................................................................... 85 2.4.1. Daylighting Simulation Elements ....................................................................... 85 2.4.2. Radiance and its Calculations ............................................................................. 88 2.4.3. Accuracies of Radiance-based Daylight Simulation ........................................... 90 2.5. Summary ................................................................................................................. 95 Chapter 3 .................................................................................................................................... 97 3. Methodology ...................................................................................................................... 97 3.1. Methodology Overview .......................................................................................... 98 3.1.1. Existing Building Model (see section 3.2 for further details) ............................. 99 3.1.2. Daylighting and Lighting Data Acquisition(see section 3.3 for further details)100 3.1.3. Lighting Control Algorithm (see section 3.4 for further details) ...................... 100 3.1.4. Data Visualization Dashboard (see section 3.5 for further details)................... 101 3.2. Existing Building Model ....................................................................................... 101 3.2.1. Geometry Division ............................................................................................ 103 3.2.2. Simulation Preparation ...................................................................................... 104 3.3. Daylighting & Lighting Data Acquisition ............................................................ 111 3.3.1. Daylighting Simulation ..................................................................................... 112 3.3.2. Lighting Simulation........................................................................................... 117 3.3.3. Data Processing ................................................................................................. 124 3.4. Lighting Control Algorithm .................................................................................. 125 3.4.1. Programming Environment ............................................................................... 127 3.4.2. Switch On/Off control Algorithm ..................................................................... 131 3.4.3. Dimming Control Algorithm ............................................................................. 140 v 3.5. Data Visualization Dashboard .............................................................................. 144 3.5.1. Lighting Energy Comparison ............................................................................ 147 3.5.2. Data Visualization ............................................................................................. 152 3.6. Grasshopper Flowchart ......................................................................................... 158 3.7. Summary ............................................................................................................... 160 Chapter 4 .................................................................................................................................. 162 4. Program Development ..................................................................................................... 162 4.1. Key Customized Components in Grasshopper ..................................................... 162 4.1.1. Simulation Preparation ...................................................................................... 163 4.1.2. Simulation Parameters Setup ............................................................................ 166 4.1.3. Data Storage & Formatting ............................................................................... 167 4.1.4. Lighting Control Algorithm .............................................................................. 170 4.1.5. Data Visualization ............................................................................................. 174 4.2. Workflow in Visual Studio ................................................................................... 177 4.2.1. Importing Modules ............................................................................................ 178 4.2.2. Data Input .......................................................................................................... 179 4.2.3. Lighting Control Algorithms ............................................................................. 181 4.2.4. Date Output ....................................................................................................... 183 4.2.5. Data Visualization ............................................................................................. 184 4.3. Summary ............................................................................................................... 185 Chapter 5 .................................................................................................................................. 188 5. Case Study 1 .................................................................................................................... 188 5.1. Run Case Study 1 Model ...................................................................................... 190 5.1.1. Existing Building Model ................................................................................... 190 5.1.2. Daylighting and Lighting Data Acquisition ...................................................... 195 5.1.3. Lighting Control Algorithm .............................................................................. 206 5.2. Data Visualization ................................................................................................. 214 5.2.1. Lighting Control Results ................................................................................... 214 5.2.2. Link False-color Maps ...................................................................................... 225 5.3. Results and Discussion ......................................................................................... 230 5.3.1. Lighting Electricity Usage Dashboard .............................................................. 231 5.3.2. Hourly Luminaires' Status Dashboard .............................................................. 237 5.3.3. Lighting Operation Hours Dashboard ............................................................... 240 vi 5.3.4. Financial Analysis Dashboard ........................................................................... 245 5.4. Summary ............................................................................................................... 251 Chapter 6 .................................................................................................................................. 254 6. Case Study 2 .................................................................................................................... 254 6.1. Overall Case Study 2 Model ................................................................................. 254 6.1.1. Existing Building Model ................................................................................... 255 6.1.2. Daylighting and Lighting Data Acquisition ...................................................... 258 6.1.3. Lighting Control Algorithm .............................................................................. 262 6.2. Data Visualization ................................................................................................. 265 6.2.1. Lighting Control Results ................................................................................... 266 6.2.2. Link False-color Maps ...................................................................................... 269 6.3. Result and Discussion ........................................................................................... 270 6.3.1. Lighting Electricity Usage Dashboard .............................................................. 272 6.3.2. Hourly Luminaires’ Status Dashboard .............................................................. 276 6.3.3. Lighting Operation Hours Dashboard ............................................................... 279 6.3.4. Financial Analysis Dashboard ........................................................................... 284 6.4. Validation .............................................................................................................. 288 6.4.1. Validation Model............................................................................................... 289 6.4.2. Pathological Case .............................................................................................. 290 6.5. Summary ............................................................................................................... 307 Chapter 7 .................................................................................................................................. 308 7. Discussion and Future Work ............................................................................................ 308 7.1. Discussion ............................................................................................................. 308 7.2. Evaluation and Limitations ................................................................................... 315 7.2.1. Evaluation of current workflow ........................................................................ 316 7.2.2. Limitations of the current workflow ................................................................. 319 7.3. Future Work .......................................................................................................... 322 7.3.1. Enhancements.................................................................................................... 322 7.3.2. Future Field-Test ............................................................................................... 323 7.4. Conclusion ............................................................................................................ 328 References ................................................................................................................................ 329 vii Appendix A .............................................................................................................................. 344 A.1. Overall Grasshopper Workflow .............................................................................. 344 A.2. Customized Grasshopper Component ..................................................................... 350 A.3. Lighting Control Algorithms – Switching On/Off and Dimming Control ............. 363 viii LIST OF TABLES Table 1. 1 Title 24, part 6 - Matrix of Indoor Lighting Control (Title 24, 2019) ........................... 7 Table 1. 2 Keyed notes for each space/area type (Title 24, 2019) .................................................. 8 Table 1. 3 Indoor Lighting Power Density Allowances (Title 24, 2019) ....................................... 9 Table 1. 4 Recommended illuminance levels by space type (IESNA, 2021). .............................. 10 Table 1. 5 Summary of Light Sensors (Shawn, 2020) .................................................................. 14 Table 1. 6 Example of Photocell and motion sensors in the current market (https://www.leviton.com/en) ....................................................................................................... 15 Table 1. 7 The summary of daylight simulation tools (Banerjee, 2015) ...................................... 38 Table 1. 8 The example of lighting simulation outputs (Ochoa et al., 2012) ............................... 43 Table 2. 1 Comparison between daylight-linked switching and dimming controls (ul Haq et al., 2014). ..................................................................................................................... 67 Table 2. 2 The results of standard switching controls and differential switching controls (Li et al., 2019).............................................................................................................................. 69 Table 2. 3 Summary of energy savings uses daylight-linked controls in the literature review (ul Haq et al., 2014). ..................................................................................................................... 74 Table 2. 4 The main parameters of -ab, -aa, -ar, -ad, and -as (Lawrence Berkeley National Laboratory, 2020) ......................................................................... 92 Table 2. 5 Recommended parameters for Radiance (Kharvari, 2020). ........................................ 93 Table 3. 1 Definition and consideration of the main radiance parameters (Lawrence Berkeley National Laboratory, 2020) ....................................................................... 113 Table 3. 2 The usage of existing plugins in Grasshopper. .......................................................... 160 Table 4. 1 The summary of all the customized components. ...................................................... 186 Table 5. 1 Case Study 1: Summary of each element's detailed transmittance or reflectance ..... 193 Table 5. 2 Case Study 1: summary of weather file, location, and sky file. ................................ 198 Table 5. 3 Radiance Parameters for both daylight and lighting simulations for case studies..... 199 Table 5. 4 Case Study 1: summary of the selected IES files and LED light type. ...................... 200 Table 5. 5 Case Study 1: Summary of test room information. ................................................... 201 Table 5. 6 IESNA Recommended illuminance level for Cafeteria (IESNA, 2021). .................. 202 Table 5. 7 New Hampshire State Energy Profile – Electricity Price (EIA, 2021). ..................... 246 Table 5. 8 Summary of created seven dashboards. ..................................................................... 252 Table 5. 9 The summary of electricity savings for case study 1. ................................................ 253 Table 6. 1 Case Study 2: Summary of each element's detailed transmittance or reflectance ..... 257 Table 6. 2 Case Study 2: summary of weather file, location, and sky file. ................................ 257 Table 6. 3 Case Study 2: summary of the selected IES files and LED light type ....................... 259 Table 6. 4 Case Study 2: Summary of test room information. ................................................... 260 Table 6. 5 IESNA Recommended illuminance level for Open Floor Office (IESNA, 2021). ... 260 Table 6. 6 Summary of created seven dashboards. ..................................................................... 271 Table 6. 7 California State Energy Profile - Electricity Price (EIA, 2021). ............................... 284 Table 6. 8 Model information used for the shoebox model for validation. ................................ 290 Table 6. 9 Simulation Setting used for the shoebox model for validation. ................................. 290 Table 6. 10 The summary of the electricity savings for case study 2. ........................................ 307 Table 7. 1 The summary of all the customized components. ...................................................... 309 Table 7. 2 Summary of created seven dashboards. ..................................................................... 311 Table 7. 3 The running time of the proposed algorithm for two case studies............................. 322 ix LIST OF FIGURES Figure 1. 1 Electricity consumption by primary end uses in US residential sector in 2020 (EIA 2021) ...................................................................................................................................... 3 Figure 1. 2 Electricity consumption by main end uses in US commercial sector in 2020 (EIA 2021) ...................................................................................................................................... 4 Figure 1. 3 Lighting Control Requirements Overall (DiLouie, 2019) ............................................ 5 Figure 1. 4 A Flowchart shows the typical smart lighting system (Imam et al., 2016) ................ 13 Figure 1. 5 The range of measurement and placement of Lumina RG photosensor (https://www.leviton.com/en) ....................................................................................................... 16 Figure 1. 6 The example of a Lux Meter (photodetector) (https://www.globalspec.com/learnmore/optics_optical_components/optoelectronics /lux_meters_light_meters) ............................................................................................................ 17 Figure 1. 7 Measuring illuminance in the workplace (Kitazawa, 2015). ...................................... 18 Figure 1. 8 Mounting the sensor on a tripod (Kitazawa, 2015). ................................................... 18 Figure 1. 9 The definition of Horizontal illuminance (Lighting Research Center, 2021) ............ 19 Figure 1. 10 The example of the Skyometer sensor mounted on the roof (http://www.terrestriallight.com/). ................................................................................................ 19 Figure 1. 11 Skyometer sensors locally monitoring and analyzing the dynamic sky conditions and changes (http://www.terrestriallight.com/). ........................................................ 20 Figure 1. 12 The measurements of global illuminances (lux) and irradiance (w/m2 ) from the Skyometer sensor (http://www.terrestriallight.com/) ..................................................... 21 Figure 1. 13 A customized BAS dashboards offer the graphical UI to help users monitor and control multiple building systems. It includes the data visualization of the energy performance of the building (Frank, 2015). .................................................................................. 22 Figure 1. 14 BACnet-based building lighting control system. (Park and Hong, 2009) ................ 24 Figure 1. 15 The central zone of a big office building is devoid of natural light and vistas. Ambient overhead fluorescent lighting creates a uniform and steady-state lighting environment. Electrical illumination may be sufficient in such settings. (Konis and Selkowitz, 2017)......................................................................................................... 25 Figure 1. 16 Daylit perimeter zone on the sixth floor. Note: the lack of traditional ceiling- mounted electrical lighting fixtures (Konis and Selkowitz, 2017) ............................................... 26 Figure 1. 17 Daylight simulation elements and procedure (Reinhart, 2011) ................................ 30 Figure 1. 18 The calculation of daylight factor in a sidelight area by using Autodesk Ecotect v5.6 (Reinhart, 2011) ....................................................................................................... 31 Figure 1. 19 The definition of daylight factor: the ratio of an interior to the outside illuminance (Moon and Spencer 1942). ........................................................................................ 31 Figure 1. 20 The distribution of sky luminous above Boston on April 2nd at noon according to the uniform, 'old' CIE (Moon and Spencer 1942). ................................................... 32 Figure 1. 21 The hourly illuminance on March 21st at 15:00 represented as a false-color map in Rhino (By author) ............................................................................................................. 33 Figure 1. 22 Daylight distribution of a part of Airport Terminal for sDA300 lux (By author) .... 35 Figure 1. 23 Daylight distribution of a part of Airport Terminal for UDI 300 lux – 3000 lux (By author) .................................................................................................................................... 36 Figure 1. 24 Raytracing Process (Wikipedia Images) .................................................................. 39 Figure 1. 25 Radiosity Process (Ochoa et al., 2010) ..................................................................... 40 x Figure 1. 26 Top - Ray tracing: with reflections, soft shadows, and using textures. Bottom - Radiosity: within the Cornell box and Undistributed radiosity during iterative solving (Todd, 2014). ................................................................................................................................ 41 Figure 1. 27 Examples of quantitative (left) and qualitative output (right) (Ochoa et al., 2010) . 42 Figure 1. 28 An example of Rhinoceros 3D Model (By author) .................................................. 44 Figure 1. 29 A Grasshopper script example to conduct daylight simulation (By author) ............ 45 Figure 1. 30 The features and functionality of the Ladybug plugin (source: https://www.ladybug.tools/ladybug.html) (Roudsari, 2021) ........................................................ 46 Figure 1. 31 The features and functionality of the Honeybee plugin (source: https://www.ladybug.tools/honeybee.html ) (Roudsari, 2021) ..................................................... 47 Figure 1. 32 Honeybee daylight related analysis and the output matrix (source: https://www.ladybug.tools/honeybee.html) (Roudsari, 2021) ...................................................... 48 Figure 1. 33 The “GHPython script” component in Grasshopper overall .................................... 49 Figure 1. 34 To add or remove the input and output parameters in the "GH python" component ..................................................................................................................................... 50 Figure 1. 35 The Grasshopper Python Script Editor example ...................................................... 50 Figure 1. 36 The UI of Visual Studio, the example shown by using the Python code in Visual Studio to operate the linear programming of Matrix ......................................................... 51 Figure 1. 37 The UI of Microfost Power BI, the example shows the lighting electricity usage of three typical days with the switch on/off and dimming controls. ................................... 52 Figure 1. 38 The data source connection selection of Microsoft Power BI. ................................. 52 Figure 2. 1 Lighting design integrates with daylighting (NCSU, 2013). ...................................... 55 Figure 2. 2 The overall workflow of the lighting design process (Jason, 2016). .......................... 56 Figure 2. 3 The ceiling lighting plan of Westfield Terminal 2 in LAX (KGM Architectural Lighting, 2017) ............................................................................................................................. 57 Figure 2. 4 The interior lighting view of Westfield Terminal 2 in LAX (KGM Architectural Lighting, 2017). ............................................................................................................................ 57 Figure 2. 5 Interior views of SunLuminaire TM fixtures and their installation (Konis and Selkowitz, 2017). .......................................................................................................................... 58 Figure 2. 6 A Standard PWM circuit with the switch in series (Narra and Zinger, 2004) ........... 59 Figure 2. 7 Signal waveforms at PWM duty cycles of (a) 30 %, and (b) 90 % and their corresponding snapshots of the LED brightness at PWM duty cycles of (c) 30 %, and (d) 90 % (Choi et al., 2015) .......................................................................................................... 59 Figure 2. 8 The relationship between relative consumed power and LED luminaires' control signal (Doulos et al., 2017). .......................................................................................................... 60 Figure 2. 9 The relationship between relative consumed power with light output for test luminaires LED luminaires (Doulos et al., 2017). ........................................................................ 61 Figure 2. 10 Photometric and electric measurements for dimming levels of one T5 luminaire (Doulos et al., 2017)...................................................................................................................... 62 Figure 2. 11 Relationships between daylight illuminance values measured on the workplane and at the equivalent position on the ceiling on a sunny (A) and overcast (B) day (Bonomolo et al., 2017). ............................................................................................................... 63 Figure 2. 12 Example of photosensors location and its spatial responses (Bellia & Fragliasso, 2017) ........................................................................................................... 65 Figure 2. 13 An Example room for the lighting control simulation test (Bellia & Fragliasso, 2017). ............................................................................................................................................ 66 xi Figure 2. 14 Correlation between lighting energy savings and the average number of switch-offs within differential switching controls (Li et al., 2019). ............................................. 70 Figure 2. 15 The luminaires near the window and at the rear of the room were dimmed in proportion to the amount of daylight illuminance (Tetri, 2002). .................................................. 71 Figure 2. 16 Example of illuminance values measured on the same day in continuous dimming operation and “simulated” ON/OFF operation (Bonomolo et al., 2017) ...................... 72 Figure 2. 17 The flowchart of the daylight-linked closed-loop algorithm (ul Haq et al., 2014). . 76 Figure 2. 18 The flowchart of the daylight-linked open-loop algorithm (ul Haq et al., 2014). ... 76 Figure 2. 19 Typical control algorithms: a)Open-loop switching algorithm; b)Closed-loop switching algorithm; c) Open-loop dimming algorithm; d) Closed-loop linear proportional control (or sliding setpoint control) algorithm; e) Closed-loop constant setpoint control (or integral reset control algorithm; f) Tri-level control algorithm (Bellia et al., 2016) .................... 77 Figure 2. 20 Closed-loop Daylight- linked control systems (http://intelliblinds.com/daylightharvesting.html) ........................................................................ 78 Figure 2. 21 Typical closed-loop control system (Lawrence Berkeley National Laboratory, n.d) ............................................................................ 79 Figure 2. 22 Open-loop Daylight-linked Control Systems (source: http://intelliblinds.com/daylightharvesting.html ) ........................................................................ 80 Figure 2. 23 The daylight sensing-based lighting control system (Li et al., 2016) ...................... 82 Figure 2. 24 An open-loop control integrated with real-time daylight modeling (Jain and Garg, 2018). ................................................................................................................... 82 Figure 2. 25 Skyometer in horizontal orientation (top left). Horizontal, HDR sky luminance map (top right) Skyometer in the vertical orientation (bottom left). Vertical, HDR sky luminance map (bottom right) (Humann and Mcneil, 2017). ........................ 83 Figure 2. 26 Current state-of-the-art in lighting simulation for building science (Ochoa et al., 2012) ....................................................................................................................... 85 Figure 2. 27 The Simulation results after six consecutive runs (Kharvari, 2020). ...................... 89 Figure 2. 28 Assessing relative bias in daylight simulation (Kharvari, 2020). ............................. 91 Figure 2. 29 Measured illuminance and simulated illuminance levels (Kharvari, 2020). ............ 94 Figure 2. 30 The Structure of Chapter 2 ....................................................................................... 95 Figure 3. 1 Overall outline of the workflow diagram of Methodology and software and tools usage ............................................................................................................................................. 98 Figure 3. 2 Overall sub-outline and details of the workflow diagram of Methodology ............. 99 Figure 3. 3 Overall outlines of the workflow diagram of Methodology in Existing Building Model and its software and tools usage ...................................................................................... 102 Figure 3. 4 Overall sub-outline and details of the workflow diagram of Methodology in Geometry Division and Simulation Preparation ......................................................................... 102 Figure 3. 5 Northeast art of the airport terminal of Beijing Daxing International Airport geometry Division in Rhino (Each color represent different building elements) ....................... 103 Figure 3. 6 Assign the geometry in Rhino as walls element by using the “Brep” component and use “internalize data” to store the geometry in Grasshopper ............................................... 104 Figure 3. 7 Assign the walls reflectance value using the “Honeybee_Radiance Opaque Material” component .................................................................................................................. 105 Figure 3. 8 Assign the glazing transmittance value using the “Honeybee_Radiance Glass Material” component .................................................................................................................. 105 Figure 3. 9 Adding glazing to the walls by using the “Honeybee_addHBGlz” component ...... 106 xii Figure 3. 10 Get the weather file of Beijing from EnergyPlus (source: https://energyplus.net/weather) ................................................................................................... 107 Figure 3. 11 Inputting the EPW weather file of Los Angeles to Ladybug in Grasshopper for the daylighting simulation (https://www.ladybug.tools/epwmap/) ....................................... 107 Figure 3. 12 Generate climate-based sky from the EPW weather file by applying the “Honeybee_Generate Climate Based Sky” component. ............................................................. 108 Figure 3. 13 The second method: Set the orientation of the building geometry by using the “North” input. ............................................................................................................................. 108 Figure 3. 14 Create the sensor grids, assign the sensor points within the grids, and set these points offset by using the “LB Generate Point Grid” component ..................................... 109 Figure 3. 15 The geometry and sensor points in Rhino (Perspective view and Top view) ....... 110 Figure 3. 16 Overall outlines of the workflow diagram of Methodology in Daylighting & Lighting Data Acquisition and its software and tools usage ....................................................... 111 Figure 3. 17 Overall sub-outline and details of the workflow diagram of Methodology in Simulation Parameters Setup and Data Storage & Formatting ................................................... 111 Figure 3. 18 Set up analysis time of the date by using the “Honeybee_Generate Climate Based Sky” component. Example shows the analysis time is March 21st at 7:00 am. .............. 112 Figure 3. 19 Setting up Radiance Parameters by using the “Honeybee_RADParameters” component ................................................................................................................................... 113 Figure 3. 20 The preparation of the daylighting simulation setup ............................................. 114 Figure 3. 21 The customized Python scripts are developed to automatically output the hourly analysis date and month from sunrise to sunset .............................................................. 115 Figure 3. 22 The customized Python scripts are developed automatically to calculate the whole year hours from sunrise to sunset. .............................................................................. 115 Figure 3. 23 Using the “Honeybee_Run Daylight Simulation” component to run the hourly illuminance daylighting simulation in Grasshopper ................................................................... 116 Figure 3. 24 Preview the result data by using the “panel” component ...................................... 116 Figure 3. 25 On the left is the “Ladybug_Recolor Mesh” component, in the middle is the “Ladybug_Legend Parameters” component, and on the right is the “Text Tag 3D” component. .................................................................................................................................. 117 Figure 3. 26 The CAD file of the lighting fixture plan imported in Rhino................................ 118 Figure 3. 27 The points in the lighting fixture plan, each point represent two-dimension coordinates of each lighting fixture ............................................................................................ 118 Figure 3. 28 Lighting fixtures are drawn as points in Rhino and saved in layers ...................... 119 Figure 3. 29 Set the lighting fixtures points in Grasshopper, internalize data and connect to "Honeybee_IES Luminaire Zone" Component .......................................................................... 119 Figure 3. 30 Read IES file by using using the "Honeybee IES Luminaire" component ........... 120 Figure 3. 31 The luminaire details show the example of one 71 W LED IES file format ......... 121 Figure 3. 32 A Example of the Polar Curve of a 71W LED from DiaLux software ................. 121 Figure 3. 33 Using the dark sky condition for the lighting simulation ...................................... 122 Figure 3. 34 Overall workflow to run the lighting simulation in Grasshopper by using the Honeybee plugins........................................................................................................................ 122 Figure 3. 35 Test whether the test analysis grid satisfies the illuminance and remove data which does not fulfill the requirement using the "Cull Pattern" component in Grasshopper ..... 123 xiii Figure 3. 36 Running lighting simulation with each lighting fixture one by one sequentially to acquire illuminance value on sensor points. Each set of data is stored in Grasshopper using the “Data Recorder” component ...................................................................................... 124 Figure 3. 37 Each light illuminance data is processed as each column of the Matrix and exported as a CSV file by using the C# script in Grasshopper ................................................... 125 Figure 3. 38 Overall outlines of the workflow diagram of Methodology in Lighting Control Algorithm and its software and tools usage ................................................................................ 126 Figure 3. 39 Overall sub-outline and details of the workflow diagram of Methodology in Input and Output ......................................................................................................................... 126 Figure 3. 40 Run "GHPython Remote" plugin to call the external functions from Python 2.7 . 127 Figure 3. 41 Using Anaconda Prompt to activate Rhinoremote ................................................ 127 Figure 3. 42 Using Anaconda Prompt to install numpy and scipy packages ............................. 128 Figure 3. 43 The location of the programming environment for the “GHPython Remote” component. .................................................................................................................................. 128 Figure 3. 44 Details workflow of running "GHPython Remote" and the results....................... 129 Figure 3. 45 The method to import and call the functions in the "GHPython script" component ................................................................................................................................... 129 Figure 3. 46 The example path of Python Packages will be called for use in "GHPython Remote." ..................................................................................................................................... 130 Figure 3. 47 The overall flowchart of the switch on/off control algorithm. .............................. 131 Figure 3. 48 Example of a simple box room with illuminance value (lux) on 15 sensor points at 0.75m height at a certain time t1 ................................................................................... 132 Figure 3. 49 The illuminance distribution on 15 sensor points when ONLY light No.1 turns on under dark sky conditions (no daylight). ...................................................................... 133 Figure 3. 50 The illuminance distribution on 15 sensor points when ONLY light No.2 turns on under dark sky conditions (no daylight). ...................................................................... 133 Figure 3. 51 The illuminance distribution on 15 sensor points when ONLY light No.3 turns on under dark sky conditions (no daylight). ...................................................................... 134 Figure 3. 52 The illuminance distribution on 15 sensor points when ONLY light No.4 turns on under dark sky conditions (no daylight). ...................................................................... 134 Figure 3. 53 The flowchart of the switch on/off control example case: with 4 lights, 15 sensor points........................................................................................................................... 136 Figure 3. 54 The process to input the daylighting illuminance data as each array to the Switch On/Off Algorithm for Linear Programming operation ................................................... 136 Figure 3. 55 The lighting illuminance data as a Matrix be imported from the CSV file by using the Padas package as pd.read_csv function in "GHPython script" Editor ........................ 137 Figure 3. 56 The example of the lighting illuminance data as a Matrix in the CSV file ........... 138 Figure 3. 57 Using the Pulp package from Python to call the LP function for operating the Linear Programming and assigning Binary to get the value 0 or 1 ............................................ 138 Figure 3. 58 The method to create j = 1, 2, ....., m in Python script .......................................... 139 Figure 3. 59 The method to operate the function of inequality 3-6 ........................................... 139 Figure 3. 60 Transform the 0 or 1 (integer) to on/off string in Python ...................................... 140 Figure 3. 61 The overall flowchart for the dimming control algorithm. .................................... 140 Figure 3. 62 The flowchart of the dimming control example case: with 4 lights, 15 sensor points ........................................................................................................................................... 141 xiv Figure 3. 63 Using the Pulp package from Python to call the LP function for operating the Linear Programming and assigning Continuous to get the float value 0 to1 .............................. 143 Figure 3. 64 Calculating the Dimming Level of each light, transforming the dimming level as the percentage and numbering each light ...................................................................... 144 Figure 3. 65 Overall outlines of the workflow diagram of Methodology in Energy Usage Comparison and its software and tools usage ............................................................................. 145 Figure 3. 66 Overall sub-outline and details of the workflow diagram of Methodology in Lighting Energy Calculation and Data Visualization ................................................................. 146 Figure 3. 67 The on/off status of each luminaire at a certain time with the proposed switch on/off control algorithm. ............................................................................................................. 147 Figure 3. 68 The information of luminaire can be acquired from the IES file by using the "Honeybee_IES_Luminaires" component to read the file .......................................................... 148 Figure 3. 69 Relative consumed power versus light output for test luminaires LED luminaires (Doulos et al., 2017). ................................................................................................. 148 Figure 3. 70 The light output of each luminaire at a certain time with the proposed dimming control algorithm. ........................................................................................................ 149 Figure 3. 71 Example of hourly illuminance distribution false-color map (luminaires + daylighting) and Light Status with proposed switch on/off and dimming control at January 1st, 14:00 using climate-based sky file.......................................................................... 150 Figure 3. 72 The hourly illuminance distribution false-color map images are stored in AWS S3 ...................................................................................................................................... 151 Figure 3. 73 The URL of each illuminance false-color map image is generated. ..................... 151 Figure 3. 74 The URLs of each hourly illuminance distribution false-color maps (luminaires + daylighting) are stored in excel ............................................................................ 152 Figure 3. 75 Excel sheet contains the whole year hourly light status and electricity results after implementing the proposed switch on/off and dimming control algorithm in a certain case study model. ........................................................................................................................ 153 Figure 3. 76 Import the Excel as a data source to Power BI. ..................................................... 154 Figure 3. 77 Using Power Query in Power BI to organize the data for further interactive data visualization. ....................................................................................................................... 154 Figure 3. 78 Using Power Query insect column to add the month, day, hour as separate columns. ...................................................................................................................................... 155 Figure 3. 79 The results of each separate column after using Power Query ............................. 155 Figure 3. 80 Other KPIs such as cost and savings for different controls can be calculated inside Power BI ........................................................................................................................... 156 Figure 3. 81 Example of lighting electricity comparison dashboard in Microsoft Power BI. ... 157 Figure 3. 82 Example of illuminance distribution false-color map in Microsoft Power BI. ..... 157 Figure 3. 83 The overall Grasshopper Flowchart follows the structure of the overall methodology. .............................................................................................................................. 158 Figure 3. 84 Each group in Grasshopper is color-coded with different colors - Ladybug, Honeybee, TT Toolbox plugins, Python, and C# scripts. ........................................................... 159 Figure 3. 85 Four main stages of overall methodology and the software and tools usage ........ 161 Figure 4. 1 The overall Grasshopper Flowchart follows the structure of the overall methodology, and sixteen customized components are highlighted. .......................................... 163 Figure 4. 2 The customized Python scripts (GH components 1 and 4) were developed to automatically output the hourly analysis date and months from sunrise to sunset. .................... 164 xv Figure 4. 3 The customized Python scripts (GH components 2 and 4) were developed to automatically output the hourly analysis date and ranges of months from sunrise to sunset. .... 165 Figure 4. 4 The customized Python scripts (component 3) were developed to automatically calculate the whole year hours from sunrise to sunset. ............................................................... 165 Figure 4. 5 The customized Python scripts (component 5) were developed automatically to process the time data for the daylighting simulation. ............................................................. 166 Figure 4. 6 The customized Python scripts (component 6) were developed automatically to filter the illuminance data. ...................................................................................................... 167 Figure 4. 7 The customized Python scripts (component 7) were developed for numbering and naming each analysis luminaires under different control algorithms as a string. ................ 168 Figure 4. 8 The customized Python scripts (component 11) were developed to extract the input watt and calculate lumen per watt of the selected luminaire. ............................................ 169 Figure 4. 9 The customized C# scripts (component 13) were developed to write the organized lighting illuminance data as CSV files. ...................................................................... 170 Figure 4. 10 The customized Python scripts (component 8) - the proposed daylight-linked switch on/off control algorithm. The process of operating the linear matrix programming. ...... 171 Figure 4. 11 The customized Python scripts (component 9) - the proposed daylight-linked dimming control algorithm. The process of operating the linear matrix programming. ............ 172 Figure 4. 12 The customized Python scripts (component 10) - the lighting electricity calculator for hourly switch on/off and dimming control. .......................................................... 173 Figure 4. 13 The customized Python scripts (component 12) - timeline generator add an hourly timeline (one column) to the exported Excel. ............................................................. 174 Figure 4. 14 The customized Python scripts (component 14) – calculator for the average hourly illuminance (from daylight and luminaires) distribution value for all the analysis hours. ........................................................................................................................................... 175 Figure 4. 15 The customized Python scripts (component 15) – text title creator for illuminance distribution false-color map. ................................................................................... 176 Figure 4. 16 The created title in illuminance distribution false-color map. The example title of dimming control illuminance distribution false-color maps ........................................... 176 Figure 4. 17 The customized Python scripts (component 16) - Links String Generator From AWS S3. ............................................................................................................................ 177 Figure 4. 18 The lighting control algorithms in Grasshopper were written and run with Python in Visual Studio. ............................................................................................................. 178 Figure 4. 19 Importing key modules in Visual Studio (top) for linear matrix programming with explanation and Grasshopper (bottom). .............................................................................. 179 Figure 4. 20 Importing key modules in Visual Studio (top) for writing output data to Excel with explanation and Grasshopper (bottom). .................................................................... 179 Figure 4. 21 Inputting the total number of luminaires, illuminance recommendation level (threshold), and input watt of luminaires in Visual Studio. ........................................................ 180 Figure 4. 22 Inputting the whole year hourly daylighting illuminance data file (trimmed data) into Visual Studio with Python for the linear matrix programming operation. ................. 181 Figure 4. 23 Inputting lighting illuminance data file (trimmed data) into Visual Studio with Python for the linear matrix programming operation. ........................................................ 181 Figure 4. 24 The switch on/off control algorithm and hourly electricity calculation were written in Visual Studio. ............................................................................................................. 182 xvi Figure 4. 25 The dimming control algorithm and hourly electricity calculation were written in Visual Studio. ............................................................................................................. 182 Figure 4. 26 Process, append, and organize data after each time the control algorithms run. The flowchart in Grasshopper (left) and code were written in Visual Studio (right). ................ 183 Figure 4. 27 The process of writing the lighting control results to the Excel file in Grasshopper (left) and Visual Studio (right). ............................................................................. 184 Figure 4. 28 Importing the data into Visual Studio to calculate hourly daylighting and lighting illuminance value (after lighting controls) on the analysis surface. .............................. 185 Figure 4. 29 The workflow summary uses Visual Studio to run the whole year's hourly switch on/off, dimming control algorithms, and export the data. ............................................... 187 Figure 5. 1 Case Study 1 model and Test Room......................................................................... 189 Figure 5. 2 The overall flowchart of methodology workflow. ................................................... 189 Figure 5. 3 Case Study 1: 3D views of Revit Sample Project (Autodesk) and test room........... 190 Figure 5. 4 Case Study 1: The floor plan and room type of test room in Revit Sample Project (Autodesk). ..................................................................................................................... 191 Figure 5. 5 Case Study 1: The section of the test room from Revit Sample Project (Autodesk)................................................................................................................................... 191 Figure 5. 6 Case Study 1: Test Room was modeled in Rhino based on Revit sample project model. ............................................................................................................................. 192 Figure 5. 7 Case Study 1: Setting each building element and its transmittance or reflectance in Grasshopper with Honeybee version 0.0.66. ........................................................ 192 Figure 5. 8 Case Study 1: Acquired weather file of Manchester from EnergyPlus website....... 193 Figure 5. 9 Case Study 1: using Ladybug to process the import weather file and read it as an epw weather file. ................................................................................................................ 194 Figure 5. 10 Case Study 1: set the analysis surface and create sensor points using Ladybug. ... 195 Figure 5. 11 Analysis hours: Sunrise to Sunset in March 21st, June 21st, and December 21st, Climate Base Sky to test the algorithms............................................................................. 196 Figure 5. 12 Hourly daylighting simulation results from sunrise to sunset on March 21 st , June 21 st , and December 21 st . ..................................................................................................... 196 Figure 5. 13 Case Study 1: The whole year analysis hours. Using customized components to calculate the sunrise to sunset hours through the entire year with Python script and "Honeybee_Generate Climate Base Sky" Component. .............................................................. 197 Figure 5. 14 Case Study 1: Whole year analysis hours with four different types of sky conditions: climate base sky, CIE sunny with sun sky, CIE intermediate with sun sky, and CIE cloudy sky. ............................................................................................................ 198 Figure 5. 15 Case Study 1: lighting plan in Rhino (Top), lighting plan in Revit Sample Project (Bottom).......................................................................................................................... 199 Figure 5. 16 Using Honeybee plugins to conduct the lighting simulation to test whether the lighting plan satisfies the IESNA recommendation for the illuminance level. ..................... 200 Figure 5. 17 Case Study 1: The results of the tested lighting plan for illuminance level - 250 lux. ..................................................................................................................................... 202 Figure 5. 18 Case Study 1: Simulated 12 luminaires one by one to acquire lighting illuminance data and exported to CSV file. ................................................................................ 203 Figure 5. 19 Case Study 1: The results of lighting illuminance data from 12 luminaries and 136 sensor points in a CSV file. ........................................................................................... 204 xvii Figure 5. 20 Case Study 1: The results of lighting illuminance data from 12 luminaires and 133 sensor points in a CSV file. ........................................................................................... 205 Figure 5. 21 Using the “GH Python Remote” component to import Python module - Pulp, numpy, and Scipy. ........................................................................................................... 206 Figure 5. 22 Case Study1: Operating the switch on/off and dimming control algorithms for the three days test. ................................................................................................................. 207 Figure 5. 23 Case Study 1: The results of the switch on/off and dimming control for 12 luminaries with three days test. ................................................................................................... 207 Figure 5. 24 Export the entire year's hourly daylighting illuminance data (trimmed and original) to a CSV and Excel file. ............................................................................................... 209 Figure 5. 25 The whole year timeline, analysis area, and Sky Condition file usage were extracted and exported as an Excel file. ...................................................................................... 209 Figure 5. 26 Case Study 1: Put the exported excel files and CSV files in the created Visual Studio Project. The bottom is the way to input the files in Python code. ................................... 210 Figure 5. 27 Case Study 1: inputs for the lighting control algorithm – total number of luminaries, recommendation illuminance level (threshold), and input watt. ............................. 211 Figure 5. 28 Case Study 1: The whole year result of the switch on/off and dimming control for the Case Study 1 test room under the climate-based sky. ......................................... 212 Figure 5. 29 Case Study 1: The whole year result of the switch on/off and dimming control for the Case Study 1 test room under CIE sunny with sun sky. ..................................... 212 Figure 5. 30 Case Study 1: The whole year result of the switch on/off and dimming control for the Case Study 1 test room under CIE intermediate with sun sky. ........................... 213 Figure 5. 31 Case Study 1: The whole year result of the switch on/off and dimming control for the Case Study 1 test room under CIE cloudy sky.................................................... 213 Figure 5. 32 Case Study 1: whole year hourly switch on/off and dimming lighting control outputs under four different types of sky conditions. ..................................................... 214 Figure 5. 33 Inputting the results of lighting illuminance data from 12 luminaires and 136 sensor points with a CSV file. ....................................................................................... 215 Figure 5. 34 Case Study 1: The results of whole year hourly illuminance data from luminaires on 136 sensors points with switch on/off and dimming controls using climate-based sky file. ................................................................................................................ 216 Figure 5. 35 Importing three datasets to Grasshopper for creating false-color maps. ................ 217 Figure 5. 36 The workflow to create the whole year hourly illuminance distribution false-color maps (luminaires + daylighting). .............................................................................. 217 Figure 5. 37 The process to create the whole year hourly illuminance false-color maps. .......... 218 Figure 5. 38 Case Study 1: Generating hourly illuminance distribution false-color maps (luminaires + daylighting) and the switch on/off and dimming status of each luminaire (Jan 1st 13:00) under the climate-based sky condition. .............................................................. 219 Figure 5. 39 Using the slider animation function to generate the whole year's hourly false-color maps. ......................................................................................................................... 220 Figure 5. 40 Case Study 1: the whole year's hourly illuminance false-color maps and the witch on/off and dimming status of each luminaire using the climate-based sky file. ............... 221 Figure 5. 41 Using recorded data from the whole year’s hourly illuminance distribution (luminaires + daylighting) to calculate the average illuminance distribution (luminaires + daylighting). ................................................................................................................................ 222 xviii Figure 5. 42 Case Study 1: the whole year average illuminance distribution false-color maps (luminaires + daylighting) and the witch on/off and dimming status of each luminaire using the climate-based sky file. ................................................................................................. 223 Figure 5. 43 Case Study 1: the whole year average illuminance distribution false-color maps (luminaires + daylighting) and each luminaire’s switch on/off and dimming average status during the whole year using CIE Sunny with Sun Sky file. ............................................. 223 Figure 5. 44 Case Study 1: the whole year y average illuminance distribution false-color maps (luminaires + daylighting) and each luminaire’s switch on/off and dimming average status during the whole year using CIE Intermediate with Sun Sky file. ................................... 224 Figure 5. 45 Case Study 1: the whole year average illuminance distribution false-color maps (luminaires + daylighting) and each luminaire’s switch on/off and dimming average status using the whole year under CIE Cloudy Sky file. ............................................................ 224 Figure 5. 46 Case Study 1: Uploaded illuminance distribution false-color maps (under the climate-based sky) to AWS S3. .................................................................................................. 225 Figure 5. 47 Case Study 1: Acquired URL of each image (Noted that from “Frame_00000” to “Frame_08759”, the structure of each link is regular)............................................................ 226 Figure 5. 48 Using Python code to generate 8760 URLs (whole year) for the illuminance false-color map under the climate-based sky. ............................................................................. 227 Figure 5. 49 Case Study 1: Copy and Paste the URLs to the same Excel sheet with lighting control results. ............................................................................................................................. 227 Figure 5. 50 Case Study 1: Appending all the results data from lighting controls in Excel. Ready for importing into Power BI. ........................................................................................... 228 Figure 5. 51 Importing the appended Excel file (the results of lighting controls from four sky files) to Power BI. ................................................................................................................ 228 Figure 5. 52 Case Study 1: Using the Power Query to add the date and time (organize and layer the data structure). .............................................................................................................. 229 Figure 5. 53 Case Study 1: Using DAX function in Power BI to calculate some Key Performance Indicators (KPI). .................................................................................................... 230 Figure 5. 54 By clicking different tabs under the UI of Power BI to choose different dashboards................................................................................................................................... 231 Figure 5. 55 Case Study 1: the lighting electricity usage dashboard to visualize the hourly, daily, and monthly lighting electricity usage with the climate-based sky file. .............. 232 Figure 5. 56 Case Study 1: the lighting electricity usage dashboard to visualize the hourly, daily, and monthly lighting electricity usage with CIE sunny with sun file. ................. 233 Figure 5. 57 Case Study 1: the lighting electricity usage dashboard to visualize the hourly, daily, and monthly lighting electricity usage with CIE intermediate with sun file. ....... 234 Figure 5. 58 Case Study 1: the lighting electricity usage dashboard to visualize the hourly, daily, and monthly lighting electricity usage with CIE Cloudy sky file. ....................... 234 Figure 5. 59 Case Study 1: Whole year's total lighting electricity usage and savings to baseline under four sky conditions and the average combining with four sky conditions savings (operation hours from 0:00 to 23:00). ............................................................................ 236 Figure 5. 60 Case Study 1: Whole year's total lighting electricity usage and savings to baseline under four sky conditions and the average combining with four sky conditions savings (operation hours from 8:00 to 20:00) ............................................................................. 237 Figure 5. 61 Case Study 1: point-in-time validation for the space illuminance level satisfaction and the visualization of each luminaire's status with the implementation of xix the proposed switch on/off and dimming control algorithms – with the CIE Cloudy Sky file on Sept 21 st at 8:00. .............................................................................................................. 238 Figure 5. 62 Case Study 1: point-in-time validation for the space illuminance level satisfaction and the visualization of each luminaire's status with the implementation of the proposed switch on/off and dimming control algorithms – with the CIE Intermediate with sun sky file on Sept 21 st at 8:00. ......................................................................................... 238 Figure 5. 63 Case Study 1: point-in-time validation for the space illuminance level satisfaction and the visualization of each luminaire's status with the implementation of the proposed switch on/off and dimming control algorithms – with the CIE sunny with sun sky file on Sept 21 st at 8:00. ......................................................................................... 239 Figure 5. 64 Case Study 1: point-in-time validation for the space illuminance level satisfaction and the visualization of each luminaire's status with the implementation of the proposed switch on/off and dimming control algorithms – with the Climate-based sky file on Sept 21 st at 8:00. ........................................................................................................ 239 Figure 5. 65 Case Study 1: the lighting operations hours results for Switch On/Off control (with CIE Cloudy Sky and operation hours: 0:00 to 23:00). ...................................................... 241 Figure 5. 66 Case Study 1: the lighting operations hours results for Switch On/Off control (with CIE intermediate with sun Sky and operation hours: 0:00 to 23:00). ............................... 242 Figure 5. 67 Case Study 1: the lighting operations hours results for Switch On/Off control (with CIE sunny with sun Sky and operation hours: 0:00 to 23:00). .......................................... 242 Figure 5. 68 Case Study 1: the lighting operations hours results for Switch On/Off control (with Climate-based Sky and operation hours: 0:00 to 23:00). .................................................. 243 Figure 5. 69 Case Study 1: the lighting operations hours results for Dimming control (with CIE Cloudy Sky and operation hours: 0:00 to 23:00). ...................................................... 243 Figure 5. 70 Case Study 1: the lighting operations hours results for Dimming control (with CIE intermediate with sun and operation hours: 0:00 to 23:00). ...................................... 244 Figure 5. 71 Case Study 1: the lighting operations hours results for Dimming control (with CIE sunny with sun and operation hours: 0:00 to 23:00). ................................................. 244 Figure 5. 72 Case Study 1: the lighting operations hours results for Dimming control (with Climate-based sky and operation hours: 0:00 to 23:00). ................................................... 245 Figure 5. 73 Case Study 1: the whole year financial analysis dashboard to analyze the cost and saving of proposed switch on/off and dimming controls compared with baseline lighting mode for case study 1 room (operation hours: 0:00 to 23:00). ..................................... 247 Figure 5. 74 Case Study 1: the quarterly financial analysis dashboard analyzed the cost and saving per square meter of the proposed switch on/off and dimming controls compared with baseline lighting mode for case study 1 room (operation hours: 0:00 to 23:00). ............... 248 Figure 5. 75 Case Study 1: the whole year financial analysis dashboard to analyze the cost and saving of proposed switch on/off and dimming controls compared with baseline lighting mode for case study 1 room (operation hours: 8:00 to 20:00). ..................................... 249 Figure 5. 76 Case Study 1: the quarterly financial analysis dashboard analyzed the cost and saving per square meter of the proposed switch on/off and dimming controls compared with baseline lighting mode for case study 1 room (operation hours: 8:00 to 20:00). ............... 250 Figure 6. 1 Case Study 2 Model and Test Room. ....................................................................... 254 Figure 6. 2 Case Study 2 building in Monterey Park, CA. (1) Axonometric view of entire building (2) Perspective view from Southeast. ........................................................................... 255 Figure 6. 3 Case Study 2: the test room floor plan and its dimensions....................................... 256 xx Figure 6. 4 Case Study 2: Acquired weather file of Los Angeles, California from EnergyPlus website. .................................................................................................................... 257 Figure 6. 5 Case Study 2: the analysis surface and created sensor points. ................................. 258 Figure 6. 6 Case Study 2: lighting plan and modeled luminaires in Rhino. ............................... 259 Figure 6. 7 Case Study 2: The results of lighting illuminance data from 44 luminaries and 535 sensor points in a CSV file................................................................................................... 261 Figure 6. 8 Case Study 1: The results of lighting illuminance data from 44 luminaires and 523 sensor points in a CSV file................................................................................................... 262 Figure 6. 9 Case Study 2: The whole year result of the switch on/off and dimming control for the Case Study 2 test room under the climate-based sky. ..................................................... 263 Figure 6. 10 Case Study 2: The whole year result of the switch on/off and dimming control for the Case Study 2 test room under CIE sunny with sun sky. .................................................. 264 Figure 6. 11 Case Study 2: The whole year result of the switch on/off and dimming control for the Case Study 2 test room under CIE intermediate with sun sky. ....................................... 264 Figure 6. 12 Case Study 2: The whole year result of the switch on/off and dimming control for the Case Study 2 test room under CIE cloudy sky. ............................................................... 265 Figure 6. 13 Case Study 2: the whole year's hourly illuminance false-color maps and the witch on/off and dimming status of each luminaire using the climate-based sky file. ............... 266 Figure 6. 14 Case Study 2: the whole year average illuminance distribution false-color maps (luminaires + daylighting) and the witch on/off and dimming status of each luminaire using the climate-based sky file. ................................................................................................. 267 Figure 6. 15 Case Study 2: the whole year average illuminance distribution false-color maps (luminaires + daylighting) and each luminaire’s switch on/off and dimming average status during the whole year using CIE Sunny with Sun Sky file. ....................................................... 268 Figure 6. 16 Case Study 2: the whole year y average illuminance distribution false-color maps (luminaires + daylighting) and each luminaire’s switch on/off and dimming average status during the whole year using CIE Intermediate with Sun Sky file. ................................... 268 Figure 6. 17 Case Study 2: the whole year average illuminance distribution false-color maps (luminaires + daylighting) and each luminaire’s switch on/off and dimming average status using the whole year under CIE Cloudy Sky file. ............................................................ 269 Figure 6. 18 Case Study 2: Appending all the results data from lighting controls in Excel. Ready for importing into Power BI. ........................................................................................... 270 Figure 6. 19 Case Study 2: the lighting electricity usage dashboard to visualize the hourly, daily, and monthly lighting electricity usage with the climate-based sky file. ........................... 272 Figure 6. 20 Case Study 2: the lighting electricity usage dashboard to visualize the hourly, daily, and monthly lighting electricity usage with CIE sunny with sun file. .............................. 273 Figure 6. 21 Case Study 2: the lighting electricity usage dashboard to visualize the hourly, daily, and monthly lighting electricity usage with CIE intermediate with sun file. ................... 273 Figure 6. 22 Case Study 2: the lighting electricity usage dashboard to visualize the hourly, daily, and monthly lighting electricity usage with CIE Cloudy sky file. .................................... 274 Figure 6. 23 Case Study 2: Whole year's total lighting electricity usage and savings to baseline under four sky conditions and the average combining with four sky conditions savings (operation hours from 0:00 to 23:00). ............................................................................ 275 Figure 6. 24 Case Study 2: Whole year's total lighting electricity usage and savings to baseline under four sky conditions and the average combining with four sky conditions savings (operation hours from 8:00 to 19:00) ............................................................................. 276 xxi Figure 6. 25 Case Study 2: point-in-time validation for the space illuminance level satisfaction and the visualization of each luminaire's status with the implementation of the proposed switch on/off and dimming control algorithms – with the CIE Cloudy Sky file on Sept 21 st at 8:00. ..................................................................................................................... 277 Figure 6. 26 Case Study 2: point-in-time validation for the space illuminance level satisfaction and the visualization of each luminaire's status with the implementation of the proposed switch on/off and dimming control algorithms – with the CIE Intermediate with sun sky file on Sept 21 st at 8:00. ................................................................................................. 277 Figure 6. 27 Case Study 2: point-in-time validation for the space illuminance level satisfaction and the visualization of each luminaire's status with the implementation of the proposed switch on/off and dimming control algorithms – with the CIE sunny with sun sky file on Sept 21 st at 8:00. .............................................................................................................. 278 Figure 6. 28 Case Study 2: point-in-time validation for the space illuminance level satisfaction and the visualization of each luminaire's status with the implementation of the proposed switch on/off and dimming control algorithms – with the Climate-based sky file on Sept 21 st at 8:00. ..................................................................................................................... 278 Figure 6. 29 Case Study 2: the lighting operations hours results for Switch On/Off control (with CIE Cloudy Sky and operation hours: 0:00 to 23:00). ...................................................... 280 Figure 6. 30 Case Study 2: the lighting operations hours results for Switch On/Off control (with CIE Intermediate with Sun Sky and operation hours: 0:00 to 23:00). .............................. 280 Figure 6. 31 Case Study 2: the lighting operations hours results for Switch On/Off control (with CIE Sunny with Sun Sky and operation hours: 0:00 to 23:00). ........................................ 281 Figure 6. 32 Case Study 2: the lighting operations hours results for Switch On/Off control (with Climate-based Sky and operation hours: 0:00 to 23:00). .................................................. 281 Figure 6. 33 Case Study 2: the lighting operations hours results for Dimming control (with CIE Cloudy Sky and operation hours: 0:00 to 23:00). ...................................................... 282 Figure 6. 34 Case Study 2: the lighting operations hours results for Dimming control (with CIE Intermediate with Sun and operation hours: 0:00 to 23:00)....................................... 282 Figure 6. 35 Case Study 2: the lighting operations hours results for Dimming control (with CIE Sunny with Sun and operation hours: 0:00 to 23:00)................................................. 283 Figure 6. 36 Case Study 2: the lighting operations hours results for Dimming control (with Climate-based Sky and operation hours: 0:00 to 23:00). .................................................. 283 Figure 6. 37 Case Study 2: the whole year financial analysis dashboard to analyze the cost and saving of proposed switch on/off and dimming controls compared with baseline lighting mode for case study 2 room (operation hours: 0:00 to 23:00). ..................................... 285 Figure 6. 38 Case Study 2: the quarterly financial analysis dashboard analyzed the cost and saving per square meter of the proposed switch on/off and dimming controls compared with baseline lighting mode for case study 2 room (operation hours: 0:00 to 23:00). ....................... 286 Figure 6. 39 Case Study 2: the whole year financial analysis dashboard to analyze the cost and saving of proposed switch on/off and dimming controls compared with baseline lighting mode for case study 2 room (operation hours: 8:00 to 19:00). ..................................... 287 Figure 6. 40 Case Study 2: the quarterly financial analysis dashboard analyzed the cost and saving per square meter of the proposed switch on/off and dimming controls compared with baseline lighting mode for case study 2 room (operation hours: 8:00 to 19:00). ............... 288 Figure 6. 41 Shoebox Model used for validation of proposed lighting control algorithm. ........ 289 Figure 6. 42 March 23rd 23:00 (nighttime). ............................................................................... 291 xxii Figure 6. 43 March 23rd 23:00 (nighttime)- zoomed view. ....................................................... 291 Figure 6. 44 March 23rd 23:00 (nighttime) validation. .............................................................. 292 Figure 6. 45 March 23rd 23:00 (nighttime) validation - zoomed view. ..................................... 293 Figure 6. 46 June 23rd 23:00 (nighttime). .................................................................................. 294 Figure 6. 47 June 23rd 23:00 (nighttime) - zoomed view........................................................... 294 Figure 6. 48 December 23rd 23:00 (nighttime). ......................................................................... 295 Figure 6. 49 December 23rd 23:00 (nighttime) - zoomed view. ................................................ 295 Figure 6. 50 June 23rd 23:00 (nighttime) validation. ................................................................. 296 Figure 6. 51 June 23rd 23:00 (nighttime) validation - zoomed view.......................................... 297 Figure 6. 52 December 23rd 23:00 (nighttime) validation. ........................................................ 297 Figure 6. 53 December 23rd 23:00 (nighttime) validation - zoomed view. ............................... 298 Figure 6. 54 March 23rd 17:00 (before sunset). ......................................................................... 299 Figure 6. 55 March 23rd 17:00 (before sunset) - zoomed view.................................................. 299 Figure 6. 56 March 23rd 17:00 validation. ................................................................................ 300 Figure 6. 57 March 23rd 17:00 validation - zoomed view. ....................................................... 300 Figure 6. 58 June 23rd 19:00 (before sunset). ............................................................................ 301 Figure 6. 59 June 23rd 19:00 (before sunset) - zoomed view. .................................................... 302 Figure 6. 60 June 23rd 19:00 validation. .................................................................................... 303 Figure 6. 61 June 23rd 19:00 validation - zoomed view............................................................. 304 Figure 6. 62 December 23rd 16:00 (before sunset). ................................................................... 305 Figure 6. 63 December 23rd 16:00 (before sunset) - zoomed view. ........................................... 305 Figure 6. 64 December 23rd 16:00 validation. ........................................................................... 306 Figure 6. 65 December 23rd 16:00 validation - zoomed view. .................................................. 306 Figure 7. 1 Overall outline of the workflow diagram of Methodology and software and tools usage. .................................................................................................................................. 309 Figure 7. 2 The created dashboards applied in Case Study 1 (lighting operation hours dashboard only showed one type for on/off control here). ......................................................... 312 Figure 7. 3 The created dashboards applied in Case Study 2. (lighting operation hours dashboard only showed one type for dimming control here). .................................................... 313 Figure 7. 4 The summary of validation for the Control Algorithms. .......................................... 315 Figure 7. 5 The overall flowchart of the switch on/off control algorithm. ................................. 316 Figure 7. 6 The overall flowchart for the dimming control algorithm. ....................................... 316 Figure 7. 7 The overall Methodology diagram. .......................................................................... 317 Figure 7. 8 The overall flowchart of methodology workflow. ................................................... 319 Figure 7. 9 The proposed workflow to conduct the filed-test for the proposed control algorithm in future work. ............................................................................................................ 324 Figure 7. 10 The future work for integrating lighting control algorithms into Building Automation System (BAS). ........................................................................................................ 326 Figure 7. 11 Lighting Control Dashboard (source: https://www.hvac- inc.com/commercial/building-automation/)................................................................................ 327 Figure 7. 12 Neuron digital platform (source: https://www.arup.com/expertise/services/digital/arup-neuron) .................................................. 327 xxiii ABSTRACT Electric lighting consumed approximately 8% of total electricity consumption in the US residential and commercial sectors combined in 2020, accounting for an estimated 6% of total US electricity consumption (EIA, 2021). Reducing electricity consumption while maintaining an acceptable illuminance level is critical to achieving environmental sustainability and developing net-zero energy buildings. Lighting control systems, which have become widely adopted in residential and commercial buildings, aim to adjust the electrical lighting levels depending on daylight penetration automatically. Automated lighting control strategies include daylight harvesting, time scheduling, occupancy sensing, institutional tuning, and others. These control strategies can substantially save lighting energy and cost for the owner. Two simulation-based lighting control algorithms (switch on/off and dimming control) have been developed to reduce lighting energy consumption and operational cost, integrating time scheduling and daylight harvesting strategies by implementing daylight and lighting simulations. The intent was to create two control algorithms for the luminaires that predetermined their hourly on/off and dimming status based on pre-calculated daylight and lighting illuminance data from simulations. The proposed daylight-linked control algorithms were tested with two Rhino models for case studies. The luminaires and IES files were imported to the Rhino model for the lighting simulations. March 21st, June 21st, and December 21st with the climate-based sky file were first chosen as test dates to run the algorithm. The EPW weather file was acquired from the EnergyPlus website. Then, the annual hourly analysis was run with Python in Microsoft Visual Studio using four sky files (climate-based sky, CIE sunny with sun, CIE intermediate with sun, and CIE cloudy sky) to determine if having an actual sensor determining the overall sky xxiv condition could help in producing more accurate results. The Ladybug and Honeybee packages in Grasshopper were used to analyze the hourly illuminance from daylighting and luminaires. The illuminance data was structured with an array and matrix by applying Python scripts in Grasshopper with a linear programming mathematics method to calculate the maximum luminaire off and dimming levels while maintaining the desired illuminance level. The result shows that each luminaire's hourly on/off and dimming status and the reduced energy consumption can be theoretically calculated. The first case study showed that about 61% and 82% of the electricity could be reduced with switch on/off and dimming control algorithm compared with no lighting control mode when operation hours are from 8:00 to 20:00. The second case study showed that the electricity could be reduced by about 17% and 29% when operation hours are from 8:00 to 19:00. The proposed lighting control algorithm can be run by integrating hourly daylight and lighting simulation illuminance data and theoretically applied for the open-loop lighting controls for achieving electric lighting energy savings. Finally, seven dashboards were created in Microsoft Power BI to interactively visualize the luminaries’ electricity usage, cost-saving, the lighting operation hours, and illuminance distribution false-color maps (luminaires + daylighting) under four sky conditions after implementing the proposed simulation-based switch on/off and dimming control algorithms to assist better decision-makings. xxv Hypothesis The annual hourly daylight simulation data and lighting simulation data can be integrated with closed-loop daylight-linked lighting control logic to develop simulation-based daylight-linked switch on/off and dimming control algorithms Keywords: Daylighting Simulation, Lighting Simulation, Ladybug and Honeybee, Switch On/Off Lighting Control Algorithm, Dimming Control Algorithm Research objectives: • To improve building energy performance and facilitate decision making in the early design stage by employing lighting control system selection • To propose lighting control algorithms by integrating daylighting and lighting illuminance data from simulation using linear matrix programming • To minimize the lighting electricity usage while maintaining the desired indoor illuminance level based on the proposed simulation-based daylight-linked switch on/off and dimming control algorithms • To compare the lighting electricity savings between two proposed lighting control algorithms and visualize the comparison results of lighting energy consumption in dashboards to facilitate decision-making and provide insights for building energy management operators, lighting designers, and owners 1 Chapter 1 1. Introduction The advancements of Solid-state lighting have created more efficient light sources and lighting fixtures (Scartezzini, 2020). The recent innovation in lighting control systems has resulted in significant improvements in energy savings and visual comfort in workplaces (Kaminska and Oadowicz, 2018). The increased use of daylighting has emerged as an indispensable strategy for improving building energy efficiency by reducing the amount of electricity used by lighting. Daylighting, an effective strategy in modern architecture that entails introducing natural light into the space through windows and skylights to supplement or replace electric lighting, may assist reduce a building’s energy consumption and enhance visual comfort (Apian-Bennewitz et al., 1998) (Chen et al., 2014, Schiler, 1996) (Manning, 2006). Simulation tools are essential to identify the daylighting availability and distribution to determine the seasonal, daily, or even hourly changes in interior illuminance levels due to daylighting and electric lights (Reinhart and Herkel, 2000). This chapter introduces the current electrical lighting energy consumption in the U.S. and the lighting code briefly discusses the ASHRAE 90.1 standard and the International Energy Conservation Code (IECC). The following section introduces lighting control systems and light sensors and briefly discusses how they can be integrated into the Building Automation System (BAS). Then, the impacts of daylight on human health, how daylight can be an alternative to electric lighting, and how lighting controls can integrate with it are discussed. The next section talks about daylight availability metrics, including Daylight Factor (DF), daylight illuminance (Lux), Spatial Daylight Autonomy (sDA), and Useful Daylight Illuminance (UDI). Several 2 current daylight simulation software programs are listed, and, Ladybug and Honeybee for Grasshopper in Rhino are introduced. And the purpose of data visualization and the dashboard inside the Rhino was introduced. 1.1. Electric Lighting The term " electric lighting" refers to light that is generated electrically, such as a lamp, bulb, diode, or tube; this light may be dimmed, enhanced, focussed, directed, or colored, depending on the application (Sholanke et al., 2021). Electric illumination can be provided from different light sources, such as incandescent, fluorescent, and light-emitting diodes (LED) (Van Bommel and Van den Beld, 2004) (Borisuit et al., 2015). Although the invention of the light- emitting diode (LED) has improved 90% of lighting efficiently compared to incandescent (EnergyStar, 2020), electric lighting accounts for around 6 % of total US electricity consumption (EIA 2021). As a result, some mandatory codes were enacted for indoor lighting control to reduce lighting energy consumption, such as Title 24. The controlled lighting combined with natural lighting must satisfy the workplace’s minimum illuminance level for code compliance. This section introduces the current lighting energy consumption of the building industry and related building lighting codes in the U.S. 1.1.1. Lighting Energy Consumption According to the Annual Energy Outlook 2021 and Commercial Buildings Energy Consumption Survey (CBECS) published by the US Energy Information Administration (EIA), lighting consumed approximately 219 billion kilowatt-hours (kWh) of electricity in the US residential and commercial sectors combined in 2020, accounting for approximately 8% of total 3 electricity consumption in these sectors and an estimated 6% of total US electricity consumption (EIA 2021). In 2020, lighting electricity consumption from the residential sector was around 62 billion kWh, or approximately 4% of total residential sector power consumption, and an estimated 2% of overall US electricity consumption (EIA 2021) (Fig.1.1). Figure 1. 1 Electricity consumption by primary end uses in US residential sector in 2020 (EIA 2021) In 2020, the commercial sectors, including commercial and institutional buildings as well as the public street and highway lighting, used approximately 157 billion kWh for lighting, which equals to about 12% of total commercial sector electricity consumption and an estimated 4% of total US electricity consumption (EIA 2021) (Fig. 1.2). 4 Figure 1. 2 Electricity consumption by main end uses in US commercial sector in 2020 (EIA 2021) 1.1.2. Building Lighting Code The regulation of electric lighting in buildings used for various purposes in the United States began to be addressed with IESNA and CIE in the 20 th century. Then, the ASHRAE 90.1 standard began to develop the electric lighting regulation in 2013 (ASHRAE, 2016). The International Energy Conservation Code (IECC) is a residential and commercial building energy code updated every three years. The IECC references ASHRAE/IES 90.1 as an alternative standard, providing a choice to building designers. These standards were concerned with lowering the intensity and strength of lighting and identifying methods to save electrical energy by integrating lighting controls (Halverson et al., 2014). Prescriptive and obligatory standards for lighting systems are imposed by ASHRAE/IES 90.1-2016 and IECC 2018. There is a requirement for a broad diversity of lighting controls, all of which must be functionally tested and documented. The maximum design power must not be 5 exceeded. The owner must get all documentation about the lighting and control system (DiLouie, 2019) (Fig. 1.3). ASHRAE/IES 90.1-2016's lighting controls rules apply to interior and outdoor lighting modifications in existing buildings. These are activities that include the replacement of the lighting system. In these instances, all interior control measures, except for daylight- responsive control, must be followed and all external control provisions. This standard's automatic cutoff control rules also apply to modifications that replace more than 20% of the associated lighting load, including lamp-plus-ballast retrofits. The majority of commercial building energy regulations, with few exceptions, demand required lighting controls across commercial buildings. For instance, each area must have at least one general lighting control device: a manual switch, occupancy sensor, or manual-ON timer switch. Manual controls must be capable of reducing the amount of light (ASHRAE 90.1, 2016) (IECC, 2018). Figure 1. 3 Lighting Control Requirements Overall (DiLouie, 2019) 6 Both ASHRAE 90.1-2016 and IECC 2018 provide extensive criteria for required lighting regulation. There are, however, certain general exceptions. Exemptions from ASHRAE 90.1 include emergency lighting that is turned off automatically during the regular building operation, ornamental gaslighting, illumination needed by life/safety regulation or legislation, and external lighting not supplied by the building's electrical service (ASHRAE 90.1, 2016). Exemptions from the IECC include emergency or security lighting that operates 24 hours a day, emergency egress lighting that is turned off during the regular building operation, and illumination for internal egress stairwells, exit ramps, and exit corridors (IECC, 2018). Otherwise, any standard provision may have its unique exemption. Lighting controls automatically adjust the light output and intensity of connected lighting in a given space. Lighting control triggers come in various forms, including sensors, time clocks, schedules, daylight detection, occupancy/vacancy detection, and occupant-operated controls. California's Title 24, Part 6 of Building Energy Efficiency Standards (Energy Standards) governs the installation of lighting controls to eliminate wasteful or unneeded lighting, thus lowering energy usage (Title 24, 2019) (Table 1.1). Keyed notes A, B, C, D, E represent each space/area type for their minimum required control type (Table 1.2). Title 24 also has the requirement of indoor lighting power intensity (Table 1.3). 7 Table 1. 1 Title 24, part 6 - Matrix of Indoor Lighting Control (Title 24, 2019) 8 Table 1. 2 Keyed notes for each space/area type (Title 24, 2019) 9 Table 1. 3 Indoor Lighting Power Density Allowances (Title 24, 2019) The IESNA Lighting Handbook's recommended light levels and the Lighting Power Density (LPD) requirement in IECC 2021 are very specific (Table 1.4) (IESNA, 2021). 10 Table 1. 4 Recommended illuminance levels by space type (IESNA, 2021). Recommended illuminance levels are given in a range since various activities require different amounts of light even within the same area. In general, activities with low contrast and a high level of detail need more light, and tasks with low contrast and a lower level of detail 11 require less light (IESNA, 2021). In sum, ASHRAE 90.1-2016 and IECC 2018 regulate lighting power allowance and lighting control requirements, while IESNA provides the building indoor illuminance level recommendations. They focus on different aspects of lighting. 1.2. Introduction to Lighting Controls As the building industry progresses toward the development of “zero-energy houses,” the United States Department of Energy (US DOE), under the Architecture 2030 program (Architecture 2030), recognized that lighting control plays an important role in energy savings (Burmaka et al., 2020). Lighting controls are usually used to regulate lighting in a specific area independently. Dimmers, motion sensors, photocells, and timers that are hardwired to control specific groups of lights are included in this category. An intelligent network-based lighting control solution can use the central computer to communicate between different system input and output signals (DiLouie, 2007). It is widely used in commercial, industrial, and residential areas to ensure the appropriate lighting outputs. This system aims to maximize energy savings from the lighting system, comply with lighting requirements, and participate in green building and energy conservation initiatives. (DiLouie, 2007). 1.2.1. Smart Lighting Control Systems The current lighting systems use advancements in solid-state lighting and spectral technologies to provide functionality and illumination, combining light from various light- emitting diode (LED) sources to produce energy-efficient, high-quality, and healthful 12 illumination (Wen and Mishra, 2017). Additionally, such systems seek to utilize the data collected by different sensors in the lighted area to estimate the occupants’ number and position, user preferences, and available daylight. The lighting control algorithm then makes intelligent adjustments to individual LEDs from input signals to accomplish the required control goal while saving energy. While LEDs are more efficient than other lighting technologies, such as incandescent and fluorescent bulbs, integrating sensor data to control lights further improves energy efficiency (Wen and Mishra, 2017). Occupancy-based and daylight response lighting controls are two common strategies that contribute to lighting energy savings (Van et al., 2014) (Verso and Pellegrino, 2015). The former aims to dim or turn off the light in unoccupied space areas. Occupancy sensing control systems turn on lights in an area when motion is sensed and turn them off when a predetermined period elapses (Guo et al., 2010). The latter refers to harvesting daylight from windows or skylight, allowing light fixtures to be dimmed or turned off where and when daylight is sufficient (Van et al., 2014). The primary components of designing a sophisticated, intelligent lighting system are controller parameters, user preferences, desired lighting conditions, the controller, lighting fixtures, activity modeling, the light sensor/occupancy sensor, and illuminated space (Fig. 1.4) (Imam et al., 2016). 13 Figure 1. 4 A Flowchart shows the typical smart lighting system (Imam et al., 2016) 1.2.2. Light Sensor Since light sensors convert light energy (photons) to electricity, they are sometimes referred to as "Photoelectric Devices" or "Photo Sensors" (electrons) (Webster and Yazici, 2016). There are different types of light sensors: photoresistors, photodiodes, and phototransistors (Table 1.5). 14 Table 1. 5 Summary of Light Sensors (Shawn, 2020) Photoresistors, also known as light-dependent resistors (LDR), are the most often used type in light sensor circuits (Shawn, 2020). Photoresistors are used to determine if a light is on or off and to compare the relative amounts of light throughout the day (Shawn, 2020). Photoresistors operate similarly to conventional resistors, except that their resistance changes in response to the quantity of light they are exposed to. It is composed of cadmium sulfide cells, a kind of high-resistance semiconductor material that is quite sensitive to visible and near-infrared light (Itssubhadeep, 2017). Another type of light sensor is the photodiode, often known as a photodetector or photosensor. However, unlike LDR, it is more sensitive to light, rapidly converting it to a flow of electric currents (Webster and Yazici, 2016). Photodiodes operate based on what is known as the inner photoelectric effect. Simply defined, when a beam of light strikes, it loosens electrons, creating electron-holes that allow electrical current to pass through (Shawn, 2020). The more light there is, the higher the electrical current. Photodiodes are constructed of silicon and 15 germanium and include optical filters, integrated lenses, and large surface areas (Shawn, 2020). A phototransistor light sensor may be regarded as a combination of a photodiode and an amplifier. With the additional amplification, the phototransistors' light sensitivity is significantly improved. However, it does not perform as well as photodiodes in low light detection (Shawn, 2020). The photosensor can be integrated with the lighting control system to automatically adjust (switch on-off or dim) the luminous flux out of the luminaires due to the available amount of daylight (Delvaeye et al., 2016). The photocells are widely used in the current markets to continually measure daylight availability to turn off or dim the lights when daylight is sufficient or satisfies the lighting code requirement. By using a segmented, tamper-resistant lens, passive infrared (PIR) detection technology monitors a room for occupancy (Leviton, 2021). When a person enters or exits a sensor zone, the sensor detects motion and turns on the lights. The lights will stay on as long as the occupant passes across the sensor zones and the timeout period has not expired (Table 1.3). Table 1. 6 Example of Photocell and motion sensors in the current market ( https://www.leviton.com/en) 16 Take the Lumina RF Photosensor, for example, in the area with pendant fixtures with uplighting; it is typically placed at least 4 feet away from the uplighting fixture and 6 to 15 feet from the window. This type of sensor can measure the light from any source in the visible spectrum within a 60-degree cone at an 8 to 12-foot mounting height, typically ranging from 1 to 2000 lux. According to Title 24, part 6, photosensors located within the daylit zone must have at least one photosensor in code compliance. Figure 1. 5 The range of measurement and placement of Lumina RG photosensor (https://www.leviton.com/en) Most lux meters have a photodetector to measure luminance. For the best exposure, the photodetector is angled perpendicular to the light source—many lux meters use an articulated or tethered photodetector for this purpose. The user is supplied with readouts through an analog instrument or a digital LCD. Frequently, digital types incorporate simple operator inputs 17 (Kitazawa, 2015). Numerous digital types allow for the storage of measurements and provide an adjustable detection range (Fig. 1.6) or feed the information into a control system. Figure 1. 6 The example of a Lux Meter (photodetector) (https://www.globalspec.com/learnmore/optics_optical_components/optoelectronics/lux_meters_light_meters) The majority of lux meters are portable and readily taken to the worksite. Although articulated and tethered photodetectors need both hands to appropriately position the photodetector and module, they also provide measuring flexibility (Fig. 1.7). Some handheld models are equipped with a stand or other mounting framework, such as a tripod (Fig. 1.8). 18 Figure 1. 7 Measuring illuminance in the workplace (Kitazawa, 2015). Figure 1. 8 Mounting the sensor on a tripod (Kitazawa, 2015). According to IESNA, the work plane is defined as the plane on which a visual task is typically performed and on which the illuminance is specified and measured. Unless otherwise specified, the horizontal plane is assumed to be 0.76 meters (30 inches) above the floor (DiLaura 19 et al., 2011). Horizontal illuminance measurements should be taken by placing a lux meter or other type of sensor face-up flat on a horizontal surface at the desired spot (Fig. 1.9). Figure 1. 9 The definition of Horizontal illuminance (Lighting Research Center, 2021) The HDR sky luminance maps and outside global horizontal illumination values can be captured using a Skyometer sky scanning device (Humann and Mcneil, 2017) (Fig. 1.10) (Fig. 1.11). Figure 1. 10 The example of the Skyometer sensor mounted on the roof (http://www.terrestriallight.com/). 20 Figure 1. 11 Skyometer sensors locally monitoring and analyzing the dynamic sky conditions and changes (http://www.terrestriallight.com/). This type of sensor is typically mounted on the roof or building’s façades and needs to avoid surrounding obstacles. One example of this type of sensor is the Skyometer sky scanning system, which captures 360° high dynamic range (HDR) photographs of the sky vault using a charged coupled device (CCD) camera with a connected UV light filter and a Fujinon 1.4mm fisheye lens. The components are protected from the environment by a waterproof enclosure that has an optically clear glass dome above the lens (Terrestrial Light, 2021). The lens and glass dome combination was tested and shown to retain a correct, equiangular projection. The Skyometer integrates a Li-cor LI-210 photometer and a Li-cor LI-200 pyranometer for global illuminance (lux) and irradiance (w/m 2 ) measurements (Humann and Mcneil, 2017) (Fig. 1.12). 21 Global irradiance and illuminance values from sensors positioned beside the photoradiometer are used to calibrate the HDR image. It can be used to infer the luminance viewed through a window or to precisely duplicate the sky in a Radiance daylight simulation (Terrestrial Light, 2021). Figure 1. 12 The measurements of global illuminances (lux) and irradiance (w/m2 ) from the Skyometer sensor (http://www.terrestriallight.com/) 1.2.3. Building Automation System Building Management Systems (BMS) or Building Automation Systems (BAS) are computer-based control systems that are placed in buildings to monitor and manage the mechanical and electrical equipment inside the building, including ventilation, lighting, security, and fire systems (Jadhav, 2016). They gather and analyze data, providing insights and taking appropriate actions to improve efficiency and productivity (Jadhav, 2016). In terms of lighting services, control systems provide a significant potential for controlling lighting systems and 22 lowering energy consumption through integration strategies between daylight and electric lighting and occupancy-based strategies (Aghemo et al., 2014). The BAS can provide end-user with a user interface (UI) that allows them to set and adjust the control systems, visualize the system status on the dashboard, monitor the performance of the building system, and detect any potential issues of the system (Fig. 1.13) (Frank, 2015) (Zito, 2019). Figure 1. 13 A customized BAS dashboards offer the graphical UI to help users monitor and control multiple building systems. It includes the data visualization of the energy performance of the building (Frank, 2015). A building management system is comprised of two components: hardware and software. Hardware is used to collect data (analog and digital meters and sensors), analyze data (computers or servers and dashboard visualizations), and/or control actuators (Jadhav, 2016). The software program serves as a conduit between acquired data and the building management system or controllers. Additionally, this layer is often accompanied by a layer of software 23 applications that define the control methods (HVAC controls, lighting controls, etc.) as well as the instruction and optimization algorithms (Jadhav, 2016). Control through a network is widely implemented in building automation systems (Newman and Morris, 1994). A network-based control system enables real-time management and monitoring of building facilities and efficient maintenance of the building automation system via collecting, analyzing, and storing building- related information and data (Park and Hong, 2009). The American Society of Heating Refrigerating and Air-Conditioning Engineers (ASHRAE) developed the Building Automation and Control Network (BACnet) (ANSI/ASHRAE, 2004). Modern lighting control systems have evolved away from hardwired circuits and analog signals toward more adaptable digital solutions, and several complex technologies for digital lighting control systems have been proposed (Park and Hong, 2009) (Fig. 1.14). Automated lighting management is a key part of intelligent buildings and green buildings (Rubinstein et al., 1993) (Choi and Mistrick, 1997) 24 Figure 1. 14 BACnet-based building lighting control system. (Park and Hong, 2009) 1.3. Daylighting The sun provides the primary source of daylighting and energy on Earth, and it produces light in two ways: directly as sunlight or indirectly as it diffuses the skylight that is altered and redistributed by the atmosphere (Jan and Roberto, 2019). So the sunlight allows humans to see, but it also supplies energy and power to the whole earth’s environment. The term “daylight” refers to the combination of direct sunlight and diffuse skylight (Baker and Steemers, 2002). The quality and intensity of daylight are determined by geographical latitude, year’s season, time of 25 day, local weather, sky conditions, the geometry and orientation of the building (Wong, 2017). Daylighting describes the use of natural light in buildings (Jan and Roberto, 2019). 1.3.1. Daylighting Impacts on Well-Being and Performance While improving solar energy management is crucial for meeting low-energy performance goals, daylighting in buildings to fulfill human light needs is a far more difficult issue. Exposure to natural light and window views positively impacts human health, well-being, and productivity (Konis and Selkowitz, 2017) (Fig. 1.15). Figure 1. 15 The central zone of a big office building is devoid of natural light and vistas. Ambient overhead fluorescent lighting creates a uniform and steady-state lighting environment. Electrical illumination may be sufficient in such settings. (Konis and Selkowitz, 2017). In the United States, a greater focus on giving all users window views assists in the inversion of conventional office space design techniques by situating open-plan offices on the 26 perimeter of the floor plate and enclosed cellular office space in the center (Konis and Selkowitz, 2017). The majority of commonly utilized space requires a shift away from relatively “fat” floor plate designs with a low surface-to-volume ratio and toward “thinner” more elongated and complex building forms with a higher surface-to-volume ratio (Konis and Selkowitz, 2017) (Fig. 1.16). Even in buildings with large floor plates, emerging metrics for quantifying and rating available views, such as the view factor adopted by the LEED rating system (USGBC 2014). Figure 1. 16 Daylit perimeter zone on the sixth floor. Note: the lack of traditional ceiling-mounted electrical lighting fixtures (Konis and Selkowitz, 2017) Daylighting is associated with healthy buildings and indoor environmental quality. People strongly prefer windows in their workplaces (Puleo and Leslie, 1991) (Collins, 1975) and feel that windows increase productivity. This pervasive demand for natural light may indeed be 27 associated with increased human performance and well-being since daylighting has an effect on aesthetics, vision, and photobiology (Leslie, 2003). 1.3.2. Daylighting as an alternative to electric lighting Integrating daylight with electric light has the potential to decrease a building’s reliance on electric lighting; this is one of the easiest ways for increasing a building’s energy efficiency (Loutzenhiser et al., 2007) (Doulos et al., 2008). This method would result in savings in energy usage and reductions of environmental impacts (Ghisi and Tinker, 2006). Daylight has a high luminous efficacy (defined as lux or lumens in the numerator over watts in the denominator), which means that it contains and transmits less heat than electric lighting at the same light output, requiring less related cooling energy consumption and as well as less cooling, ventilation, and air-conditioning (HVAC) plants (Li and Lam, 2001). Additionally, daylight offers environmental advantages since the power generated from nonrenewable fossil fuels has significant environmental consequences. In general, it takes three units of fossil fuels to generate one unit of electricity, with the remaining two units being lost as heat. Consequently, conserving one unit of power usage equates to saving three times the amount of fossil fuels and reducing pollution (Li et al., 2008). This is often referred to as the difference between site and source energy. Daylight is also considered to be the light source that most nearly fits human visual reaction; its quality and color rendering is far superior to electric lighting, and so may produce a 28 more appealing inside the atmosphere while also providing visual contact with the outside world (Lim et al., 2012). 1.3.3. How Daylighting Can Work with Lighting Controls Introducing daylight in a building does not automatically lead to energy saving by itself; Daylighting can only potentially save money and energy if lighting control strategies or photosensors are applied to reduce or turn off electric lights when there is enough daylight (Wong, 2017). This is often called daylight harvesting (Schiler, 1996). It is recognized that daylight-linked lighting control is the way to accomplish the potential energy savings from daylight (Bellia and Fragliasso, 2019). Other factors such as window-to-wall ratio (WWR) and orientation have less influence on energy savings potential than lighting control system selection (Dubois and Flodberg, 2013). If lighting can be switched off automatically in response to the level of natural light available, the amount of electrical energy used for electric lighting may be decreased (Yang and Nam, 2010). This is especially true for photosensor-controlled electric lighting systems, which automatically adjust (switch on-off or dim) the luminous flux output of luminaires in response to the amount of available daylight (Delvaeye et al., 2016). High-frequency dimming and on-off control are two of the most extensively used daylight responsive lighting systems (Li et al., 2010). The on-off light control principle is that when the amount of inside daylight reaches a certain threshold, the electric lights are turned off; when the amount of indoor daylight falls below the threshold, the lights are switched on (Littlefair, 1999). Consequently, the annual energy savings associated with this type of light control are proportional to the number of working hours in a year during which the building's 29 daylight level exceeds the acceptable level. On-off controls use differential switching, time- delayed switching, and solar reset functions to minimize the disturbance caused by lights turning on and off rapidly when daylight levels fluctuate (Littlefair, 1999). High-frequency dimming control may adjust the light output of luminaires to supplement the available daylight level to meet the desired design level. The light output is related to the amount of electricity spent with this type of control (Krüger and Dorigo, 2008). Thus the quantity of daylight entering the area through windows or skylights determines the amount of electric light energy saved. Photosensors, which monitor daylight levels and transmit signals to electric ballasts, are used to perform both of these forms of light management (Krüger and Dorigo, 2008) (Schiler, 1996). This is especially valuable when daylight is often not quite enough. The switch on/off system could spend most of the time in the off position, whereas the dimming system continues to harvest savings (Schiler, 1996). 1.4. Daylight Performance Predictions Daylight simulations can assist design teams in forecasting daylight availability inside or outside buildings under specific sky conditions or over an entire year and interpret the results through conversion to certain performance metrics (Winkelmann and Selkowitz, 1985). Daylight simulations can be coupled with electric lighting simulations to ensure that both types of space illumination work in complement (Janak, 1997). Additionally, they can be embedded within an integrated lighting-thermal simulation to evaluate the total influence of a daylighting strategy on energy consumption for lighting, heating, and cooling (Reinhart and Wienold, 2011). 30 The essential elements and procedures required for a daylight performance simulation are building geometry, surrounding context, optical properties of materials, including ground and shadings, the status of luminaires, the grid of sensor points, the space type for analysis, the sky model, which includes geographical information and weather data. In addition, the daylight simulation engine requires to be ensured (Fig. 1.17) (Reinhart, 2011). Figure 1. 17 Daylight simulation elements and procedure (Reinhart, 2011) 1.4.1. Daylight Availability Metrics Illuminance-based metrics are commonly used to determine daylight availability in the space. They are generated mostly from a grid of upward-facing illuminance sensors placed at the work plane height to represent the distribution of daylight inside a space (Reinhart, 2011) (Fig. 1.18). 31 Figure 1. 18 The calculation of daylight factor in a sidelight area by using Autodesk Ecotect v5.6 (Reinhart, 2011) The Daylight Factor (DF) is defined as the ratio of the interior daylight illuminance at the work plane inside a building to the unshaded, horizontal illuminance outside the building under a CIE overcast sky. DF is the earliest daylight availability metric (Fig. 1.19) (Moon and Spencer 1942). Figure 1. 19 The definition of daylight factor: the ratio of an interior to the outside illuminance (Moon and Spencer 1942). 32 The daylight factor is only defined under a CIE overcast sky since it is a ratio of the interior to outside illuminances, and it is independent of the location of the building, time of day, and year. Given the CIE cloudy sky (Fig. 1.20), the daylight factor is also independent of the building’s orientation (Reinhart, 2011). The daylight factor is often not very useful as it does not take into account the amount of daylight due to the sun’s location and contribution. Figure 1. 20 The distribution of sky luminous above Boston on April 2nd at noon according to the uniform, 'old' CIE (Moon and Spencer 1942). Daylight illuminance indicates how bright daylight could illuminate the indoor environment. The method to explore how a space performs under clear sky conditions is to model space at key moments in a year, such as at 9:00, 12:00, and 15:00 on the solstice and equinox days (Reinhart, 2011). The concept of illuminance is also used in Green Building Rating 33 systems, such as LEED v4.1 under IEQ-Daylight (Option 2) (US Green Building Council, 2017). The false-color map represented the hourly illuminance on March 21 st at 15:00 for a part of the Airport Terminal in Beijing (Fig.1.21). It illustrates the distribution of daylighting and the value of illuminance (Lux) at each sensor grid. The simulation was done in Grasshopper using the Ladybug and Honeybee Legacy package, and the simulation result was visualized in Rhino. Figure 1. 21 The hourly illuminance on March 21st at 15:00 represented as a false-color map in Rhino (By author) Climate-based metrics are generated from the previously described yearly illuminance profiles, that is, hourly or even sub-hourly time series of interior daylight illuminances. This massive amount of data must be transformed into an intuitive measure to make this massive amount of data usable for design (Reinhart, 2011). 34 Several climate-based daylight availability metrics have been proposed (Mardaljevic et al., 2009) (Reinhart et al., 2006). As of 2018, the two approaches that were most widely accepted were spatial daylight autonomy (sDA) and useful daylight illuminance (UDI). sDA is defined as “the percentage of the occupied hours of the year when a minimum illuminance threshold is met by daylight alone” (Reinhart and Walkenhorst, 2001) (Hosseini et al., 2020). IESNA, followed by the US Green Building Council (USGBC), adopted sDA as a daylighting metric, assuming a year-round standard occupancy schedule of 8:00 to 18:00 with daylight savings time from March to November and a target illuminance level of 300 lux (IESNA, 2012) (US Green Building Council, 2017). The false-color map represented the sDA 300 lux for a part of the Airport Terminal in Beijing (Fig.1.22). Each sensor grid illustrates the percentage of the occupied hour that meets the 300 Lux threshold during the whole year. The simulation was done in Grasshopper using the Ladybug and Honeybee 1.2 version package, and the simulation result was visualized in Rhino. 35 Figure 1. 22 Daylight distribution of a part of Airport Terminal for sDA300 lux (By author) UDI is an alternative daylighting metric to sDA with lower and upper thresholds of 100 lux and 2000 lux, respectively (Nabil and Mardaljevic, 2005). These two values divide the year into three bins. The upper bin (UDI>2000 lux) is intended to represent times when there is an excess of daylight that may cause visual and/or thermal discomfort, the lower bin (UDI 100 lux) is meant to show the times when there is insufficient daylight, and the intermediate bin (UDI 100 - 2000 lux) is intended to represent times when there is ‘useful’ daylight. Later, Mardaljevic increased the highest limit to 3000 lux. In 2014, the UK Education Funding Agency (EFA) established the UDI-based daylighting requirement for its Priority School Building Programme. Being classroom-based, the EFA demands a space-averaged UDI 100 lux–3000 lux value of 80% 36 for a time basis of 8:30 to 15:00. The illumination ranges of 300 lux – 3000 lux of useful daylight illuminance (UDI) show the horizontal daylight illumination distribution values of the part of the Airport Terminal (Fig. 1.23). In this false-color map, each sensor grid illustrates the occupied hour’s percentage that meets the threshold of 300 lux to 3000 lux during the whole year. The simulation was done in Grasshopper using the Ladybug and Honeybee 1.2 version package, and the simulation result was visualized in Rhino. Figure 1. 23 Daylight distribution of a part of Airport Terminal for UDI 300 lux – 3000 lux (By author) 37 1.4.2. Daylight Simulation Software Overview The field of lighting simulation in software gradually came into being and started developing in 1968 (Kota and Haberl, 2007). Different calculation algorithms like radiosity in AGi32 and raytracing in Radiance and Relux were explored in different (Ochoa et al., 2010). The computerized daylight analysis in buildings began with repetitive electric lighting installations in closed rectangular rooms (Ochoa et al., 2010). Meanwhile, an attempt was made to integrate natural and electric lighting under fixed conditions using simplified formulae (Plant and Archer 1973). Apart from numerical results, the software’s capacity to generate physically based renderings was augmented (Kniss et al., 2003). The drawbacks of early simulation systems were their inability to simulate complicated geometries, their incapacity to conduct daylighting studies, and their output inaccuracy (Svendenius and Pertola, 1995). Lighting simulation is classified into two main categories, photorealistic rendering and predictive rendering (Ochoa et al., 2010). Photorealistic rendering deals with the production of high-quality rendered images that might not be completely accurate. Predictive rendering is concerned with accurately representing a scene using light’s physical principles (Banerjee, 2015). Different algorithms used in daylight simulation are summarized for a few lighting programs (Table 1. 7). 38 Table 1. 7 The summary of daylight simulation tools (Banerjee, 2015) Direct Calculation - They utilize specific formulae, assumptions, and simplifications to correctly assess the most frequent circumstances, such as a rectangle room with electric lighting. They are commonly used for electric lighting analysis in software (Ochoa et al., 2010). Raytracing is a rendering approach that simulates the behavior of light when it strikes virtual objects by casting rays in a scene, tracing a path of light across pixels in an image plane, and modeling what occurs when light strikes virtual objects (Whitted, 1980). These are typically 39 classified by the direction in which the rays originate: from the light source (forward tracing), from the observer’s eye (backward tracing), or from both the light source and the observer (bi- directional tracing) (Ochoa et al., 2010). Backward raytracing renders an object through a pixel on a virtual picture plane using only a few rays of light from a light source that can view a specific area of it (Ochoa et al., 2010). This approach enables the creation of realistic pictures with features such as reflections (Fig. 1.24). Figure 1. 24 Raytracing Process (Wikipedia Images) Radiosity is a rendering technique for scenes with diffusely lit surfaces. The assumption is that all surfaces in the surroundings are ideal (or Lambertian) diffusers, reflectors, or emitters. Such surfaces are considered to reflect incident light evenly in all directions. By dividing the environment into a collection of small areas, or patches, a formulation for the system of equations is facilitated. The radiosity over a patch is constant (Goral et al., 1984). It considers light leaving a light source, diffusely reflected a few times, and finally striking the eye (Dudka, 2013). In this method, all of the rays from a light source strike the surface of an object and 40 transmit energy (Ochoa et al., 2010). This surface then functions as a light source, transmitting a portion of the light energy to adjacent surfaces until all surfaces have been reached. These are view-independent, which allows for walkthroughs. Generally, this is utilized in calculations (Ochoa et al., 2010) (Fig. 1. 25). Thus, radiosity is not capable of modeling the specular (mirror- like) surface often found within buildings. Figure 1. 25 Radiosity Process (Ochoa et al., 2010) While ray tracing is an image-space approach, radiosity is an object-space algorithm. Because the view constrains the solution, ray tracing is often referred to as a view-dependent method, but this is somewhat misleading because the radiance itself is view-dependent. The term "view-independent" refers only to the view being used to limit the set of locations and directions for which radiance is calculated. The simulation differs in one respect: raytracing traces all rays from the viewer's eye back to light sources. Radiosity simulates the diffuse propagation of light, starting with the source of light (Chan and Tzempelikos, 2012) (Fig. 1.26). 41 Figure 1. 26 Top - Ray tracing: with reflections, soft shadows, and using textures. Bottom - Radiosity: within the Cornell box and Undistributed radiosity during iterative solving (Todd, 2014). Integrative approaches - Two or more algorithms are combined. Radiance uses both backward raytracing and radiosity. Calculation aids like the Monte Carlo method where the expected value is assumed to be correct and many expected outcomes are averaged for a solution. It requires many samples and often has accuracy issues (Ochoa et al., 2010). Nonetheless, if estimating energy savings due to daylight is the goal — particularly at the bottom 42 floor — the more precise approach (ray-tracing) should be used because of the higher accuracy of the results (Tsangrassoulis and Bourdakis, 2003). Daylight analysis can generate two main outputs (Ochoa et al., 2010): quantitative and qualitative output. Quantitative includes calculation results that are purely textual in form and calculation grids that display point-by-point data. Qualitative includes interactive renderings and graphical data (isocontours, false-color pictures, etc. for luminance and illuminance (Ochoa et al., 2010) (Fig. 1.27). Figure 1. 27 Examples of quantitative (left) and qualitative output (right) (Ochoa et al., 2010) Knowledge of lighting techniques and trends is required to interpret the results (Hong et al., 2000). Appropriate data visualization and interpretation are required when dealing with huge quantities of simulation results (Glaser et al., 2004) (Table 1. 8). The following comparison examined the many output interpretations available. 43 Table 1. 8 The example of lighting simulation outputs (Ochoa et al., 2012) 1.5. Rhinoceros 3D & Grasshopper Rhinoceros 3D, commonly referred to as Rhino, is a commercial three-dimensional computer graphics and computer-aided (CAD) design application software developed by Robert McNeel & Associates. Rhino is used to design, analyze, and generate complicated geometries that include non-uniform rational B-splines (NURBS) curves, surfaces, solids, point clouds, and polygon meshes. Rhinoceros developer tools enable third-party developers to build plug-ins and add-ons for the open-source Rhino modeling program, making it a versatile modeling platform. (Robert McNeel & Associates, 2021) (Fig. 1.28). 44 Figure 1. 28 An example of Rhinoceros 3D Model (By author) Grasshopper is a visual programming tool, graphical algorithm, and parametric modeling editor working tightly with Rhino. Grasshopper makes it possible to develop algorithms based on pre-coded components. It is real-time interaction with Rhino, allowing changes to the grasshopper canvas to be displayed directly in the Rhinoceros viewport (Scott, 2021) (Fig. 1.29). Additionally, Grasshopper also supports various open-source plug-ins that streamline the process of design, simulation, analysis, and other features without additional programming by the user. 45 Figure 1. 29 A Grasshopper script example to conduct daylight simulation (By author) 1.5.1. Ladybug and Honeybee for Grasshopper Ladybug and Honeybee are two open-source plugins for Grasshopper developed by Mostapha S. Roudsari. Ladybug imports standard EnergyPlus Weather files (.EPW) into Grasshopper, providing a range of interactive 2D and 3D climatic visualizations that assist in decision-making, including sunlight-hours modeling, weather data importing, solar radiation analyzing, and others (Fig. 1.30). It uses local weather data for simulation in an EPW format (Roudsari, 2021). 46 Figure 1. 30 The features and functionality of the Ladybug plugin (source: https://www.ladybug.tools/ladybug.html) (Roudsari, 2021) Honeybee provides detailed daylighting modeling that can be applied during the mid and later design stages, which creates, runs, and visualizes the results of daylight simulations using RADIANCE, energy models with EnergyPlus/OpenStudio. It can also serve as an object- oriented Application Programming Interface (API) for these simulation engines and link to Grasshopper (Roudsari, 2021) (Fig. 1.31). 47 Figure 1. 31 The features and functionality of the Honeybee plugin (source: https://www.ladybug.tools/honeybee.html ) (Roudsari, 2021) Radiance is a simulation engine programmed by Greg Ward and is one of the most widely used engines for simulating architectural lighting (Roy, 2000). The input files define the scene's geometry, materials, luminaires, and the time, date, and sky conditions (for daylight calculations). All estimated values include spectral radiance (i.e., luminance + color), irradiance (illuminance + color), and glare indices. The results from the simulation can be shown in various ways, such as false-color images, numerical values, and contour plots. The fundamental benefit of Radiance over more straightforward lighting calculation and rendering technologies is that the geometry and materials that may be simulated are not constrained. Architects and engineers utilize radiance to forecast the illumination, visual quality, and appearance of innovative design environments, while academics use it to assess novel lighting and daylighting technology (Fritzl and AMcneil, 2019). Since Radiance has no interface, Honeybee components are developed to 48 describe Radiance settings. Daysim utilizes backward raytracing to run daylight simulations and yearly DGP simulations (Reinhart, 2013). Honeybee is capable of conducting the illuminance studies to output the hourly illuminance level (Lux), annual daylight studies to output the Daylight Autonomy (DA), Continuous Daylight Autonomy (cDA), Useful Daylight Illuminance (UDI), and Annual Sun Exposure (ASE). Additionally, Honeybee can conduct the glare analysis to output the Daylight Glare Probability (DGP) and electric lighting simulation (Reinhart, 2021) (Fig. 1.32). Figure 1. 32 Honeybee daylight related analysis and the output matrix (source: https://www.ladybug.tools/honeybee.html) (Roudsari, 2021) 49 1.5.2. Python in Grasshopper Rhino allows access to its algorithms through the Rhino SDK (software development kit). The most basic SDK for Python is RhinoScriptSyntax. The "GHPython Script" component works as an integrated part of Grasshopper; it can be used to create specialized functionality that opens up great potential beyond the standard components (Scott, 2019). The “GHPython script” component is from the "Script" panel under the "Maths" tab, and it can get inputs and generate outputs from and to other standard Grasshopper components. The “GHPython script” component also provides a dynamic UI with control over the number of inputs and outputs (Fig. 1.33). Figure 1. 33 The “GHPython script” component in Grasshopper overall Two inputs and two outputs are included in the basic script component. The user can modify the names of the input and output and the data type and structure of the input (Scott, 2019). The user can add or delete input and output parameters by zooming in on the component 50 and clicking the plus and minus bottom (Fig. 1.34). To write the code in the “GHPython Script” component, the first step is to double-click on the component to bring up the Python editor (Fig.1. 35). Figure 1. 34 To add or remove the input and output parameters in the "GH python" component Figure 1. 35 The Grasshopper Python Script Editor example 51 1.5.3. Microsoft Visual Studio Microsoft Visual Studio is an integrated development environment (IDE) from Microsoft. It can be used to create computer programs, websites, online applications, web services, and mobile applications. Microsoft software development platforms such as Windows API and Windows Forms. It is capable of producing both native and managed code. Microsoft Visual Studio is a Python-compatible environment; the python code can run in Visual Studio using the Microsoft Python extension (Microsoft Visual Studio, 2021) (Fig. 1.36). Figure 1. 36 The UI of Visual Studio, the example shown by using the Python code in Visual Studio to operate the linear programming of Matrix 1.5.4. Microsoft Power BI Microsoft Power BI is a business intelligence service. Its objective is to give interactive data visualizations via an interface that enables end-users to generate reports and create dashboards that provide interactive data insight for decision-making (Microsoft Power BI, 2021) (Fig. 1.37). 52 Figure 1. 37 The UI of Microfost Power BI, the example shows the lighting electricity usage of three typical days with the switch on/off and dimming controls. Power BI can connect to various data sources, including an Excel sheet, a CSV file, a SQL server, and a set of hybrid cloud-based and on-premises data warehouses (Fig. 1.38). Figure 1. 38 The data source connection selection of Microsoft Power BI. 53 1.6.Summary This chapter introduced the U.S.'s current electric lighting energy consumption and the lighting code, briefly discussing the ASHRAE 90.1 standard and The International Energy Conservation Code (IECC). The following sections introduced lighting control systems and light sensors and briefly discussed how they could be integrated into the Building Automation System (BAS), the impacts of daylight on human health, how daylight can be an alternative to electric lighting, and how lighting controls can be integrated. Daylight availability metrics, including Daylight Factor (DF), daylight illuminance (Lux), Spatial Daylight Autonomy (sDA), and Useful Daylight Illuminance (UDI), were described. Furthermore, current daylight simulation software was listed, including Ladybug and Honeybee plugins in Grasshopper, which works within Rhino. The programming language Python in Grasshopper was shown. 54 Chapter 2 2. Background and Literature Review The simulation-based lighting control algorithm development requires a deep understanding of lighting fixtures, photosensors choice and placement, and the process of lighting systems design. Additionally, it is important to understand the closed-loop, open-loop daylight-linked switching and dimming controls and their control algorithms. Furthermore, it is significant to comprehend the parameters of daylighting simulation, its calculation engine, and the reliability of the daylight simulation results. This chapter introduces past research on the daylight-linked control system, closed-loop and open-loop control algorithm, and the radiance- based daylight simulation using the Ladybug and Honeybee plugin in Grasshopper. 2.1. Lighting System Design Lighting system design plays an important role in contributing energy savings and visual comfort to the occupants. Typically, the architects design the conceptual model of the building, determining the window-to-wall ratio (WWR) and the size of any skylights. The lighting designer needs to figure out the transmission properties of these glass materials and the reflectance of the opaque elements in the building. Then the lighting designer may conduct daylight availability analysis. Identifying effective daylighting solutions is a lengthy task. The system has many components, and their possible functions are very diversified and often subtle (Fig. 2.1). 55 Figure 2. 1 Lighting design integrates with daylighting (NCSU, 2013). After evaluating daylighting availability and defining the control strategy, the lighting system can be developed. And devices must be compatible with the particular control strategy (these will be further discussed in the following sections). The designer has to select and position photosensors, lamps and luminaires, related accessory devices such as relays or ballasts, and zone lighting fixtures based on the interior daylight distribution (Bellia et al., 2016) (Jason, 2016) (Fig. 2.2). The photosensor is the primary component of a daylight collecting system. The selection and placement are critical in designing daylight-linked lighting controls (DLCs) (Bellia et al., 2016). 56 Figure 2. 2 The overall workflow of the lighting design process (Jason, 2016). The example below shows the ceiling lighting plan of Westfield Terminal 2 in LAX (Fig. 2.3) and the interior lighting view (Fig. 2.4). The lighting design is by KGM Architectural Lighting. 57 Figure 2. 3 The ceiling lighting plan of Westfield Terminal 2 in LAX (KGM Architectural Lighting, 2017) Figure 2. 4 The interior lighting view of Westfield Terminal 2 in LAX (KGM Architectural Lighting, 2017). 58 2.1.1. Light-Emitting Diode (LED) As lighting technology advances and more sources switch to solid-state lighting (SSL) using LEDs (Fig. 2.5), savings possibilities grow significantly due to LEDs' inherent digital control capability (Poland, 2014). Figure 2. 5 Interior views of SunLuminaire TM fixtures and their installation (Konis and Selkowitz, 2017). When LED sources are used, the relationship between the power consumption of the luminaire and the dimming level can be approximated as proportional (Rossi et al., 2015) (Caicedo and Pandharipande, 2012). There are two ways to dim LEDs: reducing the forward current (continuous current reduction, CCR) or adjusting the duty cycle using pulse width modulation (PWM). In general, PWM is preferred since it establishes a linear relationship between light output and duty cycle (Gu et al.,2006) (Fig. 2.6). An energy-saving controller was demonstrated to be capable of adjusting the dimming levels of LED sources in response to sensor inputs. They revealed that the brightness of the examined LED string was directly proportional to the string current and PWM duty cycle using PMW (Chew et al., 2016) (Choi et al., 2015) (Fig. 2.7). 59 Figure 2. 6 A Standard PWM circuit with the switch in series (Narra and Zinger, 2004) Figure 2. 7 Signal waveforms at PWM duty cycles of (a) 30 %, and (b) 90 % and their corresponding snapshots of the LED brightness at PWM duty cycles of (c) 30 %, and (d) 90 % (Choi et al., 2015) 60 Each test LED luminaire's relative light output and power consumption were compared to the control signal (Fig. 2.8) (Doulos et al., 2017). The result from the test shows that linear relationships can be assumed for a specific LED driver (Doulos et al., 2017). Additionally, the relative power consumption against the light output percentage of the test LED luminaires indicated the different consumed power when the light output is the same. In the example, when 40% relative light output is needed, luminaire A and D LED have 38.5% and 47.5% relative power consumption, respectively (Fig. 2.9) (Doulos et al., 2017). Figure 2. 8 The relationship between relative consumed power and LED luminaires' control signal (Doulos et al., 2017). 61 Figure 2. 9 The relationship between relative consumed power with light output for test luminaires LED luminaires (Doulos et al., 2017). Fluorescent lights can also be dimmed to match the illuminance of daylight using dimmable ballasts and a daylight sensor (Doulos et al., 2017) (Fig. 2.10). Most sensors detect a quantity proportional to the amount of light emitted by natural and electric lighting. However, sensors may also detect direct sunlight, for example, when directed at a window (Tetri, 2002). 62 Figure 2. 10 Photometric and electric measurements for dimming levels of one T5 luminaire (Doulos et al., 2017) 2.1.2. Photosensor Choice and Placement The primary component of daylight-controlled devices is the photosensor. In order to guarantee the system perform optimal functions, it is essential to choose a photosensor with the best spatial response (sensitivity to incident radiation from various directions), spectral response (sensitivity to incident radiation of different wavelengths), and range (a limited range of output signal values in which light measurement is accurate) (Bellia et al., 2016). Additionally, the photosensor's placement is critical to ensuring high control performance. The photosensor signal should represent the illuminance measured at the workplane. The position of the photosensor must be determined by the ratio of the task illuminance (E) to the photosensor signal (S) to make sensor detections as indicative of workplane illuminance as possible. As a result, the E/S ratio is critical and may define the system's performance (Choi et al.,2005). 63 It was confirmed that illuminance values measured on the ceiling and the position of the visual task are distinct and that their ratio varies during the daytime depending on the location and orientation of the windows (Valíček et al., 2016). The relationships between measured illuminances of daylight on the workplane and a ceiling point on two different days in a test room were concluded (Bonomolo et al., 2017) (Fig. 2.11). Figure 2. 11 Relationships between daylight illuminance values measured on the workplane and at the equivalent position on the ceiling on a sunny (A) and overcast (B) day (Bonomolo et al., 2017). 2.1.3. Demand Response (DR) Demand response (DR) is a temporary modification of electrical equipment's power usage to balance available supply and demand. For example, reducing peak energy use during hot summer afternoons when electricity demand is high due to air conditioning helps mitigate the risk of blackouts. Additionally, DR may help save money on energy costs since utility prices are sometimes much higher during periods of grid-wide strong energy demand. (Pandharipande and Newsham, 2018). The lighting control system is an attractive DR resource decision (Newsham and Birt, 2010) since it can be controlled in real-time and dimming permits incremental alterations without 64 attracting undue notice or creating trouble to occupants (Husen et al., 2012). The lighting system requires to be connected to both the utility demand response and price signals and the more comprehensive building automation system (Pandharipande and Newsham, 2018). 2.2. Daylight-linked Lighting Controls (DLCs) Lighting control based on daylight availability can yield maximum savings, provided variables affecting daylight availability, such as direction and obstructions (Williams et al., 2012). Daylight-linked controls can be used to switch lights on and off, which is more suited for outdoor and standard space lighting fixtures, or in combination with dimmable electronic fixtures to provide the required degree of electric lighting when daylight is present (Li et al., 2010) (Li and Lam, 2001). Simple daylight-controlled on-off switch configurations are suitable for outside areas such as sports facilities, corridors, and parking lots. This would assure that lights are switched off throughout the day, eliminate the need for manual monitoring, and save labor time (ul Haq et al., 2014). Previous research investigated daylight-linked controls by using two different types of photosensors mounted on the ceiling: The first one faces down, has a symmetrical spatial response, and has a 60° targeting angle. Another one faces to the window, has an asymmetrical reaction, and is defined by a 40° aiming angle toward a vertical plane perpendicular to the window and a 60° aiming angle toward a horizontal plane parallel to the ceiling (Fig. 2.12) (Bellia & Fragliasso, 2017). They are both daylight and electric light sensitive. Daylight illuminances were simulated at several calculation points inside a rectangular grid (distance between points 0.5 m) located 0.75 m above the floor and corresponding to the desk (Fig. 2.13). 65 The threshold was set to 500 lux illuminance for code compliance (Bellia & Fragliasso, 2017). For example, the IESNA standards for typical office work illuminance values were applied as a guideline; a range of 300 to 500 lux is considered to be comfortable and functional for paper and computer tasks in an office environment (DiLaura et al., 2011). Figure 2. 12 Example of photosensors location and its spatial responses (Bellia & Fragliasso, 2017) 66 Figure 2. 13 An Example room for the lighting control simulation test (Bellia & Fragliasso, 2017). On the principle of how they operate the lighting system, DLCs can be classified into two categories: (1) daylight-linked switching and (2) daylight-linked dimming (Li et al., 2010). Daylight-linked switching enables lights to be controlled by switching between 'On' and 'Off' states in response to available daylight. Additionally, there may be multi-level switching. For example, depending on the amount of available daylight in a certain control zone, 33%, 50%, or 66% of light in that zone may be turned off (ul Haq et al., 2014). This is called “stepped dimming” or stepped controls. These states are between 100% on and 100% off states. Daylight- linked dimming is the electronic lamps that continuously adjust the light output based on daylighting availability to achieve the required illuminance level. Dimming requires the use of dimmable luminaires to maintain the lamp's illuminance (ul Haq et al., 2014). Several factors affect the selection between dimming and switching controls. The cost of installing the system is one of the key driving factors (ul Haq et al., 2014). The daylight availability pattern will also determine the effectiveness of the systems in the room throughout the day. Occupant behavior in a room, i.e., how users move through the area, the type of task 67 they conduct, the frequency with which they enter and exit, can significantly impact a daylight- linked system's performance and energy savings (ul Haq et al., 2014). Dimming electric lighting has no detrimental effect on lamp life compared to switching controls (Tetri, 2002). Switching and dimming system have their advantages and disadvantages (Table 2.1). Dimming strategies show their main advantage in sky situations that are close to the desired design level, where a stepped or on/off system would spend significant time in the off position. Table 2. 1 Comparison between daylight-linked switching and dimming controls (ul Haq et al., 2014). 2.2.1. Daylight-linked Lighting Switching Controls Daylight-linked switching enables the lights to be controlled by switching between the "On" and "Off" states in response to available daylight. It is intended to automatically switch electrical lights on and off when illuminance levels vary over time within a range of predefined values (Littlefair, 2001). The daylight illuminance measured by an outdoor photocell at every minute was checked to the target illuminance level, and if it was less than target lux, control kept the lights on since the last scan. If it exceeded the target lux, the lights would be switched off. One disadvantage of on-off lighting controls is their frequent switching, which is especially apparent when the sky is unpredictable and daylight swings about the desired illuminance level. Numerous variations of 68 standard on-off control exist to restrict the number of on and off cycles (Littlefair, 2001). The fundamental one is the "differential switching or dead-band" photoelectric control, which employs two switching illuminance levels: turning on the lights and turning them off. The primary benefit is that it can decrease the pace at which the light switches on and off rapidly when the illuminance oscillates near the necessary value (Li et al., 2019). Additionally, it reduces the obtrusiveness of lamp shutdown since it occurs when daylight accounts for a more significant percentage of the illuminance to which the eye is accustomed (Li et al., 2019). The daytime illuminance data was measured and recorded at 2.5-min intervals, daily, from 9:00 to 18:00 in a 13-story institutional building in Hong Kong to study standard and differential switching controls. The percentage of time the lights were turned off was determined after the working day. For all control methods studied, if the daylight illuminance was larger than the switching on illuminance at 9:00, it was assumed that the lights were switched off at the start. It was believed that the measured illuminance values remained constant during the 2.5 minutes (Li et al., 2019). The conventional switching control provided 200, 225, and 250 lux switching illuminances. The target illuminance (200 lux) was evaluated for differential switching controls and lights turn-off at 210, 220, 250, 280, and 300 lux. The range and the average number of switch-offs each day, as well as the percentage of the working day (9:00-18:00) when lights are turned off in the four zones under standard and differential switching controls, are summarized (Table 2.2). It demonstrates that the energy savings associated with different daylight-linked 69 on/off controls are highly correlated with the switching frequency associated with those controls(Li et al., 2019). Table 2. 2 The results of standard switching controls and differential switching controls (Li et al., 2019). The scatter of data points is explained by the wide range of energy savings associated with various switch-off numbers. The relationship between percentage savings and average daily switch-offs in Zone 2 under differential switching was created using regression methods and mathematical expressions to associate the average daily switch-offs (N) with the lighting energy savings (LES) (Fig. 2.14) (Li et al., 2019). 70 Figure 2. 14 Correlation between lighting energy savings and the average number of switch-offs within differential switching controls (Li et al., 2019). Lighting energy savings were evaluated at a switching illuminance of 200 lux and varied between 64% and 79%, with an average of 3.9 to 7.9 switch-offs. When 200 lux is used for on and 300 lux for off, the number of switch-offs may be decreased to one, resulting in a 71% energy savings in Zone 4. (Li et al., 2019). Thus, the ideal on/off method should balance a low rate of switching off with a significant amount of energy savings during the lamp's life. 2.2.2. Daylight-linked Dimming Controls When using dimmable electronic lamps, dimming systems constantly control lighting outputs. Dimming is more expensive than switching since it requires the use of dimmable luminaires to maintain the lamp's illuminance level (Li et al., 2010). When the fluorescent lamp was dimmed according to the daylight level, the lightings were dimmed continually. For example, one study found that the fluorescent lamp in a luminaire at the back of the test room 71 and near the window was rapidly dimmed several times a day from 70 % light level to 1% light level with different fluctuations (Fig. 2.15) (Tetri, 2002). It was concluded that lamps were continuously burned even at very low light levels and dynamically dimmed without the lumen reduction, which can cause the replacement of lamps. Another study measured the illuminance data of daylight, LED lighting, and the combination for the same day, considering the continuous dimming operation and in “simulated” ON/OFF operation (from 6:00 to 21:00) in-office use (500 lx for the target on the task area) (Fig. 2.16). It aimed to illustrate the relationships between daylighting fluctuation and the illuminance swing on the working surface from LED light with different light patterns. Figure 2. 15 The luminaires near the window and at the rear of the room were dimmed in proportion to the amount of daylight illuminance (Tetri, 2002). 72 Figure 2. 16 Example of illuminance values measured on the same day in continuous dimming operation and “simulated” ON/OFF operation (Bonomolo et al., 2017) Dimming systems constantly control the emitted flux, resulting in gentler oscillations than those produced by switching systems. They may, however, be very common (Bellia and Fragliasso, 2018). Typically, dimming systems save more energy than switching systems. For example, the operation of lighting systems was observed in two lecture rooms at the University of Trento's Faculty of Engineering. They were equipped with various types of automated controls. It was discovered that the combination of the switching on/off with an occupancy-based system could obtain a 40% energy reduction. The savings increased to 65 % when a dimming DLC was installed (Chiogna et al., 2012). However, the installation of the dimming system can be various depending on the required task illuminance level and indoor daylight availability. For instance, when indoor illuminance levels are consistently high during the entire year but the required task illuminance is low, it can be more efficient to adopt a switching system (Li et al., 2010). 73 The energy savings achieved by controlling a dimmable DLC placed in a south-facing workplace in Athens were compared using different control algorithms (Doulos et al., 2008). A linear proportional system observed annual savings of between 66.9% and 72.82% and between 70.35% and 76.09% with the reset control, depending on the installed ballast. Additionally, when the overall illuminance at the work plane was tested, the reset control did not always produce the appropriate illuminance level (Doulos et al., 2008). Energy savings gained using stepped and dimmed control algorithms in tested workplaces indicated that stepped controls typically generate fewer savings than dimming controls; the performance gap between the two system types narrows as the glazing area increases (Ihm et al., 2009). The zoning distribution of luminaires is also crucial to the effectiveness of daylight integration since interior daylight dispersion may result in large luminous gradients, especially in sidelight contexts (Bellia and Fragliasso, 2018). The performance of a DLC system was evaluated in an open-plan office setting, with each workstation equipped with integrated photocell-controlled lighting. The potential energy savings for different work situations were estimated, and it was observed that as the window distance reduced, the energy savings increased (Galasiu et al., 2007). The zoning of luminaires in a classroom lighted by three rows of fluorescent lighting bulbs showed that by grouping three rows differently and controlling them differently, they generated savings ranging from 23.4% to 70.4 % (Li et al., 2010). 2.2.3. Energy Savings from Daylight-linked Controls Many studies have been conducted to evaluate the potential energy savings associated with daylight-linked lighting control systems. These studies' scope and methods vary depending 74 on the kind of room to which the control is applied, the mechanism of lighting control, and the research methodology (Table 2.3) (ul Haq et al., 2014). Table 2. 3 Summary of energy savings uses daylight-linked controls in the literature review (ul Haq et al., 2014). Additionally, numerous research projects have been conducted on estimating energy savings through simulations (ul Haq et al., 2014). Consistency between the estimated and measured energy savings is challenging to achieve, as daylight-linked controls are dependent on many factors that are difficult to simulate precisely (Williams et al., 2012). A daylighting simulation was conducted in a corridor by using Radiance lighting simulation software. The simulated results were compared to on-site measurements. The findings indicated that the majority of energy savings were overstated. The disparity was substantially smaller during periods when significant energy savings were obtained. The results revealed that the discrepancy between the predicted and measured data was driven by different factors, such as occupant behavior, fluctuating sensor light detection, and imprecise simulations of a closed-loop control system. (Li and Tsang, 2005). 75 Other studies have discovered that daylight simulations are a reliable source of information (Li and Tsang, 2005). An analysis approach was created to consider the aspects that affect daylight availability, such as building geometry, glazing area, and glass properties. Their approach resulted in energy savings estimates consistent with experimental data (Krarti et al., 2005). Other researchers provided additional validation for the estimations derived from this method (Ihm et al., 2009). It was recommended to use advanced behavioral models in daylight control simulations to improve their accuracy (Bourgeois et al., 2006). In sum, it is possible to simulate or calculate the energy savings of daylight-linked controls, and the energy savings results are applicable. 2.3. Daylight-linked Systems Based on the Algorithm of Control Daylight-linked systems can be categorized into closed-loop and open-loop systems based on their control algorithm (Ruck and Aydinli, 2000). A closed-loop system continuously monitors the lux levels in the control zone, taking both natural and electric light into account. The control system receives constant input on the change in lamp light levels caused by daylight availability, and it may make appropriate adjustments depending on the feedback (ul Haq et al., 2014). The flowchart of a closed-loop algorithm can be read from the reference lux level, which is typically the code requirement illuminance level. Then, the photosensor measures the interior illuminance level and may be compared with the code requirement level by the computing chips. This signal will be sent to the lux level controller, which will control the electric light. Again the interior lux level is measured by the photosensor. Thus, it is a closed-loop algorithm (Fig. 2.17). 76 Figure 2. 17 The flowchart of the daylight-linked closed-loop algorithm (ul Haq et al., 2014). However, open-loop systems do not get input from electrical lighting levels but rather from available daylight levels. It communicates with the controller through a signal dependent on the quantity of available daylight to give the appropriate lighting output (ul Haq et al., 2014). The flowchart of an open-loop control system starts from the photosensor, which typically measures the outdoor sky illuminance level and sends the signal to the lux level controller. The controller takes action on electric lamps and interior lux level changes (Fig. 2.18). Figure 2. 18 The flowchart of the daylight-linked open-loop algorithm (ul Haq et al., 2014). Closed-loop systems can be highly effective when controlling a single control zone or relatively limited areas, such as private offices. Open-loop systems are preferable when the goal is to control multiple control zones with a single sensor. Open plan office spaces are good targets for open-loop daylight system implementation (ul Haq et al., 2014). The capacity of a control system to compensate for variations in indoor daylight depends on the control strategy used. Researchers propose five different kinds of control algorithms: “open-loop switching, closed-loop switching, open-loop dimming, closed-loop linear 77 proportional control (or sliding setpoint control), closed-loop constant setpoint control (or integral reset control), and tri-level control” (Fig. 2.19) (Bellia et al., 2016). These will be further explained in the next sections. Figure 2. 19 Typical control algorithms: a)Open-loop switching algorithm; b)Closed-loop switching algorithm; c) Open-loop dimming algorithm; d) Closed-loop linear proportional control (or sliding setpoint control) algorithm; e) Closed-loop constant setpoint control (or integral reset control algorithm; f) Tri-level control algorithm (Bellia et al., 2016) 78 2.3.1. Closed-loop Daylight- linked control systems The photosensor is positioned and oriented under closed-loop daylight-linked control to detect a portion of the light emitted by the controlled lamp and any daylight present in the daylighting zone. The system changes the dimming level (or turns the lights on and off) to maintain the desired overall illuminance level as detected directly by the photosensor (Fig. 2.20). Figure 2. 20 Closed-loop Daylight- linked control systems (http://intelliblinds.com/daylightharvesting.html) The closed-loop daylight-linked control algorithms include switching, linear proportional (sliding setpoint), and constant setpoint (or integral reset) control algorithms (Bellia et al., 2016). In closed-loop switching control, the output of the lamps is either 0% or 100%, and the photosensor receives signals either increasing daylight levels or decreasing daylight levels. The zone between the on point and shutoff point is called the dead-band; in this zone, the controller takes no action on luminaires. The closed-loop switching has a delay time compared to open- loop switching (Fig. 2.19b). The dimming curve is offset in the closed-loop linear proportional control, preventing dimming until the photosensor's optical signal reaches the required set point level. This offset facilitates the efficient application of a proportional control algorithm in closed- 79 loop systems (Fig. 2.19d). In constant setpoint (or integral reset) control algorithms, the feedback loop maintains that the setpoint value can be customized set. The controller carries out a simple subtraction of the latest photosensor signal from the setpoint (Fig. 2.19e) (Bellia et al., 2016). The acquired illuminance is monitored using photodetectors mounted on the ceiling (Bierman, 2015). The goal in office applications is to produce illuminance levels at workstations that exceed the minimum requirements of the lighting level. A photodetector detects natural and electric light reflected from all luminaires within its range of view. Thus, it is necessary to carefully construct closed-loop feedback control to acquire the luminaires' dimming levels to preserve system stability and responsiveness to light changes (Caicedo and Pandharipande, 2012) (van De Meugheuvel et al., 2014) (Fig. 2.21). Figure 2. 21 Typical closed-loop control system (Lawrence Berkeley National Laboratory, n.d) 80 2.3.2. Open-loop Daylight-linked Control Systems The photosensor is positioned and oriented only to detect daylighting in open-loop control, but almost no light from the controlled bulb is sensed (Fig. 2.22). Although the system cannot directly measure the total lighting in the controlled area, the controller then adopts a certain control algorithm to adjust luminaires, such as providing electric light in the predetermined proportion to the estimated daylight level to dim or switch luminaires. However, the absence of feedback from controlled space's light levels may limit the performance of such systems. For instance, if blinds are shut in certain workplaces, the contribution from luminaires may be insufficient if there is still adequate daylight outside, resulting in lower than needed light levels (Jain and Garg, 2018) (Pandharipande and Newsham, 2018). Since the open-loop photosensor only measures daylighting, it is usually mounted on the top of the building to monitor the sky and daylight availability, and it should be placed without obstructions that can cast shadows or otherwise affect performance (Pandharipande and Newsham, 2018). Figure 2. 22 Open-loop Daylight-linked Control Systems (source: http://intelliblinds.com/daylightharvesting.html ) 81 The open-loop daylight-linked control algorithms include switching and dimming algorithms (Bellia et al., 2016). In the switching controls, the output of the luminaire is either 0% or 100%, the photosensor receives the signal either increasing daylight levels or decreasing daylight levels. The zone between the on point and shutoff point is called the dead-band; in this zone, the controller takes no action on lamps (Fig. 2.19a). In the dimming controls, the output range of the luminaire is between 5% to 100%, and the dimming level is linearly proportional to the photosensor signal (Fig. 2.19c) (Bellia et al., 2016). This also improves occupant satisfaction since a constant turning light on and off would be distracting and annoying. An open-loop lighting control method was proposed for obtaining luminaire dimming levels for daylight adaption based on daylight sensing (Fig. 2.23). The performance of daylight sensing lighting control system was compared to that of a photodetector-based system, and the daylight-sensing system was shown to be more robust in the presence of environmental reflectance variations (Li et al., 2016). 82 Figure 2. 23 The daylight sensing-based lighting control system (Li et al., 2016) One research proposed a modified open-loop control system that incorporates a simulation model and daylight prediction sensors. This system can perform lighting control by using simulated indoor metrics (Jain and Garg, 2018) (Fig. 2.24). Another research applied the simulation model with real-time updating in response to changes in lighting conditions. The parameters of indoor illuminance are re-measured using daylight simulation, and control is applied to achieve the desired or code compliance illuminance level (Mahdavi et al., 2007). Figure 2. 24 An open-loop control integrated with real-time daylight modeling (Jain and Garg, 2018). 83 Another research used a Skyometer, a sky scanning instrument, to capture HDR sky luminance maps and record external global horizontal illumination values to improve the accuracy of virtual work plane illuminance sensors. The Skyometer was triggered at 3-minute intervals by a scripted, time-sequenced computer procedure (Humann and Mcneil, 2017) (Fig. 2.25). The researchers indicated that having two or more vertically oriented cameras facing opposite or cardinal directions atop a building would give the most accurate way of obtaining sky luminance maps for input into a virtual sensor automation system (Humann and Mcneil, 2017). This paves the road for the future development of open-loop lighting control systems. Figure 2. 25 Skyometer in horizontal orientation (top left). Horizontal, HDR sky luminance map (top right) Skyometer in the vertical orientation (bottom left). Vertical, HDR sky luminance map (bottom right) (Humann and Mcneil, 2017). 84 2.3.3. Closed-loop and Open-loop Daylight-linked Control Comparison Open-loop systems provide more calibration flexibility than closed-loop systems. (Doulos et al., 2005). In contrast to open-loop systems, sensor placement in a closed-loop system must be precise and calibrated. A calibration factor for the difference between photosensor signals (placed on the ceiling) and work plane illuminance must be calculated for both day and night in a closed-loop system (Park et al., 2011) (Caicedo et al., 2014) (Peruffo et al., 2015). This process may result in control decision inaccuracies. The complicated and costly calibration procedure inherent in daylighting systems is only one of many obstacles that must be overcome for daylighting methods to be feasible (Lee et al., 2017). Additionally, sensors in closed-loop systems are expensive to install at each workplace, difficult to commission, and challenging to calibrate, making them highly prone to errors (Jain and Garg, 2018). According to research performed by Berkeley Lab (LBNL) on the New York Times Building, primary obstacles to adoption include a shortage of products on the market that match current requirements, ambiguity about their performance, and a lack of operational knowledge (Fernandes et al., 2014). Year-long research was performed to monitor three different daylight control strategies in a school facility. The findings indicate that an open-loop control system saved more energy than a closed-loop system in this study (Delvaeye et al., 2016). However, the result from this study does not mean it is true for all instances. The difference between two control systems may measure energy saved in that situation but does not measure that against occupant satisfaction or productivity. 85 2.4. Daylighting Simulation The current state of the art in lighting simulation for building science is summarized schematically below (Fig. 2.26). The inputs include building geometry, materials optical properties, the description of luminaires, and sky models. Furthermore, these inputs are processed by algorithms or direct formulas. The output includes the illuminance, luminance, daylight factor, glare indexes, and energy consumption (Ochoa et al., 2012). Figure 2. 26 Current state-of-the-art in lighting simulation for building science (Ochoa et al., 2012) 2.4.1. Daylighting Simulation Elements Daylight simulations are based on a standard set of models that take into account the sky models, the building models, and daylight models (Ayoub, 2020). 86 The celestial hemisphere of the sky is represented using direct and diffuse sunlight received (Murdoch, 1985). However, external illuminance values are seldom acquired compared to routinely recorded global and diffuse irradiances (Alshaibani, 1996) or the monitored sky luminance distribution (Mardaljevic, 2000). Among measurements that weather stations collect, solar irradiances are widely accessible for many places worldwide (Crawley, 2007) as part of larger meteorological data sets. This is why most simulation tools rely on luminous efficacy models to translate these irradiances to values for sky luminance (Seo, 2018) (Ayoub, 2020). Weather datasets are available in standardized file formats such as EnergyPlus Weather (EPW) and DAYSIM Weather Format (WEA), with typical single-years representing historical weather conditions across prolonged periods (Moazami et al., 2019). Typically, EPW captures hourly data on a variety of meteorological variables, including temperature, humidity, precipitation, cloud cover, and sun irradiances, as well as wind velocity and direction (Levermore and Parkinson, 2006). Only two parameters are relevant for daylight modeling: direct normal and diffuse horizontal irradiances (Ayoub, 2020). Previously developed models predicted the spatiotemporal configurations of external illumination conditions. Geometrical representations of spaces are created utilizing a variety of 3D modeling techniques. Computer-Aided-Drafting (CAD) is essentially a static technique that creates models using a coordinate-based system. Ladybug and Honeybee are used to connect Grasshopper for energy and light simulation engines of buildings (EnergyPlus, Radiance, Daysim, and OpenStudio) (Kensek, 2019). The BIM (Building Information Modeling) software also has built-in simulation capabilities, and there is no need to export the model to another software application. For instance, since the program contains the location of the project, the 3D 87 geometry may be utilized for shadow casting. The designer can use this data to develop a preliminary plan for the building's design and shading devices and determine the optimal location of solar panels depending on solar radiation (Kensek, 2019). Additionally, preliminary daylighting experiments may be conducted. The geometry of the 3D model alone can be transferred to more complex simulation software capable of balancing daylighting, advanced interior lighting design, and lighting control systems to maximize energy savings (Kensek, 2019). Apart from these modeling techniques, there is no standard definition of the information required to support daylight models (Ayoub, 2020). However, to be considered well-represented by daylight models, they must contain any surrounding obstructions, external ground, façade openings, and inside furnishings (Ayoub, 2019a) (Ng, 2001) (Reinhart, 2011) (Reinhart and Walkenhorst, 2001). Additionally, they should incorporate any shading or light redirection devices (Ashikhmin and Shirley, 2000) (Lafortune et al., 1997) (Ward, 1992), as well as light scattering models, which can be collected analytically or by data-driven models (Guarnera et al., 2016). While the behavior of daylight in buildings is complicated, it is well studied (Tregenza and Mardaljevic, 2018); and many researchers and practitioners depend on climate-based daylight modeling (CBDM) using the Daylight Coefficient (DC) approach (Mardaljevic, 2000) (Reinhart and Herkel, 2000) (Tregenza and Waters, 1983). This includes using physically-based modeling tools to estimate illuminance values at sensor grids in built environments while considering the variations in the sky luminance distributions as determined by representative meteorological data for the research location. This method produces 145 diffuse and three 88 ground daylight coefficients in a single raytracing run, significantly reducing the amount of time necessary to compute a yearly daylight simulation. Exact calculations of interior illuminances under all 4703 annual hourly mean sky luminance distributions from the Freiburg test reference year (TRY) serve as a benchmark against which the other approaches are compared (Reinhart and Herkel, 2000). 2.4.2. Radiance and its Calculations Radiance is verified against the CIE 171:2006 analytical test cases, which serve as the industry standard for testing daylighting tools before their release (CIE, 2006) (Reinhart and Walkenhorst, 2001). Radiance converts the geometry in a scene to an "octree," the data structure used in the ray tracing process. The accurate results can be obtained in a reasonable amount of time by combining deterministic ray tracing with the Monte Carlo technique. “The Monte Carlo ray-trace (MCRT) method is based on the statistically predictable behavior of entities known as rays. Rays characterize the pathways taken by energy bundles as they are emitted, reflected, scattered, refracted, diffracted, and finally absorbed” (Mahan, 2019). As a result, the lighting simulation engine selects a measurement point and follows the light ray back to its source. This is referred to as backward raytracing. Calculations are based on three primary sources: the diffuse indirect component, the indirect specular component, and the direct component (Ward, 1994). The direct component consists of light produced directly from a source of light or light transferred perfectly specularly from other surfaces. Radiance calculates accurate penumbra 89 using Monte Carlo sampling and adaptive subdivision after identifying emitters based on their potential contribution to minimizing the number of rays required for computations. The indirect specular component includes light that is reflected or transmitted from other surfaces directionally. The diffuse indirect component is composed of light that hits a surface and is reflected or transmitted in a unidirectional manner. Although this component requires analyzing several directions to obtain accurate results, Radiance improves interpolations by utilizing the diffuse indirect computation in conjunction with the gradient information from each Monte Carlo evaluation (Chadwell, 1997). As a result of Radiance's hybrid calculating technique, simulation results will vary slightly between each run (Fig. 2.27). Figure 2. 27 The Simulation results after six consecutive runs (Kharvari, 2020). 90 2.4.3. Accuracies of Radiance-based Daylight Simulation Various factors, including differences in sky models (CIE, 2004) (Perez et al., 1993), and climate conditions (Bhandari et al., 2012) (Brembilla and Mardaljevic, 2019), surface reflectance coefficients (Brembilla et al., 2018), simulation parameters settings, and bias in simulation methods (Brembilla et al., 2019) (McNeil and Lee, 2013), influence predicted daylighting, the accuracy and reliability of daylighting simulation results in buildings (Fig. 2.28) (Kharvari, 2020). Simulation-related biases, these biases mainly from Root Mean Square Error (RMSE), the Normalized Mean Bias Error (NMBE), or the Coefficient of Variation of the Root Mean Square Error (CV RMSE) when operating the calculation (Taveres and Goia, 2020). It causes the prediction from simulation may generate a slightly higher or lower value. These biases can substantially impact the outcome and forecast of simulated daylighting and electric lighting energy consumption. 91 Figure 2. 28 Assessing relative bias in daylight simulation (Kharvari, 2020). 92 Ambient bounces mean the maximum number of diffuse bounces to calculate before discarding a ray path. A value of 0 indicates that no indirect computation occurred. Ambient accuracy means the value close to the mistake generated by indirect illuminance interpolation. It is a number between 1 and 0.1, with lower values providing the maximum degree of accuracy. The ambient resolution parameter specifies the maximum density of ambient data to be interpolated. A value of zero is regarded as the unlimited resolution. The number of Ambient divisions indicates that the inaccuracy in Monte Carlo calculations of indirect illuminance is inversely related to the square root. A value of 0 indicates that no indirect calculation occurred (Chien and Tseng, 2014) (Lawrence Berkeley National Laboratory, 2020) (Table. 2.4). Table 2. 4 The main parameters of -ab, -aa, -ar, -ad, and -as (Lawrence Berkeley National Laboratory, 2020) The reflection factors used to calculate reflections from the interior and exterior surfaces and simulation parameters for engine-based calculations such as ambient accuracy or ambient bounces significantly affect the simulation accuracy (Reinhart and Walkenhorst, 2001). Prior research (Monteoliva et al., 2020) set “-ab 5, -ad 2048, -as 512, -aa 0.08, and -ar 512” in its simulations, whereas another study set “-as 1024, -ar 64, -ad 4096, -aa 0.0, and -ab 10 (as well as -ab 30)” for maximum accuracy (Yao et al., 2020). 93 Without validation or calibration, simulations for research projects may differ considerably from reality (Judkoff et al., 2008). An empirical validation was conducted to assess Radiance version 5.1 parameters and simulation settings in Ladybug with Honeybee version 0.0.68–0.0.65 against field measurements under an overcast sky with a certain illuminance level. It demonstrates that the setting mentioned above is not as precise as with -ab 8 and that increasing -aa to 0.1 increased the system's power usage and computation time (Kharvari, 2020). In recent years, there have been several instances of this type (Mainini et al., 2019) (Manni et al., 2020). As a result, the research from Kharvari lays the foundation for future studies by providing a scientific framework for defining Radiance settings (Table 2.5). Table 2. 5 Recommended parameters for Radiance (Kharvari, 2020). Additionally, the measured illuminance levels were compared to simulation results with different Radiance parameter settings in the study (Kharvari, 2020) (Fig. 2.29). 94 Figure 2. 29 Measured illuminance and simulated illuminance levels (Kharvari, 2020). It is found that simulations with both Maximum and “Maximum -ab 10” variations resulted in almost precise results. So, using the Radiance engine in Ladybug and Honeybee package and the Maximum IIII setting for Radiance parameters, it is possible to get highly accurate results with an error of almost 3% (Kharvari, 2020) for the average illuminance. Moreover, the average inaccuracy for illuminance levels is between 14% and 15%. This error range is acceptable for analysis purposes (Kharvari, 2020). This study provided the foundation for the methodology of chapter 3 to use the ladybug and Honeybee to simulate the hourly illuminance. 95 2.5. Summary This chapter introduced lighting system design, including Light-Emitting Diode (LED), photosensor choice, and placement. Then, the literature review of daylight-linked lighting controls was summarized; studies include daylight-linked lighting switching controls, daylight- linked dimming controls, and the results of the energy savings from those daylight-linked controls. Furthermore, the research of daylight-linked control algorithms was summarized, including closed-loop daylight-linked controls and open-loop daylight-linked controls system and comparisons of these control algorithms. Finally, the studies of daylighting simulations were reviewed, including daylighting simulation elements, radiance and calculations, and accuracies of radiance-based daylight simulation (Fig. 2.30). Figure 2. 30 The Structure of Chapter 2 96 Previous research has proved that daylight-linked dimming and switched on/off lighting control has great potential to reduce energy consumption in buildings and apply to demand response. Daylight-linked lighting control systems contain luminaires and photosensors. Both fluorescent and light-emitting diodes (LED) can be integrated with indoor photosensors to operate closed-loop controls and an outdoor photosensor for open-loop controls. Although the research found that an open-loop control system saves more energy than a closed-loop system (Delvaeye et al., 2016), the sensors in closed-loop systems are expensive to install at each workplace, difficult to commission, and challenging to calibrate, making them highly prone to errors (Jain and Garg, 2018). Numerous studies have been conducted to evaluate daylight-linked lighting switches and dimming control systems. The energy savings potential may vary in scope and technique according to the type or room. The researcher has concluded that dimming electric lighting has no detrimental effect on lamp life compared to switching controls (Tetri, 2002). The research found that daylight control simulations can estimate the energy savings of buildings by integrating with daylight simulations (Bourgeois et al., 2006). Much research evaluates the factors that impact the accuracy and reliability of daylight simulation, including differences in sky models and climate conditions (Kharvari, 2020), surface reflectance coefficients (Brembilla et al., 2018), radiance parameters settings, and Simulation-related biases (Brembilla et al., 2019) (McNeil and Lee, 2013). The previous research provides solid foundations for the daylight simulation, the principle of the light sensors, and the logic of the daylight-linked switch on/off and dimming control. The next chapter will introduce the development of a simulated-based daylight-linked switch on/off and dimming control algorithm by post-processing the daylighting and lighting simulation results data and propose the possible solution for open-loop from the output lighting schedules of these pre-calculated control algorithms. 97 Chapter 3 3. Methodology The research objective is to propose lighting control algorithms based on the daylighting and lighting illuminance data from the simulation to improve building energy performance and visualize the comparison results of lighting energy consumption in a dashboard to facilitate decision-making for the proposed lighting control. This chapter introduces the overall workflow of developing the lighting switch on/off and dimming control algorithms, which includes geometry preparation and division of the existing building model, daylighting and lighting simulation parameters preparation, the method to process the acquired simulation data, the mathematic method to quantify the relationships between daylighting and lighting illuminance data, and how to use linear programming to find the optimal solutions. The methodology includes preparing the existing building model for daylighting and lighting simulation, the data acquisition and processing, lighting control algorithm development, and data visualization dashboards for the energy usage comparison, each luminaire’s hourly status, and illuminance distribution false-color maps after applying the proposed lighting control algorithms. Chapter 4 explains how the overall workflow is implemented in the case studies, the mathematics model to analyze and quantify the relationship between these data for the development of the lighting control algorithms, and the energy consumption and savings from proposed lighting controls in the dashboard. 98 3.1. Methodology Overview In order to conduct the daylighting and lighting simulation, the first step is to acquire the model of the building and ensure its software environment is interoperable with the calculation engine of the daylighting and lighting simulation. Rhino software by McNeal Associates is chosen for building the model, and the Grasshopper with Ladybug and Honeybee plugins are selected for the daylighting and lighting simulation. Then, the results of the lighting simulation data are processed as a matrix and exported as CSV files by using the C# script, and the results of the lighting simulation data are processed as the matrix in Grasshopper. The relationship between daylighting simulation data and lighting simulation data are quantified in Python script within Grasshopper or Microsoft Visual Studio using linear programming. The optimal solution from linear programming of lighting switch and dimming pattern and energy consumptions and savings are calculated and exported to Microsoft Excel. Finally, the result is imported to Power BI to create the data visualization dashboard. The methodology has four main steps: Existing Building Model, Daylighting and Lighting Data Acquisition, Lighting Control Algorithm, and Data Visualization Dashboard (Fig. 3.1) (Fig. 3.2). Figure 3. 1 Overall outline of the workflow diagram of Methodology and software and tools usage 99 Figure 3. 2 Overall sub-outline and details of the workflow diagram of Methodology 3.1.1. Existing Building Model (see section 3.2 for further details) Rhino is chosen for geometry creation and division. A completed daylighting model includes geometry information (roofs, skylights, walls, glazing, floors, shadings, and building orientation), environmental information (location, weather, and sky condition), the Radiance parameters of building elements. In addition, the site context that affects the daylighting availability contains surrounding buildings, trees, the outside ground. The daylighting simulation preparation for the Radiance parameters of building elements includes the optical properties. For the glass material used in building elements such as skylight, facade, transmittance is required, and reflectance is needed for the opaque material used in building elements such as walls, floors, ceilings, shadings, and the ground in the site context. In order to conduct the daylighting simulation in Grasshopper with Ladybug and Honeybee, the geometry of the building model is 100 divided and set as Honeybee surface;the analysis surface of grid, the sensor points, and the analysis time of date are also set up. Furthermore, the lighting fixture plan is acquired for the lighting simulation. 3.1.2. Daylighting and Lighting Data Acquisition (see section 3.3 for further details) In order to acquire the prediction of the daylighting availability data and lighting illuminance data from the simulation using the Ladybug and Honeybee plugins, the first step is to create a workflow that can conduct the needed daylighting metric in Grasshopper. The Ladybug and Honeybee legacy package is identified to achieve the hourly illuminance metric. Several components are required to acquire the hourly illuminance value of daylighting. First, the analysis period (hours of the month's date) is identified. Second, the type of sky conditions is selected to run the simulation. Third, the Radiance parameters are defined to run the daylighting simulation. In order to acquire the illuminance value of lighting fixtures, the dark sky condition is first required to eliminate the effect of daylight. Second, the lighting points are modeled in Rhino based on the existing lighting fixture plan. Third, the IES files of lighting fixtures are inputted for the lighting simulation. Finally, after the simulations are completed, the data of illuminance values are processed and inputted for the following development of lighting control algorithms. 3.1.3. Lighting Control Algorithm (see section 3.4 for further details) The proposed lighting controls are open-loop lighting switching on/off and dimming control. Linear programming is implemented as the foundation of the control algorithm. Two datasets are tested for the control algorithm: the first is the illuminance values from the lighting 101 simulations. The structure of this dataset is processed as a matrix in Grasshopper with C# script and exported as the CSV file. The second one is the hourly illuminance values from daylight simulation. It is processed as one array in Grasshopper. The customized Python script is developed in Grasshopper to calculate these two datasets dynamically by calling the NumPy and Pulp Python programming packages. 3.1.4. Data Visualization Dashboard (see section 3.5 for further details) After the control algorithms are applied, the energy consumption is calculated. The calculation is conducted in Grasshopper with Python script. The results are exported to Microsoft Excel and then fed the data to Microsoft Power BI to visualize the energy savings after implementing the proposed lighting control. This energy usage dashboard creation includes three main steps. First, in order to use the Excel file as a database to feed into Power BI, processing and organizing the data structure are essential. Second, the graph is selected, the user-select input is created, and the template is set up. Third, the interactive data visualization is created to visualize the electricity usage, lighting control validation, cost, and savings after implementing the proposed lighting control algorithms. 3.2. Existing Building Model Rhino can create a highly accurate 3D model free-from roofs, parametric facades, and versatile building shapes (Rhinoceros). An accurate geometry and its detailed division are essential for accurate daylighting simulation results (Fig. 3.3) (Fig. 3.4). Rhino 7.11 is used for the case study models creation. The first one is modeled based on the Revit sample building 102 model. The second model is the second-floor office building in Los Angeles (the details will be further described in Chapter 4). Figure 3. 3 Overall outlines of the workflow diagram of Methodology in Existing Building Model and its software and tools usage Figure 3. 4 Overall sub-outline and details of the workflow diagram of Methodology in Geometry Division and Simulation Preparation 103 3.2.1. Geometry Division In order to conduct the daylighting simulation in Grasshopper with Ladybug and Honeybee plugins, the building geometry is divided and defined as each different element: walls, floors, glazings, skylights, and ceilings (Fig. 3.5). In this example, different layers with colors of the geometry in Rhino represent different building elements. The dark blue represents the glazing, the green represents the skylights, and the gray present the ceiling, walls, and outside shading. Figure 3. 5 Northeast art of the airport terminal of Beijing Daxing International Airport geometry Division in Rhino (Each color represent different building elements) Each element is assigned as “Brep” in Grasshopper, and these elements are stored in Grasshopper by using the “internalise data” command. Using the “Honeybee_createHBsrfs” component, each geometry of the building elements is defined (Fig. 3.6). 104 Figure 3. 6 Assign the geometry in Rhino as walls element by using the “Brep” component and use “internalize data” to store the geometry in Grasshopper The accuracy of the building geometry and its detailed division is essential for the daylighting simulation to output the precise simulated results. Also, the optical properties of the building elements, simulation parameters, and weather data are important in daylighting simulation. 3.2.2. Simulation Preparation Each building element has different optical properties. Typically, the opaque elements of the building include walls, floors, ceilings, and shadings, while the transparent elements include glazing and skylights. The reflectance and transmittance of these materials need to be identified and input to the “Honeybee_createHBsrfs” component. After geometry is divided with the “Honeybee_createHBsrfs” component, the optical property of the opaque element and glazing elements are assigned by using the “Honeybee_Radiance Opaque Material” component and 105 “Honeybee_Radiance Glass Material” component, respectively (Fig. 3.7) (Fig. 3.8). Alternatively, the glazing elements can be added directly to the walls by using the “Honeybee_addHBGlz” component (Fig. 3.9). Figure 3. 7 Assign the walls reflectance value using the “Honeybee_Radiance Opaque Material” component Figure 3. 8 Assign the glazing transmittance value using the “Honeybee_Radiance Glass Material” component 106 Figure 3. 9 Adding glazing to the walls by using the “Honeybee_addHBGlz” component Climate or weather files are text files that carry information on the weather conditions at a specific location during a specified time period. They contain data on the sky and sun irradiances, temperatures, and wind speed, etc. Weather files can also include real-time data at a specific location for a certain time period. Energy Plus Weather Format (EPW) comprises weather data that has been statistically examined and organized by location as test reference years (TRY). TMY is representative of typical meteorological conditions and is so acceptable for daylighting calculations. These files typically include data for a one-year period and have a time step of one hour. The weather file is obtained from EnergyPlus website or Ladybug EPW file website (Fig. 3.10) (Fig. 3.11). 107 Figure 3. 10 Get the weather file of Beijing from EnergyPlus (source: https://energyplus.net/weather) Figure 3. 11 Inputting the EPW weather file of Los Angeles to Ladybug in Grasshopper for the daylighting simulation (https://www.ladybug.tools/epwmap/) The weather file selected for the daylighting simulation is the EPW weather file. After downloading the weather file or copying the URL, the data needs to be input to the Ladybug in Grasshopper. The climate-based sky is selected for the sky condition; it can be generated from 108 the EPW weather file by applying the “Honeybee_Generate Climate Based Sky” component (Fig. 3.12). Figure 3. 12 Generate climate-based sky from the EPW weather file by applying the “Honeybee_Generate Climate Based Sky” component. The orientation of the building is defined using two methods. First, the orientation of the building is correctly modeled in Rhino. Second, if the orientation of the building is incorrectly modeled, it can be modified by inputting a vector to set the true North direction for the sun path within the “Honeybee_Generate Climate Based Sky” component (Fig. 3.13). Figure 3. 13 The second method: Set the orientation of the building geometry by using the “North” input. After the optical properties have been assigned and the weather data of the selected location has been input, the next step is to create the sensor point and mesh, assign the sensor points within the grids and set these points offsets by using the “LB Generate Point Grid” component (Fig. 3.14). 109 Figure 3. 14 Create the sensor grids, assign the sensor points within the grids, and set these points offset by using the “LB Generate Point Grid” component The geometry is required to input as the place to put the simulated sensor; it generates a mesh with corresponding test points from a Rhino Brep (or Mesh). The “grid_size” is the number for the size of the test grid. The input offset distance represents the vertical distance to move the sensor points from the surface of the input geometry. This should be a positive number to ensure the mesh does not block the points. The simulated sensor points are at the center of each divided mesh face, and the vector of these points should be ensured to be the positive value. The geometry and sensor points are shown in Rhino (Fig. 3.15). 110 Figure 3. 15 The geometry and sensor points in Rhino (Perspective view and Top view) 111 3.3. Daylighting & Lighting Data Acquisition After the geometry of the building has been divided and the simulation preparation has been set up, the next stage is to run the daylighting simulation and collect and process the collected simulated results and data (Fig 3.16) (Fig. 3.17). Lux is used for the illuminance unit. Figure 3. 16 Overall outlines of the workflow diagram of Methodology in Daylighting & Lighting Data Acquisition and its software and tools usage Figure 3. 17 Overall sub-outline and details of the workflow diagram of Methodology in Simulation Parameters Setup and Data Storage & Formatting 112 3.3.1. Daylighting Simulation In order to successfully run the daylighting simulation, several required parameters are set before running the simulation, including the sky conditions, Radiance parameters, and the analysis time set. Additionally, it is crucial to ensure which component in Honeybee can conduct the hourly illuminance simulation. First, the sky condition is selected, and the analysis time is set up. The example represents setting up March 21 st at 7:00 am as analysis time using the “Honeybee_Generate Climate Based Sky” component (Fig. 3.18). Figure 3. 18 Set up analysis time of the date by using the “Honeybee_Generate Climate Based Sky” component. Example shows the analysis time is March 21st at 7:00 am. The next step is to set the Radiance parameters. The definition and consideration of each radiance parameter are shown below (Table 3.1). In Honeybee plugins, the “Honeybee_RADParameters” component can be applied to set up the radiance parameters for both daylight and lighting simulations (Fig. 3.19). 113 Table 3. 1 Definition and consideration of the main radiance parameters (Lawrence Berkeley National Laboratory, 2020) Figure 3. 19 Setting up Radiance Parameters by using the “Honeybee_RADParameters” component The third step for the preparation of the daylighting simulation is to connect all the above components to create the grid base simulation for the analysis by using the “Honeybee Grid Base Simulation” component (Fig. 3.20). 114 Figure 3. 20 The preparation of the daylighting simulation setup For the analysis time set, the 21 st of each month is selected for the simulation test, and the analysis hours range from sunrise to sunset. Customized Python scripts are developed; they can automatically output the hourly analysis date and month from sunrise to sunset by integrating with the radiation value from the output of the “Honeybee_Generate Climate Based Sky” component (Fig. 3.21). 115 Figure 3. 21 The customized Python scripts are developed to automatically output the hourly analysis date and month from sunrise to sunset Similarly, this method is then applied to calculate the whole year hours from sunrise to sunset (Fig. 3.22). For the case study models, three more sky conditions are used to run the control algorithms: “CIE sunny with sun,” “intermediate with sun,” and cloudy sky.” A whole year analysis is run for the case studies (See details in Chapter 5). Figure 3. 22 The customized Python scripts are developed automatically to calculate the whole year hours from sunrise to sunset. 116 Fourthly, the “Honeybee_Run Daylight Simulation” component is identified as one method to run the hourly illuminance daylighting simulation in Grasshopper (Fig. 3.23). Figure 3. 23 Using the “Honeybee_Run Daylight Simulation” component to run the hourly illuminance daylighting simulation in Grasshopper Figure 3. 24 Preview the result data by using the “panel” component After the simulation run is completed, the “Panel” component in Grasshopper is used as one method to preview the structure of the illuminance data for the later calculation with lighting 117 simulation illuminance data (Fig. 3.24). For the data storage and processing, the details will be discussed further in Section 3.4. Figure 3. 25 On the left is the “Ladybug_Recolor Mesh” component, in the middle is the “Ladybug_Legend Parameters” component, and on the right is the “Text Tag 3D” component. Finally, the “Ladybug_Recolor Mesh” component can visualize the hourly illuminance values (Lux) by providing a false-color map, and the input mesh should be the same mesh that generated the sensor points. The “Ladybug_Legend Parameters” component can divide the zone based on the level of Lux, and the “Text Tag 3D” component can show the illuminance values (lux) on the false-color map (Fig. 3.25). The location input in the tag component should be the tested sensors points. 3.3.2. Lighting Simulation The principle of lighting simulation with Honeybee plug-ins to Grasshopper is similar to daylight simulation: in the RADIANCE engine, each lighting fixture is treated as a light source. The elements that need to run the lighting simulation include lighting points and vectors, IES files, dark sky condition, and Radiance parameters. The sensor points are the same inputs as daylight simulation in the analysis mesh and its grid. 118 This first step is to create the points in Rhino that represent lighting fixtures. The lighting fixtures plan can be acquired from the existing CAD file of the specific test building or ceiling plans that contains the lighting fixtures (Fig. 3.26) (Fig. 3.27). In Rhino, each point is drawn as three-dimensional geometry, containing x, y, z coordinates space information. The lighting fixtures plan can ensure the x and y coordinates and the height of lighting fixtures can be acquired from lighting fixture schedules. So, z is set as the height of each lighting fixture. Figure 3. 26 The CAD file of the lighting fixture plan imported in Rhino Figure 3. 27 The points in the lighting fixture plan, each point represent two-dimension coordinates of each lighting fixture 119 After the lighting points are created (Fig. 3.28), these points are set in Grasshopper by using the “Point” component, internalizing data, and connecting to the "Honeybee_IES Luminaire Zone" Component for the following lighting simulations (Fig. 3.29). Figure 3. 28 Lighting fixtures are drawn as points in Rhino and saved in layers Figure 3. 29 Set the lighting fixtures points in Grasshopper, internalize data and connect to "Honeybee_IES Luminaire Zone" Component 120 The next step is to acquire the IES file. It is a standardized data file that describes a luminaire's light output as luminous intensity versus angle, as well as sufficient descriptive and documentary test information. The term is taken from the abbreviation for the Illuminating Engineering Society of North America (IESNA). The file extension ".ies" is used for IES files, which is also referred to as a luminaire's "photometric data" (IESNA). It is read with Honeybee in Grasshopper to simulate the illumination performance of lighting systems by using the "Honeybee IES Luminaire" component for the following lighting simulation (Fig. 3.30). Figure 3. 30 Read IES file by using using the "Honeybee IES Luminaire" component The IES format is previewed from the output of the luminaire details (Fig. 3.31). The detailed information includes the number of lamps and input watts. This data is exported and used for energy consumption and savings with the output with the control algorithms (it will be further discussed in section 3.5). Lighting engineers often refer to a polar curve graph as a polar luminous intensity graph. The shape is constructed by using the polar coordinate system. The coordinates radiate out in wheel-like spokes at a preset angle from a central point. The LED light bulbs are located (Andy et al., n.d.) (Fig. 3.32). 121 Figure 3. 31 The luminaire details show the example of one 71 W LED IES file format Figure 3. 32 A Example of the Polar Curve of a 71W LED from DiaLux software The principle of lighting simulation with Honeybee plug-ins for Grasshopper is similar to daylight simulation; it needs the sky condition and grid analysis recipe as input to run the simulation. For the lighting simulation, the sky condition is set as dark sky condition. The Radiance parameters are the same setting as daylight simulation (Fig. 3.33). 122 Figure 3. 33 Using the dark sky condition for the lighting simulation Then, the “Honeybee_Run Daylight Simulation” component is identified as one method to run lighting simulation. Finally, all the required components are connected to the “Honeybee_Run Daylight Simulation” component to run the lighting simulation. The illuminance values (Lux) are represented in the “Panel” component (Fig. 3.34). Each hourly illuminance is inputted as an array to control algorithms (Further discussed in section 3.4). Figure 3. 34 Overall workflow to run the lighting simulation in Grasshopper by using the Honeybee plugins 123 It is important to test whether the test analysis surface satisfies the illuminance requirement value (Lux) for the specific space type when turning on all the lights (the lighting code can refer to IESNA Lighting Handbook). Typically, it is impossible to ensure that all the sensor points reach the illuminance requirement value since some are assigned at the corner of the analysis surface, blocked by columns or other interior geometry (if these elements are considered and inputted for daylighting simulation). It is assumed that if 95% above sensor points reach the required illuminance level, it complies with the lighting illuminance requirement. Furthermore, the “Cull Pattern” component in Grasshopper is applied to remove the illuminance that does not fulfill the requirement (Fig. 3.35). The reason for removing this data is that the solution in the linear programming control algorithm would be infeasible when it runs (The algorithm will output “infeasible solutions”) (it will be further explained in Section 3.4). Figure 3. 35 Test whether the test analysis grid satisfies the illuminance and remove the data which does not fulfill the requirement using the "Cull Pattern" component in Grasshopper 124 3.3.3. Data Processing In order to test whether the proposed control algorithms can run, the datasets are prepared, and the illuminance values from daylight simulation and lighting simulations are processed. The test criteria are that for the switch on/off and dimming control, each luminaire's hourly on/off status and dimming level can be calculated after the proposed algorithms run, while the illuminance provided from luminaires and daylighting satisfy the threshold illuminance level. The hourly daylight illuminance data is processed dynamically as a single array when running each time step of one hour (the details will describe in Section 3.4), and lighting illuminance and the lighting illuminance data are processed as a matrix and exported to the CSV file using the C# script in Grasshopper. The lighting simulation is run with each lighting fixture one by one sequentially to acquire illuminance value on sensor points. Each set of data is stored in Grasshopper using the “Data Recorder” component (Fig. 3.36), and each light illuminance data is processed as each column of the matrix and exported as a CSV file by using the C# script in Grasshopper (Fig. 3.37). Figure 3. 36 Running lighting simulation with each lighting fixture one by one sequentially to acquire illuminance value on sensor points. Each set of data is stored in Grasshopper using the “Data Recorder” component 125 Figure 3. 37 Each light illuminance data is processed as each column of the Matrix and exported as a CSV file by using the C# script in Grasshopper 3.4. Lighting Control Algorithm The proposed daylight-linked lighting control included switch on/off control and dimming control. In order to develop the lighting control algorithms, an analysis method is identified: the mathematical method is applied by using linear programming to find the optimal solutions. The Python script is developed and tested in Grasshopper, the Pulp, SciPy, and NumPy Python packages are called to Python script in Grasshopper using GHPython Remote plugins. The datasets of the illuminance values from daylight simulation and lighting simulations are the input of the control algorithms. Another input is the dynamic hourly daylight illuminance value. When the daylighting simulation run, the hourly illuminance data is processed as a single array to be fed into the control algorithm and operation with linear programming in Python script (Fig. 126 3.38) (Fig. 3.39). After the script run, the whole year daylight illuminance data and lighting illuminance data are run in Microsoft Visual Studio. Figure 3. 38 Overall outlines of the workflow diagram of Methodology in Lighting Control Algorithm and its software and tools usage Figure 3. 39 Overall sub-outline and details of the workflow diagram of Methodology in Input and Output 127 3.4.1. Programming Environment The proposed control algorithm is developed with the “GHPython script” in Grasshopper. The control algorithms used NumPy and Pulp functions from Python packages to operate the calculation. However, the Python script in Grasshopper cannot call comprehensive functions from Python packages. The “GHPython Remote” plugin is identified as capable of connecting the “GHPython script” component to an external instance of Python that runs the usual programs (Fig. 3.40). Figure 3. 40 Run "GHPython Remote" plugin to call the external functions from Python 2.7 The “GHPython Remote” plugin is developed with Anaconda, and Python 2.7 is required to be installed locally since the Rhino/Grasshopper uses IronPython. Then, by using the virtual environment from Anaconda Prompt, the Rhinoremote is activated (Fig. 3.41). Figure 3. 41 Using Anaconda Prompt to activate Rhinoremote 128 The example describes how to use Anaconda Prompt to install numpy and scipy packages (Fig. 3.42). The other Python 2.7 packages and configuration of IronPython installation steps follow the same method. After the programming environment is set up (Fig. 3.43), the code written in Script Editor is able to call both RhinoCommon functions and specific functions from Python from the same script (Fig. 3.44) (Fig. 3.45). Figure 3. 42 Using Anaconda Prompt to install numpy and scipy packages Figure 3. 43 The location of the programming environment for the “GHPython Remote” component. 129 Figure 3. 44 Details workflow of running "GHPython Remote" and the results Figure 3. 45 The method to import and call the functions in the "GHPython script" component It is important to note that the Python packages that will be used are located in the "C:\Users\.....\Anaconda2\envs\rhinoremote\Lib\site-packages\" path (Fig. 3.46). 130 Figure 3. 46 The example path of Python Packages will be called for use in "GHPython Remote." A simple box model is used as a test of the proposed control algorithm using Grasshopper with the “Python Script” component (See further details in section 3.4.2). The version of Python in Grasshopper is Python 2.7 by default. After the algorithm is run successfully, the control algorithm is in Microsoft Visual Studio coding environment by Python 3.9 for running the whole year test (8760 hours) with case study models. 131 3.4.2. Switch On/Off control Algorithm The overall flowchart of the switch on/off control algorithm is shown below (Fig. 3.47). Figure 3. 47 The overall flowchart of the switch on/off control algorithm. The switch on/off control algorithm uses linear programming with matrix operation. The input is the hourly daylight illuminance value on each sensor point (Fig. 3.48), which transforms as one array and each array has the same item number with sensor points. For this example, the daylight illuminance array is: 𝑆 𝑡 1 = [137.69, 291.49, . . . , 287.38] 𝑇 132 Figure 3. 48 Example of a simple box room with illuminance value (lux) on 15 sensor points at 0.75m height at a certain time t1 Another input is from lighting fixtures' illuminance value; each light has a different illuminance distribution on the sensor points under dark sky conditions (no daylight). The number of the array is the same as the number of lights. And the number of items in each array is the same as sensor points. For this example, there should have 4 arrays for each light and 15 items in 4 arrays (Fig. 3.49) (Fig. 3.50) (Fig. 3.51) (Fig. 3.52). 133 Figure 3. 49 The illuminance distribution on 15 sensor points when ONLY light No.1 turns on under dark sky conditions (no daylight). Figure 3. 50 The illuminance distribution on 15 sensor points when ONLY light No.2 turns on under dark sky conditions (no daylight). 134 Figure 3. 51 The illuminance distribution on 15 sensor points when ONLY light No.3 turns on under dark sky conditions (no daylight). Figure 3. 52 The illuminance distribution on 15 sensor points when ONLY light No.4 turns on under dark sky conditions (no daylight). So, the arrays of these 4 lights is 𝐿 1 = [235.04, 229.39, . . . , 89.97 ] 𝑇 𝐿 2 = [323.57, 309.0, . . . , 86.54] 𝑇 …… 135 𝐿 4 = [87.33, 119.48, . . . , 330.33] 𝑇 And these arrays can be represented as one 15 * 4 Matix, and the data is stored in CSV file. 𝐿 𝑡 0 = [ 235.04 229.39 323.57 309.0 … 89.77 … 86.54 … … 87.33 119.48 … … … 330.33 ] Set 𝑋 𝑡 1 to be 4 lights status array at a certain time 𝑡 1 , which is a 4*1 array: 𝑋 𝑡 𝑖 = [𝑋 𝑡 1 ,1 , 𝑋 𝑡 1 ,2 , … , 𝑋 𝑡 1 ,4 ] 𝑇 𝑋 𝑡 1 ,1 , 𝑋 𝑡 1 ,2 , … , 𝑋 𝑡 1 ,4 = 1 𝑜𝑟 0 A standard normal array 𝑒 = [1,1, … , 1] 𝑇 , a m*1 array, m is the number of lights. So, in this example, 𝑒 = [1,1,1, 1] 𝑇 . To find vector 𝑋 𝑡 1 , linear matrix programming is applied as follows: Find a vector 𝑋 𝑡 𝑖 that minimizes 𝑋 𝑡 1 𝑇 ∗ 𝑒 or ∑ 𝑋 𝑡 1 ,4 ( 𝑗 = 1, 2, 3, 4) 𝑚 1 Subject to 𝑆 𝑡 1 + 𝐿 𝑡 0 ∗ 𝑋 𝑡 1 ≥ 𝐸 0 (𝐸 0 is the lighting requirement value, in this case, is 500 lux ) And 𝑋 𝑡 1 ,1 , 𝑋 𝑡 2 ,2 , … , 𝑋 𝑡 1 ,4 = 1 𝑜𝑟 0 (1 represent light on, and 0 represent light off) After operating in Python, the output of the vector 𝑋 𝑡 1 = [0, 1, 1, 0]. In this example, at the certain time t1, with 15 sensor points at 0.75m height within a simple box test model. The switch on/off status is calculated as lights 1 and 4 are off, and lights 2 and 3 are on. The flowchart of this switch on/off control example is shown below (Fig. 4.53). 136 Figure 3. 53 The flowchart of the switch on/off control example case: with 4 lights, 15 sensor points The mathematics functions and operation process for the switch on/off control algorithm are described below. The illuminance value at each sensor point at a specific time represents as 𝑆 𝑡 𝑖 ,𝑛 . And 𝐸 0 represent illuminance requirement value. For a certain time 𝑡 𝑖 , there are n sensor points in the daylighting simulation with only receive illuminance from the sun (no electric lightings) (Fig. 3.54); it will have one n*1 array (3- 1) ： 𝑆 𝑡 𝑖 = [𝑆 𝑡 𝑖 ,1 , 𝑆 𝑡 𝑖 ,2 , … , 𝑆 𝑡 𝑖 ,𝑛 ] 𝑇 (3 − 1) Figure 3. 54 The process to input the daylighting illuminance data as each array to the Switch On/Off Algorithm for Linear Programming operation 137 Similarly, from the lighting simulation, assuming there are m lightings, each lighting contributes different illuminance values on n sensor points, and it has m n*1 array (3-2): 𝐿 1 = [𝐿 1,1 , 𝐿 1,2 , … , 𝐿 1,𝑛 ] 𝑇 𝐿 2 = [𝐿 2,1 , 𝐿 2,2 , … , 𝐿 2,𝑛 ] 𝑇 …… 𝐿 𝑚 = [𝐿 𝑚 ,1 , 𝐿 𝑚 ,2 , … , 𝐿 𝑚 ,𝑛 ] 𝑇 (3 − 2) So, all the lightings illuminance values on n sensor points under the dark sky condition 𝑡 0 can be represented as one n*m Matrix (3-3): 𝐿 𝑡 0 = [ 𝐿 1,1 𝐿 2,1 𝐿 1,2 𝐿 2,1 … 𝐿 𝑚 ,1 … 𝐿 𝑚 ,2 … … 𝐿 1,𝑛 𝐿 2,𝑛 … … … 𝐿 𝑚 ,𝑛 ] (3 − 3) The matrix is imported from the CSV file by using the Padas package as pd.read_csv function (Fig. 3.55) (Fig. 3.56). Figure 3. 55 The lighting illuminance data as a Matrix be imported from the CSV file by using the Padas package as pd.read_csv function in "GHPython script" Editor 138 Figure 3. 56 The example of the lighting illuminance data as a Matrix in the CSV file 𝑋 𝑡 𝑖 is set to be m lights status array at a certain time 𝑡 𝑖 , which is a m*1 array (3-4) (3-5): 𝑋 𝑡 𝑖 = [𝑋 𝑡 𝑖 ,1 , 𝑋 𝑡 𝑖 ,2 , … , 𝑋 𝑡 𝑖 ,𝑚 ] 𝑇 (3 − 4) 𝑋 𝑡 𝑖 ,1 , 𝑋 𝑡 𝑖 ,2 , … , 𝑋 𝑡 𝑖 ,𝑚 = 1 𝑜𝑟 0 (3 − 5) (Note: 1 represents the light on, and 0 represents light off at a certain time 𝑡 𝑖 ). The Pulp package from Python is called to apply the (Linear Programming) LP function. The method to get the 0 or 1 value is to assign binary to the value (Fig. 3.57). Figure 3. 57 Using the Pulp package from Python to call the LP function for operating the Linear Programming and assigning Binary to get the value 0 or 1 139 A standard normal array 𝑒 = [1,1, … , 1] 𝑇 , a m*1 array, m is the number of lights. And to find vector 𝑋 𝑡 𝑖 , then linear matrix programming applied as follows (3-5) (3-6): find a vector 𝑋 𝑡 𝑖 that minimizes 𝑋 𝑡 𝑖 𝑇 ∗ 𝑒 or ∑ 𝑋 𝑡 𝑖 ,𝑗 ( 𝑗 = 1, 2, … , 𝑚 ) 𝑚 1 (Fig. 3.58). Figure 3. 58 The method to create j = 1, 2, ....., m in Python script Subject to 𝑆 𝑡 𝑖 + 𝐿 𝑡 0 ∗ 𝑋 𝑡 𝑖 ≥ 𝐸 0 (3 − 6) And 𝑋 𝑡 𝑖 ,1 , 𝑋 𝑡 𝑖 ,2 , … , 𝑋 𝑡 𝑖 ,𝑚 = 1 𝑜𝑟 0 (3 − 5) The vector 𝑋 𝑡 𝑖 is the output. The minimized 𝑋 𝑡 𝑖 𝑇 ∗ 𝑒 ( ∑ 𝑋 𝑡 𝑖 ,𝑗 𝑚 1 ) represents the minimized number of lights on at 𝑡 𝑖 time. Subject to 𝑆 𝑡 𝑖 + 𝐿 𝑡 0 ∗ 𝑋 𝑡 𝑖 ≥ 𝐸 0 or 𝑆 𝑡 𝑖 + 𝐿 𝑡 0 ∗ 𝑋 𝑡 𝑖 − 𝐸 0 ≥ 0 represent the illuminance values on each sensor point all beyond the illuminance requirement value 𝐸 0 (Fig. 3.59). Figure 3. 59 The method to operate the function of inequality 3-6 And 𝑋 𝑡 𝑖 ,1 , 𝑋 𝑡 𝑖 ,2 , … , 𝑋 𝑡 𝑖 ,𝑚 = 1 𝑜𝑟 0 represents each light on/off status at a certain time 𝑡 𝑖 (Fig. 3.60). 140 Figure 3. 60 Transform the 0 or 1 (integer) to on/off string in Python 3.4.3. Dimming Control Algorithm The overall flowchart for the dimming control algorithm is shown below (Fig. 3.61). Figure 3. 61 The overall flowchart for the dimming control algorithm. The dimming control algorithm also uses linear programming with matrix operation. The same example with section 3.4.2 with the same input: one the hourly daylight illuminance value on 15 sensor points. And the same lighting illuminance value as one 15*4 matrix. The difference is the domain of definition; it defines the 𝑋 𝑡 1 is from 0 to 1, the output of dimming level of these 4 lights shows below: Find a vector 𝑋 𝑡 𝑖 that minimizes 𝑋 𝑡 1 𝑇 ∗ 𝑒 or ∑ 𝑋 𝑡 1 ,4 ( 𝑗 = 1, 2, 3, 4) 𝑚 1 Subject to 𝑆 𝑡 1 + 𝐿 𝑡 0 ∗ 𝑋 𝑡 1 ≥ 𝐸 0 (𝐸 0 is the lighting requirement value, in this case, is 500 lux ) And 0 ≤ 𝑋 𝑡 1 ,1 , 𝑋 𝑡 1 ,2 , … , 𝑋 𝑡 1 ,4 ≤ 1 (the number 0-1 represent lighting output for dimming control) 141 After operating in Python, the output of the vector 𝑋 𝑡 1 = [0.88, 0.07, 1, 0.63]. In this example, at the certain time t1, with 15 sensor points at 0.75m height within a simple box test model. The dimming status is calculated as light 1 dims 88%, light 2 dims 7%, light 3 dims 100%, and light 4 dims 63%. The flowchart of this dimming control example is shown below (Fig. 3.62). Figure 3. 62 The flowchart of the dimming control example case: with 4 lights, 15 sensor points The mathematics operation process for the switch dimming control algorithm is described below. For a certain time 𝑡 𝑖 , there are n sensor points in the daylighting simulation with only receive illuminance from the sun (no electric lightings); it will have one n*1 array (3-1) ： 𝑆 𝑡 𝑖 = [𝑆 𝑡 𝑖 ,1 , 𝑆 𝑡 𝑖 ,2 , … , 𝑆 𝑡 𝑖 ,𝑛 ] 𝑇 (3 − 1) Similarly, from the lighting simulation, assuming there are m lightings, each lighting contribute different illuminance values on n sensor points, it will have m n*1 array (3-2): 142 𝐿 1 = [𝐿 1,1 , 𝐿 1,2 , … , 𝐿 1,𝑛 ] 𝑇 𝐿 2 = [𝐿 2,1 , 𝐿 2,2 , … , 𝐿 2,𝑛 ] 𝑇 …… 𝐿 𝑚 = [𝐿 𝑚 ,1 , 𝐿 𝑚 ,2 , … , 𝐿 𝑚 ,𝑛 ] 𝑇 (3 − 2) So, all the lighting illuminance values on n sensor points under the dark sky condition 𝑡 0 can be represented as one n*m matrix (3-3): 𝐿 𝑡 0 = [ 𝐿 1,1 𝐿 2,1 𝐿 1,2 𝐿 2,1 … 𝐿 𝑚 ,1 … 𝐿 𝑚 ,2 … … 𝐿 1,𝑛 𝐿 2,𝑛 … … … 𝐿 𝑚 ,𝑛 ] (3 − 3) The matrix can be imported from the CSV file by using the Padas package as pd.read_csv function. Set 𝑋 𝑡 𝑖 to be m lights status array at a certain time 𝑡 1 , which is a m*1 array (3-4) (3-7): 𝑋 𝑡 𝑖 = [𝑋 𝑡 𝑖 ,1 , 𝑋 𝑡 𝑖 ,2 , … , 𝑋 𝑡 𝑖 ,𝑚 ] 𝑇 (3 − 4) 0 ≤ 𝑋 𝑡 𝑖 ,1 , 𝑋 𝑡 𝑖 ,2 , … , 𝑋 𝑡 𝑖 ,𝑚 ≤ 1 (3 − 7) (Note: the value between represents the lighting dimming level at a certain time 𝑡 𝑖 ). The Pulp package from Python is called to apply the (Linear Programming) LP function. The method to get the 0 or 1 value is to assign Binary to the value (Fig. 3.63). 143 Figure 3. 63 Using the Pulp package from Python to call the LP function for operating the Linear Programming and assigning Continuous to get the float value 0 to1 A standard normal array 𝑒 = [1,1, … , 1] 𝑇 , a m*1 array, m is the number of lights. And to find vector 𝑋 𝑡 𝑖 , linear matrix programming is applied as follows (3-6) (3-7): Find a vector 𝑋 𝑡 𝑖 that minimizes 𝑋 𝑡 𝑖 𝑇 ∗ 𝑒 or ∑ 𝑋 𝑡 𝑖 ,𝑗 ( 𝑗 = 1, 2, … , 𝑚 ) 𝑚 1 . Subject to 𝑆 𝑡 𝑖 + 𝐿 𝑡 0 ∗ 𝑋 𝑡 𝑖 ≥ 𝐸 0 (3 − 6) And 0 ≤ 𝑋 𝑡 𝑖 ,1 , 𝑋 𝑡 𝑖 ,2 , … , 𝑋 𝑡 𝑖 ,𝑚 ≤ 1 (3 − 7) The vector 𝑋 𝑡 𝑖 is the output. The minimized 𝑋 𝑡 𝑖 𝑇 ∗ 𝑒 ( ∑ 𝑋 𝑡 𝑖 ,𝑗 𝑚 1 ) represents the minimized number of lights on at 𝑡 𝑖 time subject to 𝑆 𝑡 𝑖 + 𝐿 𝑡 0 ∗ 𝑋 𝑡 𝑖 ≥ 𝐸 0 or 𝑆 𝑡 𝑖 + 𝐿 𝑡 0 ∗ 𝑋 𝑡 𝑖 − 𝐸 0 ≥ 0 represents the illuminance values on each sensor point all beyond the illuminance requirement value 𝐸 0 . And 0 ≤ 𝑋 𝑡 𝑖 ,1 , 𝑋 𝑡 𝑖 ,2 , … , 𝑋 𝑡 𝑖 ,𝑚 ≤ 1 represents the lighting dimming level at a certain time 𝑡 𝑖 (Fig. 3.64). 144 Figure 3. 64 Calculating the Dimming Level of each light, transforming the dimming level as the percentage and numbering each light 3.5. Data Visualization Dashboard The switch on/off control algorithm calculates the number of lights that should be turned on/off, and the dimming control algorithm can calculate the dimming level of each luminaire based on the hourly illuminance level in a certain space. In addition, each luminaire’s input power wattage is acquired from the IES file. The next stage is to calculate the energy consumption of the luminaire with the implementation of the proposed lighting control algorithms and compare them to the baseline lighting mode. The baseline lighting mode assumes that each luminaire kept turning on during the operation hours. This study assumes that the light output is linearly proportional to the dimmable luminaire's electricity usage. 145 Figure 3. 65 Overall outlines of the workflow diagram of Methodology in Energy Usage Comparison and its software and tools usage 146 Figure 3. 66 Overall sub-outline and details of the workflow diagram of Methodology in Lighting Energy Calculation and Data Visualization 147 3.5.1. Lighting Energy Comparison The lighting electricity usage of the switch on/off control is calculated by multiplying the number of turning on luminaires at a certain time by the input power wattage. Each luminaire's hourly on/off status has been calculated from the proposed switch on/off control algorithm (Fig. 3.67). In this example, x1 = 0.0 represents the on/off status of luminaire 1 at a certain time is turning off. Figure 3. 67 The on/off status of each luminaire at a certain time with the proposed switch on/off control algorithm. The information of input wattage of the specific luminaire can be acquired in its IES file (Fig. 3.68). In this example, the input wattage of Cubic Max Recessed Module – M9 luminaire is read using the “Honeybee_IES_Luminaires,” in this case, the input watt is 60 wattage. 148 Figure 3. 68 The information of luminaire can be acquired from the IES file by using the "Honeybee_IES_Luminaires" component to read the file The lighting electricity usage of the dimming control can be calculated by implementing the relationship between light output with relative consumed power (Fig. 3.69). This study assumed that the light output is linearly proportional to the power consumption. Figure 3. 69 Relative consumed power versus light output for test luminaires LED luminaires (Doulos et al., 2017). 149 Each luminaire's hourly dimming level has been calculated from the proposed dimming control algorithm (Fig. 3.70). In this example, x12 = 0.19 represents the output level of luminaire 12 at a certain time is 19%, which is 81% dimming in this case. So, the electricity usage for this luminaire during this hour is 0.19 * 60 watts * 1 hour = 11.4 watt-hour. The baseline lighting mode assumed that each luminaire is kept turned on during the operation hours (it will be further discussed in Chapter 4). All these result data is exported and stored in an Excel file as a data source. Figure 3. 70 The light output of each luminaire at a certain time with the proposed dimming control algorithm. After each luminaire’s hourly light status is calculated under the proposed switch on/off and dimming control, the hourly illuminance value that each luminaire provided is calculated. 150 Combining these with hourly daylighting illuminance values allows creating the hourly illuminance distribution false-color map (luminaires + daylighting) (Fig. 3.71). The added illuminance values are rounded to two decimal places. Figure 3. 71 Example of hourly illuminance distribution false-color map (luminaires + daylighting) and Light Status with proposed switch on/off and dimming control at January 1st, 14:00 using climate-based sky file. Each hourly illuminance distribution false-color map is generated and stored in the local computer, so it is difficult to store this type of image-based data to Excel once the size of image 151 data exceeds 32,767 characters since each cell has a character limit of 32,767 in Excel. As a result, Amazon Simple Storage Service (AWS S3) (Fig. 3.72) (Fig. 3.73) is used to store the image data, each image is created as a Uniform Resource Locator (URL), and the URL can be stored in Excel (Fig. 3.74). Figure 3. 72 The hourly illuminance distribution false-color map images are stored in AWS S3 Figure 3. 73 The URL of each illuminance false-color map image is generated. 152 Figure 3. 74 The URLs of each hourly illuminance distribution false-color maps (luminaires + daylighting) are stored in excel 3.5.2. Data Visualization The whole year has 8760 hours, so for the hourly lighting switch on/off and dimming controls, there are 17520 results of electricity usage, lighting status, and energy cost for each analysis luminaires (Fig. 3.75). It is important to organize and visualize these data in a controllable and understandable way. 153 Figure 3. 75 Excel sheet contains the whole year hourly light status and electricity results after implementing the proposed switch on/off and dimming control algorithm in a certain case study model. Microsoft Power BI is used to import this pre-calculated data (store in Excel file); the results data contains each luminaire on/off status and output status after the linear programming calculation process is operated with Python code. The results data also contains the hourly electricity usage of each luminaire, the timeline for the lighting schedules, analysis area for the lighting control zones, URL for each hourly illuminance distribution false-color map (luminaires + daylighting), and the electricity usage of baseline lighting mode. This data is stored in the Excel file as a data source and then imported to Microsoft Power BI (Fig. 3.76), and data is transformed by using the Power Query in Power BI to organize this data (Fig. 3.77). 154 Figure 3. 76 Import the Excel as a data source to Power BI. Figure 3. 77 Using Power Query in Power BI to organize the data for further interactive data visualization. 155 It is crucial to ensure the relationship between each row and column and the type of data to be set up in the data transforming process. First, the timeline needs to be duplicated as the quarter, month, day, hour, and time separately as columns (Fig. 3. 78) (Fig. 3.79). Figure 3. 78 Using Power Query insect column to add the month, day, hour as separate columns. Figure 3. 79 The results of each separate column after using Power Query 156 Other key performance indicators (KPI) are also calculated inside Power BI using the DAX function in Power BI, formula, or expression. DAX is a collection of functions, operators, and constants that can be used to compute and return one or more values through a formula; DAX enables new data can be generated from existing data in the data model (Minewiskan, n.d.). For example, two different control's electricity cost and savings can be calculated for the financial analysis (Fig. 3. 80). Figure 3. 80 Other KPIs such as cost and savings for different controls can be calculated inside Power BI Finally, the imported data is visualized on the interactive dashboard with the user-select inputs (Fig. 3.81) (Fig. 3.82). 157 Figure 3. 81 Example of lighting electricity comparison dashboard in Microsoft Power BI. Figure 3. 82 Example of illuminance distribution false-color map in Microsoft Power BI. 158 3.6. Grasshopper Flowchart This chapter described the overall Grasshopper flowchart in two ways. The first Grasshopper chart follows the same structure as the overall methodology (Fig. 3.83). Figure 3. 83 The overall Grasshopper Flowchart follows the structure of the overall methodology. The hourly-based lighting control algorithm is first tested in Grasshopper for three days from sunrise to sunset. Then, the whole year (8760 hours) test is run in Microsoft Visual Studio. To be specific, the phrase “Data Storage & Formatting, “Data Input,” part of “Data Output,” and Lighting Energy Calculation” in Grasshopper are coded and run in Visual Studio. The daylight and lighting simulations are in the phare “Simulation Parameter Setup,” so the daylight and lighting illuminance data are acquired in Grasshopper, and the data is output to excel. This will be covered in Chapter 4, and the overall Grasshopper flowchart is separated with legible images in Appendix A.1.1. 159 The second Grasshopper chart is color-coded by different plugins/packages usage (Fig. 3. 84). The overall GH script used Ladybug, Honeybee, TT Toolbox plugins, and customized components are written with Python and C# scripts (See in Appendix A.1.2) Figure 3. 84 Each group in Grasshopper is color-coded with different colors - Ladybug, Honeybee, TT Toolbox plugins, Python, and C# scripts. Each group in Grasshopper is color-coded with different color groups, and the legend shows on the left corner of Fig. 3. 84. The usage of existing plugins in Grasshopper is summarized in Table 3.2. 160 Table 3. 2 The usage of existing plugins in Grasshopper. The customized component applied the Python script to calculate sunrise to sunset hours and develop lighting control algorithms. The C# script exported the lighting illuminance data to CSV files. See Chapter 4 for detailed descriptions of the customized components and the see detailed code in Appendix A.2.2. 3.7. Summary This chapter introduced the overall methodology based on four main stages: preparing the existing building model for daylighting simulation, daylighting data acquisition, lighting control algorithm, and data visualization dashboard (Fig. 3. 85). The four main stages include eight modules: the geometry division of the existing building model, daylighting simulation preparation, the daylighting simulation set up for running, the method to process the acquired daylighting and lighting illuminance data, and the method to quantify the relationships with these data, the electric lighting energy consumption calculation, the data visualization, and lighting energy comparison after using the proposed lighting control algorithm. Plugins Title & Version Description for Specific usage Source Ladybug Tools 1.2.0 • Use Ladybug imports standard EnergyPlus Weather files (.EPW) • To generate sensor grid and sensor points https://www.food4rhino. com/en/app/ladybug- tools Ladybug 0.0.69 and Honeybee 0.0.66 [Legacy Plugins] • Use Honeybee connects to Radiance simulation engines for daylighting and lighting simulations • To create Radiance Model • To generate Climate-based sky and CIE skys https://www.food4rhino. com/en/app/ladybug- tools TT Toolbox 1.9 • Use Excel Writer with file creation, work sheets creation • Use Excel reader link to an Excel work sheets https://www.food4rhino. com/en/app/tt-toolbox 161 Figure 3. 85 Four main stages of overall methodology and the software and tools usage Rhino, a 3d modeling software program, is selected for the building geometry preparation and division. It contains a visual programming tool called Grasshopper. Ladybug and Honeybee plug-in for Grasshopper is selected to set up the optical properties of the materials of the building elements, set the simulation parameters, run the daylighting simulation, process the data from simulation results with the Ladybug and Honeybee plugins. Python scripts are developed in Grasshopper and Visual Studio to run the control algorithms to operate the linear matrix programming and calculate each luminaire’s on/off and dimming status while satisfying the defined illuminance level. 162 Chapter 4 4. Program Development This chapter explains the key components in Grasshopper and source code in Visual Studio to assist the development of lighting control algorithm, running the linear programming of the matrix, and processing the data for the visualization. First, the most significant customized components in Grasshopper and their functions are described (See Appendix A.2 for the detailed code). Second, the entire source code in Visual Studio is explained, followed by the workflow in methodology (See Appendix A.3 for the entire code). This chapter intends to explain the source code comprehensively. 4.1. Key Customized Components in Grasshopper This section describes the most important customized component in the overall methodology workflow. Sixteen key customized components were created in Grasshopper with Python and C# script to develop the daylight-linked switch on/off and dimming control algorithm, to run the program, and to test the developed control algorithms (Fig. 4.1) (See in Appendix A.1.3). These components were explained under each stage or subsection of the methodology workflow. 163 Figure 4. 1 The overall Grasshopper Flowchart follows the structure of the overall methodology, and sixteen customized components are highlighted. 4.1.1. Simulation Preparation The customized components 1-4 were created to calculate the analysis time by integrating with the “Honeybee_Generate Climate Based Sky” component using Python script. The analysis time (sunrise to sunset) in this study is defined as the analysis hours that the illuminance is positive value on the analysis surface during daylighting simulation. Component 1 generates the months, days, and hours from sunrise to sunset. These outputs are inputted into the “Honeybee_Generate Climate Based Sky” component. This component function is to gain the analysis hours from a range of successive months on one typical day. The inputs are start month, end month, and one typical day (Fig. 4.2). 164 Figure 4. 2 The customized Python scripts (GH components 1 and 4) were developed to automatically output the hourly analysis date and months from sunrise to sunset. The output from the “Honeybee_Generate Climate Based Sky” component and component 1 connects to component 4 as input. It is noted that the “Honeybee_Generate Climate Based Sky” component uses diffuse sky radiation to calculate the daylighting simulation. So the function of component 4 is by determining whether diffuse sky radiation is zero. If the diffuse sky radiation is zero, the illuminance on the analysis surface will be zero, and it is defined as the dark sky. The output of component 4 is sunrise to sunset hours and their corresponding months and days, which will be used for the daylighting simulation inputs. Component 2 can also generate the months, days, and hours from sunrise to sunset, but the function is different from component 1. This component generates the analysis hours from a range of non-successive months on one typical day by integrating the “Honeybee_Generate Climate Based Sky” component and component 4 (Fig. 4.3). 165 Figure 4. 3 The customized Python scripts (GH components 2 and 4) were developed to automatically output the hourly analysis date and ranges of months from sunrise to sunset. Component 3 generates the analysis hours from a range of non-successive months on one typical day by integrating the “Honeybee_Generate Climate Based Sky” component and component 4. There are no inputs for component 3 (Fig. 4.4). Figure 4. 4 The customized Python scripts (component 3) were developed to automatically calculate the whole year hours from sunrise to sunset. 166 4.1.2. Simulation Parameters Setup The outputs from component 4 are processed and connected to component 5 as inputs for the hourly daylighting simulation (Fig 4. 5). The function of component 5 is that when the slider is animation for the hourly daylighting analysis, the null value (time and date have no daylight in simulation) can pass and go through the daylighting simulation components. Figure 4. 5 The customized Python scripts (component 5) were developed automatically to process the time data for the daylighting simulation. Using component 5 can greatly reduce the simulation time for the whole year of hourly daylighting simulation. The pass function inside the component can run compatibly with the “Honeybee_Generate Standard CIE Sky” component and the “Honeybee_Generate Climate Based Sky” component. 167 4.1.3. Data Storage & Formatting Component 6 connects to output results of the “Honeybee_Run Daylight Simulation” component to filter the illuminance data. In order to streamline the collecting of the illuminance data for further exporting and operation for the lighting control algorithms, it is important to avoid the “Data Recorder” component receiving the null value. Instead, the component filter replaces the null value as None since the “Honeybee_Run Daylight Simulation” component generates the null value when receiving the pass function from previous components (Fig. 4.6). Figure 4. 6 The customized Python scripts (component 6) were developed automatically to filter the illuminance data. Component 7 is used for numbering and naming each analysis luminaires under different control algorithms as a string (Fig. 4.7). This data is later formatted as the header for exported Excel file. After running the lighting controls algorithm, each hourly lighting control result is exported as each row in Excel. So, inputting the total analysis hours is used to generate the same row of data (the total number of luminaires) to write into Excel. 168 Figure 4. 7 The customized Python scripts (component 7) were developed for numbering and naming each analysis luminaires under different control algorithms as a string. Component 11 is developed to integrate with the “Honeybee_IES Luminaire” component to extract the input watt and calculate lumen per watt of the selected luminaire from the IES file. So the output result can directly connect to component 10 as input to calculate the lighting electricity with the proposed lighting control algorithms (Fig. 4.8). 169 Figure 4. 8 The customized Python scripts (component 11) were developed to extract the input watt and calculate lumen per watt of the selected luminaire. Component 13 was written in C#. The inputs include a file path to write and save the CSV file, the dataset (all the structured illuminance data), and a boolean. Users can define the file path that saved these CSV files and choose boolean to “True” to export and write the organized lighting illuminance data into CSV files. These files will later be called to transform to a matrix and operate with linear matrix programming (Fig. 4.9) (See Appendix A.2.13 for the entire code). 170 Figure 4. 9 The customized C# scripts (component 13) were developed to write the organized lighting illuminance data as CSV files. 4.1.4. Lighting Control Algorithm The algorithms in components 8 and 9 are essential for the overall methodology. Component 8 is the proposed daylight-linked switch on/off control algorithm (Fig. 4.10) (See Chapter 3 Section 3.4.2 for the detailed explanation of the mathematical process for the linear matrix programming, and see Appendix A.2.8 and A.3 for the entire code in Grasshopper and Visual Studio). 171 Figure 4. 10 The customized Python scripts (component 8) - the proposed daylight-linked switch on/off control algorithm. The process of operating the linear matrix programming. Input 1 is the daylighting data; each time the daylighting simulation runs, the daylight illuminance data is transformed into one array to operate with the matrix of lighting illuminance data. Input 2 is the indoor recommendation illuminance level (threshold). Input 3 is the total number of selected luminaires. After each time the hourly switch on/off control algorithm runs, it finds the best solution (0 or 1). The output includes the number of luminaires that should turn on or off to satisfy the indoor recommendation illuminance level (threshold), and one list includes the luminaires on/off pattern from luminaire 1 to n. 172 Component 9 is the proposed daylight-linked dimming control algorithm (Fig. 4.11) (See Chapter 3 Section 3.4.3 for the detailed explanation of the mathematical process for the linear matrix programming, and see Appendix A.2.9 and A.3 for the entire code in Grasshopper and Visual Studio). Figure 4. 11 The customized Python scripts (component 9) - the proposed daylight-linked dimming control algorithm. The process of operating the linear matrix programming. 173 Input 1 is the daylighting data; each time the daylighting simulation runs, the daylight illuminance data is transformed into one array to operate with the matrix of lighting illuminance data. Input 2 is the indoor recommendation illuminance level (threshold). Input 3 is the total number of selected luminaires. After each time the hourly dimming control algorithm runs, it finds the best solution (from 0 to 1). The output includes each luminaire’s hourly light output value (float) in order to satisfy the indoor recommendation illuminance level (threshold) as one list from luminaire 1 to n. Component 10 is a lighting electricity calculator for hourly switch on/off and dimming control (Fig. 4.12). This creation of this component is based on the assumption that the light output is linearly proportional to the dimmable luminaire's electricity power consumption. The user can edit the code to add the formula to calculate the dimmable luminaire's electricity usage. Figure 4. 12 The customized Python scripts (component 10) - the lighting electricity calculator for hourly switch on/off and dimming control. 174 Component 12 is a timeline generator (Fig. 4.13). When lighting control algorithms finish, the results will be written to an Excel file for further import into Power BI for the data visualization dashboard. This component generates an hourly timeline (one column will be added to the exported Excel) corresponding to each hourly lighting control result. Figure 4. 13 The customized Python scripts (component 12) - timeline generator add an hourly timeline (one column) to the exported Excel. 4.1.5. Data Visualization Component 14 was developed to calculate the average hourly illuminance (from daylight and luminaires) distribution value for all the analysis hours (Fig. 4.14). 175 Figure 4. 14 The customized Python scripts (component 14) – calculator for the average hourly illuminance (from daylight and luminaires) distribution value for all the analysis hours. The first input is the recorded illuminance data from luminaires after switching on/off control and certain time daylighting illuminance data. The second input is the recorded illuminance data from luminaires after dimming control and certain time daylighting illuminance data. The third and fourth inputs are the switch on/off dimming patterns of luminaires for all the analysis hours. The fifth input is the total analysis hours. The sixth input is the total luminaire number. The seventh input is the total sensor points for the lighting and daylighting simulations (See Chapter 5, section 5.1.4 for the operation process in the case study). 176 Component 15 was developed for the text title in illuminance distribution false-color map, including titles for the hourly and yearly average switch on/off and dimming control illuminance distribution false-color maps (Fig. 4.15) (Fig. 4.16). Figure 4. 15 The customized Python scripts (component 15) – text title creator for illuminance distribution false-color map. Figure 4. 16 The created title in illuminance distribution false-color map. The example title of dimming control illuminance distribution false-color maps Component 16 was created for link generation (Fig. 4.17). Input 1 is the number of analysis hours. The second input is the URL link from AWS S3. It is noted that the second input 177 should put the link with the “00000.” as the end (See Chapter 5, Section 5.1.4 for the implementation in the case study). Figure 4. 17 The customized Python scripts (component 16) - Links String Generator From AWS S3. 4.2. Workflow in Visual Studio The flowchart that runs lighting control algorithms in Grasshopper was written and run with Python in Visual Studio (Fig. 4.18) (See Appendix A.3 for the entire code). The reason for using Visual Studio is that when the control algorithm ran for the whole year in the Grasshopper environment, it caused the program to crash. In addition, the code running in the Visual Studio was faster than Grasshopper. The whole year hourly lighting control algorithms were run in Visual Studio. This section describes each part of code in Visual Studio corresponding to the script in Grasshopper. 178 Figure 4. 18 The lighting control algorithms in Grasshopper were written and run with Python in Visual Studio. The whole year switching on/off and dimming control algorithms were run in Visual Studio with Python 3.9. For the whole year (8760 hours) analysis, the lighting and daylighting simulation were also operated in Grasshopper. 4.2.1. Importing Modules The control algorithms use NumPy and Pulp functions from Python packages to process the data and operate linear matrix programming. The “GHPython Remote” plugin is used to connect the “GHPython script” component to an external instance of Python that runs the usual programs in Grasshopper. The Pulp module uses the “optimize” model to find the best solution for the linear programming problems within matrixes (Fig. 4.19). Using the “xlsxwriter,” the output data can be organized, processed, exported, and written to Excel (Fig. 4.20). In sum, these important modules must also be imported in Visual Studio before constructing the algorithm with Python 3.9. 179 Figure 4. 19 Importing key modules in Visual Studio (top) for linear matrix programming with explanation and Grasshopper (bottom). Figure 4. 20 Importing key modules in Visual Studio (top) for writing output data to Excel with explanation and Grasshopper (bottom). 4.2.2. Data Input The total number of luminaires, illuminance recommendation level (threshold), and input watt of luminaires must be input to operate the linear programming for the proposed lighting control algorithm and calculate the lighting electricity usage (Fig. 4.21). They need to be manually input in Visual Studio. 180 Figure 4. 21 Inputting the total number of luminaires, illuminance recommendation level (threshold), and input watt of luminaires in Visual Studio. The whole year’s hourly daylighting illuminance data (trimmed data) and lighting illuminance data (trimmed data) are recorded and exported as a CSV file using the C# script. Then it is called, read, and processed by Pandas modules with Python in Visual Studio for the linear matrix programming operation (Fig. 4.22) (Fig. 4.23) 181 Figure 4. 22 Inputting the whole year hourly daylighting illuminance data file (trimmed data) into Visual Studio with Python for the linear matrix programming operation. Figure 4. 23 Inputting lighting illuminance data file (trimmed data) into Visual Studio with Python for the linear matrix programming operation. 4.2.3. Lighting Control Algorithms The switch on/off and dimming control algorithm and hourly lighting electricity usage were written in Visual Studio. It is noted that the layer of the loop is different in Grasshopper (Fig. 4.24) (Fig. 4.25). 182 Figure 4. 24 The switch on/off control algorithm and hourly electricity calculation were written in Visual Studio. Figure 4. 25 The dimming control algorithm and hourly electricity calculation were written in Visual Studio. 183 In line 70 and line 125, these functions avoid the output value beyond 1 in the switch on/off and dimming control when there is an infeasible solution (0 and 1 represent turn off or on the light for switch on/off control) (0 to 1 represent the light output level in dimming control). When there is an infeasible solution, the value will be more than 1, which is unacceptable to common sense. Line 76 and 137 are used for calculating the hourly lighting electricity usage for the luminaires with the switch on/off and dimming control. 4.2.4. Date Output After each time the hourly switch on/off and dimming control algorithm runs, the results are processed, appended, and organized. This data is ready to write into the Excel file as rows and columns (Fig. 4.26). Figure 4. 26 Process, append, and organize data after each time the control algorithms run. The flowchart in Grasshopper (left) and code were written in Visual Studio (right). 184 The results of lighting controls and titles were appended with the whole year timeline, analysis area, sky condition data (Fig. 4.27). Figure 4. 27 The process of writing the lighting control results to the Excel file in Grasshopper (left) and Visual Studio (right). 4.2.5. Data Visualization The entire year's hourly daylighting illuminance data (original data) and lighting illuminance data (original data) are generated and exported to the Excel file. Then, the Excel files are imported, read, and processed to calculate with the results of lighting control. The result was the hourly daylighting and lighting illuminance value (after lighting controls) on the analysis surface (Fig. 4.28). 185 Figure 4. 28 Importing the data into Visual Studio to calculate hourly daylighting and lighting illuminance value (after lighting controls) on the analysis surface. 4.3. Summary This chapter described sixteen customized components in Grasshopper and their source code in section 4.1. All the customized components were named, and their functions are summarized in Table 4.1. 186 Table 4. 1 The summary of all the customized components. In section 4.2, the entire source code in Visual Studio was explained, followed by the workflow in methodology. After the lighting control algorithms were tested and run successfully, the whole year hourly switch on/off and dimming control algorithms were run in Visual Studio since the code running in the Visual Studio was faster than Grasshopper (Fig. 4. 29). Component No. Name Function/Description 1 Climate_Based_Daylight_Analysis_Time 1_Input Work with Component 4 and the “Honeybee_Generate Climate Based Sky” component to output the hourly analysis date and months from sunrise to sunset (based on diffuse sky radiation value) 2 Climate_Based_Daylight_Analysis_Time 2_Input Work with Component 4 and the “Honeybee_Generate Climate Based Sky” component to output the hourly analysis date and rangee of months from sunrise to sunset (based on diffuse sky radiation value) 3 Climate_Based_Daylight_Analysis_Time (Whole Year)_Input Work with Component 4 and the “Honeybee_Generate Climate Based Sky” component to calculate the whole year hours from sunrise to sunset (based on diffuse sky radiation value) 4 Climate_Based_Daylight_Analysis_Time _OutPut Work with Component 1-3 and the “Honeybee_Generate Climate Based Sky” component to automatically calculate the whole year hours, dates and months from sunrise to sunset. from sunrise to sunset (based on diffuse sky radiation value) 5 Daytime_Hourly_Filter Process the time data for the daylighting simulation 6 Null_Value_Filter Filter the illuminance data for null value 7 Light_Number_String_Generator Numbering and naming each analysis luminaires under different control algorithms as a string 8 Switch On/Off Control Calculator Operate the linear matrix programming for the proposed daylight- linked switch on/off control algorithm 9 Dimming Control Calculator Operate the linear matrix programming for the proposed daylight- linked dimming control algorithm 10 LightingControl_Electricity_Calculator Calculate lighting electricity for hourly switch on/off and dimming control. 11 luminaire_Input_Watt & Lumens_Per_Watt Miner Extract the input watt and calculate lumen per watt of the selected luminaire 12 Timeline String Generator Generate timeline add an hourly timeline (one column) to the exported Excel 13 CSV Writer Write the organized lighting illuminance data as CSV files. 14 Whole Year Hourly Average illuminance (luminaire+daylight) Calculator Calculate the average hourly illuminance (from daylight and luminaires) distribution value for all the analysis hours 15 Text title creator Create text titles for illuminance distribution false-color map 16 Links String Generator From AWS S3 Generate link strings with the same structure 187 Figure 4. 29 The workflow summary uses Visual Studio to run the whole year's hourly switch on/off, dimming control algorithms, and export the data. 188 Chapter 5 5. Case Study 1 This chapter describes how the proposed simulation-based switch on/off and dimming lighting control algorithms were tested and run with case study 1 model (Fig. 5.1) and how these results data were processed and visualized through dashboards. The test criteria are that for the switch on/off and dimming control, each luminaire's hourly on/off status and dimming level can be calculated after the proposed algorithms run, while the illuminance provided from luminaires and daylighting satisfy the threshold illuminance level. The detailed workflow instruction in Chapter 3 is also described in this chapter, followed along with Case Study 1 (Fig. 5.2). Chapter 6 shows the results of case study 2 and the validation process. 189 Figure 5. 1 Case Study 1 model and Test Room. Figure 5. 2 The overall flowchart of methodology workflow. 190 5.1. Run Case Study 1 Model A detailed workflow instruction for Case Study 1 is described in this section. The case study building model was acquired from the Autodesk Revit sample building model, located in Manchester, NH (Fig. 5.3). The first-floor northwestern room and its lighting plan were selected as the test room to run daylight and lighting simulations, to examine the lighting control algorithms, and to develop dashboards for visualizing the results from lighting controls. Figure 5. 3 Case Study 1: 3D views of Revit Sample Project (Autodesk) and test room. 5.1.1. Existing Building Model The test room is a cafeteria with a rectangular floor plan. The room’s dimensions are about 17.8 m * 8 m * 2.6 m (Fig. 5.4) (Fig. 5.5). 191 Figure 5. 4 Case Study 1: The floor plan and room type of test room in Revit Sample Project (Autodesk). Figure 5. 5 Case Study 1: The section of the test room from Revit Sample Project (Autodesk). The test room was modeled and divided by building elements in Rhinoceros 3D, version 7.13 (Fig. 5.6). 192 Figure 5. 6 Case Study 1: Test Room was modeled in Rhino based on Revit sample project model. Then, each building element and its optical properties were set in Grasshopper with Honeybee version 0.0.66 to prepare for daylight and lighting simulations (Fig. 5.7). Each element's detailed transmittance or reflectance was summarized (Table 5.1). Figure 5. 7 Case Study 1: Setting each building element and its transmittance or reflectance in Grasshopper with Honeybee version 0.0.66. 193 Table 5. 1 Case Study 1: Summary of each element's detailed transmittance or reflectance. The weather file of Manchester, New Hampshire was acquired from the EnergyPlus website (Fig. 5.8) and then imported in Grasshopper using Ladybug version 1.2.0 to process and read as an epw weather file (Fig. 5.9). Figure 5. 8 Case Study 1: Acquired weather file of Manchester from EnergyPlus website. 194 Figure 5. 9 Case Study 1: using Ladybug to process the import weather file and read it as an epw weather file. The analysis surface (the same as the floor in the test room) was input in Grasshopper, and 136 sensor points were created at 0.75-meter height from the analysis surface using Ladybug version 1.2.0 (Fig. 5.10). 195 Figure 5. 10 Case Study 1: set the analysis surface and create sensor points using Ladybug. 5.1.2. Daylighting and Lighting Data Acquisition For the analysis hours, sunrise to sunset hours in March 21 st , June 21 st , and December 21 st was first chosen as test times using the climate base sky for the algorithm examination (Fig. 5.11). The hourly illuminance (lux) was selected as the metric for the point-in-time illuminance level at each sensor point (Fig. 5.12). 196 Figure 5. 11 Analysis hours: Sunrise to Sunset in March 21st, June 21st, and December 21st, Climate Base Sky to test the algorithms. Figure 5. 12 Hourly daylighting simulation results from sunrise to sunset on March 21 st , June 21 st , and December 21 st . Three days were first tested and run with proposed switch on/off and dimming control algorithms in Grasshopper (see section 5.1.3 for the results). Then, the whole year hourly 197 daylight simulation was run in Grasshopper (8760 hours) (Fig. 5.13). These customized components calculated all the daytime hours throughout the entire year that are used for daylighting simulations. Then, all daylight illuminance data were imported in Visual Studio using Python 3.9 to run with climate base sky, CIE sunny with sun sky, CIE intermediate with sun sky, and CIE cloudy sky (Fig. 5.14) (Table 5.2). The Python script on the left filters the daytimes hours. If the input time is nighttime, it automatically outputs daylight illuminance values as 0. This component reduces the time that runs the whole year hourly daylighting simulation. Figure 5. 13 Case Study 1: The whole year analysis hours. Using customized components to calculate the sunrise to sunset hours through the entire year with Python script and "Honeybee_Generate Climate Base Sky" Component. 198 Figure 5. 14 Case Study 1: Whole year analysis hours with four different types of sky conditions: climate base sky, CIE sunny with sun sky, CIE intermediate with sun sky, and CIE cloudy sky. Table 5. 2 Case Study 1: summary of weather file, location, and sky file. The settings of the Radiance parameters are summarized in Table 5.3. To acquire the lighting illuminance level on each sensor point, twelve luminaires were modeled in Rhino (it referenced the lighting plan in Revit Sample Project) (Fig. 5.15). Each analysis luminaire was numbered from 1 to 12. The selected IES files and LED light types were summarized (Table 5.4). Weather File Choice TMY3 EPW weather files Location Manchester, NH Sky File Climate Based Sky , CIE Sunny With Sun Sky, CIE Intermediate With Sun Sky, and CIE Cloudy Sky 199 Table 5. 3 Radiance Parameters for both daylight and lighting simulations for case studies. Figure 5. 15 Case Study 1: lighting plan in Rhino (Top), lighting plan in Revit Sample Project (Bottom). Parameter in Radiance Definition Settings in Case Study 1 & 2 Ambient bounces (-ab) the maximum number of diffuse bounces computed by the indirect calculation 2 Ambient accuracy (-aa) the maximum error (expressed as a fraction) permitted in the indirect irradiance interpolation 0.1 Ambient resolution(-ar) sets the distance between ambient calculations by determining the maximum density of ambient values used in interpolation 300 Ambient divisions (-ad) set the number of initial sampling rays sent from each ambient point into the hemisphere to determine the indirect incident light 1000 Ambient super-samples (-as) the number of extra rays that will be used to sample areas in the divided hemisphere that appear to have high variance 128 200 Table 5. 4 Case Study 1: summary of the selected IES files and LED light type. The Honeybee plugin was used for lighting simulation and read the details of luminaires (Fig. 5.16). Figure 5. 16 Using Honeybee plugins to conduct the lighting simulation to test whether the lighting plan satisfies the IESNA recommendation for the illuminance level. This process tested whether the modeled lighting plan satisfied the IESNA recommended illuminance level. Since the room type in Case Study 1 is a cafeteria, the requirement of illuminance level was selected as 250 lux (Table. 5.5). The test room information for study case 1 was summarized (Table. 5.6). Luminaire Catalog Number CM9OWE-830H-L1246 Luminaire Description Cubic Max Recessed Module - M9 Lamp Description LED 60W 7340lm Luminaire Manufacture LIGHTNET IES File Format Type IESNA:LM-63-2002 Lumens per lamp 7340 Number of lamps 12 201 Table 5. 5 Case Study 1: Summary of test room information. Room Type Caferteria Room Area 143 Square meters Window-to-Wall Ratio (WWR) 0.680 Window-to-Floor Ratio (WFR) 0.443 Recommendation illuminance Level 250 Lux Analysis Hours 35 hrs (three days test) and 8760 hrs (Whole year) Number of Sensor Points 136 Sensors points Height 0.76 Meters 202 Table 5. 6 IESNA Recommended illuminance level for Cafeteria (IESNA, 2021). The lighting plan in Case Study 1 satisfied 250 lux (97.8%). In order to run the linear programming with matrix operation, every sensor point needs more than or equal to 250 lux, or it will have infeasible solutions. (Refer to the methodology in Chapter 3 for details) (Fig. 5.17). Figure 5. 17 Case Study 1: The results of the tested lighting plan for illuminance level - 250 lux. 203 After the test was passed, 12 selected luminaires were simulated one by one to acquire the lighting illuminance data and export them to CSV files for the calculation of linear programming of the matrix (Fig. 5.18). Figure 5. 18 Case Study 1: Simulated 12 luminaires one by one to acquire lighting illuminance data and exported to CSV file. The lighting data were exported in two CSV files. One has 12 columns (the same number as selected luminaires) and 136 rows (the number of sensor points before culling) (Fig. 5.19). This data was used to visualize hourly illuminance distribution (luminaires + daylighting). Another one has 133 rows (the number of sensor points after culling) (Fig. 5.20). This data was used for linear programming of matrix multiplication. 204 Figure 5. 19 Case Study 1: The results of lighting illuminance data from 12 luminaries and 136 sensor points in a CSV file. 205 Figure 5. 20 Case Study 1: The results of lighting illuminance data from 12 luminaires and 133 sensor points in a CSV file. 206 5.1.3. Lighting Control Algorithm The dates of March 21 st , June 21 st , and December 21 st were the first tests in Grasshopper. The first step is to run the “GH Python Remote” component to import Python modules, specifically the Pulp, Numpy, and Scipy modules (Fig. 5.21). Figure 5. 21 Using the “GH Python Remote” component to import Python module - Pulp, numpy, and Scipy. Several customized GH components were created to run and process the switch on/off and dimming control algorithms. Firstly, by animating the “Number Slider” component, each hour analysis went through the “Honeybee_Generate Climate Based Sky” component to create different sky files. When each hour daylighting simulation was running, the illuminance data connected to the “Switch On/Off” and “Dimming” components to operate the linear 207 programming of the matrix. Then, connect the result to the “Data Recorder” component to record the data from each time run (Fig. 5.22). Figure 5. 22 Case Study1: Operating the switch on/off and dimming control algorithms for the three days test. Finally, the data was organized and exported to an Excel file. The Excel sheet contains point-in-time light switch on/off status, light output level, and hourly electricity usage (Fig. 5.23). Figure 5. 23 Case Study 1: The results of the switch on/off and dimming control for 12 luminaries with three days test. 208 The timeline on the sheet's header represents the month of day and time. The analysis area represents the floor area of the test room. “S_L1” and “D_L1” represent the switch on/off control (0 or 1) and dimming control for analysis (0 to 1 represent the light output level) in luminaire #1. The sky condition represents the sky file used in daylight simulation, which is the climate-based sky file in this case. The whole year switching on/off and dimming control algorithms were run in Visual Studio with Python 3.9 and exported to Excel files. For the whole year (8760 hours) analysis, the lighting and daylighting simulation were also operated in Grasshopper. The differences are that the whole year's hourly daylighting illuminance data (original data) was recorded and exported as an Excel file using the TT toolbox plugin, and the whole year’s hourly daylighting illuminance data (trimmed data) was recorded and exported as a CSV file using C# script (Fig. 5.24). In addition, the whole year timeline, analysis area, sky condition were extracted and exported as another Excel File (Fig. 5.25). The data in this file will later be appended with results data. 209 Figure 5. 24 Export the entire year's hourly daylighting illuminance data (trimmed and original) to a CSV and Excel file. Figure 5. 25 The whole year timeline, analysis area, and Sky Condition file usage were extracted and exported as an Excel file. Before running the program, two exported CSV files (daylighting data and lighting data), and one Excel file (timeline data) were put into the same folder as the created Visual Studio project folder (Fig. 5.26). 210 Figure 5. 26 Case Study 1: Put the exported excel files and CSV files in the created Visual Studio Project. The bottom is the way to input the files in Python code. It is important that the input file name should be the same when writing in Python code to ensure that the language reads the correct file name in the project folder. Three more inputs were manually typed in Visual Studio before running the lighting control algorithms and calculating electricity usage (Fig. 5.27). The first input was the total number of luminaries. The second input was the recommended illuminance level. Case Study 1 has 12 luminaires, the recommended illuminance level (threshold) is 250 (See Chater 3, Section 3.4 for the details of mathematics explanation). The third input was the rated power of the selected luminaire, and the input watt of the selected LED is 60 watts (See Chapter 3, Section 3.5 for the assumption of the lighting electricity calculation with proposed lighting controls). 211 Figure 5. 27 Case Study 1: inputs for the lighting control algorithm – total number of luminaries, recommendation illuminance level (threshold), and input watt. The whole year's result of the switch on/off and dimming control for the Case Study 1 test room using the climate-based sky, CIE sunny with sun sky, CIE intermediate with sun sky, and CIE cloudy sky files were exported as Excel sheets after the running was finished (Fig. 5.28) (Fig. 5.29) (Fig. 5.30) (Fig. 5.31). 212 Figure 5. 28 Case Study 1: The whole year result of the switch on/off and dimming control for the Case Study 1 test room under the climate-based sky. Figure 5. 29 Case Study 1: The whole year result of the switch on/off and dimming control for the Case Study 1 test room under CIE sunny with sun sky. 213 Figure 5. 30 Case Study 1: The whole year result of the switch on/off and dimming control for the Case Study 1 test room under CIE intermediate with sun sky. Figure 5. 31 Case Study 1: The whole year result of the switch on/off and dimming control for the Case Study 1 test room under CIE cloudy sky. 214 The exported Excel files were located in the same folder as the created Visual Studio project folder (Fig. 5.32). Figure 5. 32 Case Study 1: whole year hourly switch on/off and dimming lighting control outputs under four different types of sky conditions. 5.2. Data Visualization After hourly luminaires’ switch on/off, dimming status, and electricity usage were calculated and run from the proposed control algorithms, the hourly illuminance distribution false-color maps (luminaires + daylighting) were created for data visualization. Finally, all the exported results data from lighting controls were imported to Microsoft Power BI to create the analytical dashboards to provide the user with intuitions and insights to make decisions. 5.2.1. Lighting Control Results First, the hourly illuminance values from luminaires with hourly switch on/off and dimming controls were calculated in Visual Studio (See Chapter 4, Section 4.2.5 for source code). With each luminaire’s hourly switch on/off and dimming status, the illuminance level at each sensor point can be calculated with the results of lighting illuminance data from 12 215 luminaries on 136 sensor points by using matrix multiplication. The lighting illuminance data with 136 sensor points were imported into Visual Studio for calculation (Fig. 5.33). Figure 5. 33 Inputting the results of lighting illuminance data from 12 luminaires and 136 sensor points with a CSV file. The results of whole year hourly illuminance data from luminaires with switch on/off and dimming controls were calculated and exported into an Excel file (Fig. 5.34). The file was exported under the same folder that was created in the Visual Studio project by default. 216 Figure 5. 34 Case Study 1: The results of whole year hourly illuminance data from luminaires on 136 sensors points with switch on/off and dimming controls using climate-based sky file. Three datasets were input into Grasshopper for the whole year hourly illuminance distribution false-color maps (luminaires + daylighting): whole year hourly illuminance data from luminaires with switch on/off and dimming controls (Excel file name “SW_DM_illuminance”), whole original year hourly daylighting data (Excel file name “Original_EntireYearDaylight_Climate_Based_Sky”), and whole year hourly lighting controls data (Excel file name “SW_DM”) (Fig. 5.35). The TT toolbox was applied for importing and reading the data from Excel files in Grasshopper (Fig. 5.36). 217 Figure 5. 35 Importing three datasets to Grasshopper for creating false-color maps. Figure 5. 36 The workflow to create the whole year hourly illuminance distribution false-color maps (luminaires + daylighting). 218 The structure of these data was then processed. After processing the data, they were inputted to the “LB Spatial Heatmap” (Ladybug plugins) and other GH components to generate the false-color maps (Fig. 5.37) (Fig. 5.38). Figure 5. 37 The process to create the whole year hourly illuminance false-color maps. 219 Figure 5. 38 Case Study 1: Generating hourly illuminance distribution false-color maps (luminaires + daylighting) and the switch on/off and dimming status of each luminaire (Jan 1st 13:00) under the climate-based sky condition. On the bottom, the existing circle represents the on or off status of each luminaire. In this example, luminaire 9, 10, and 11 at January 1 st 13:00 are turned on based on the proposed switch on/off control algorithm. On the top, the radius of each circle represents the light output level of each luminaire based on the proposed dimming control algorithm. The blue line at the boundary of the room represents the glazing, and the annotation shows that 99.26% and 98.53% of sensors point reach 250 lux (threshold) with switch on/off and dimming controls. 220 The whole year's hourly false-color maps were generated with the slider animation function (Fig. 5.39). Figure 5. 39 Using the slider animation function to generate the whole year's hourly false-color maps. After the animation was finished, 8760 false-color maps were generated and stored in the folder (Fig. 5.40). 221 Figure 5. 40 Case Study 1: the whole year's hourly illuminance false-color maps and the witch on/off and dimming status of each luminaire using the climate-based sky file. 222 Three more sky conditions (CIE sunny with sun sky, CIE intermediate with sun sky, and CIE cloudy sky) were run to generate the whole year hourly illuminance distribution false-color maps (luminaires + daylighting) with the same workflow. The whole year average illuminance false-color maps and each luminaire’s switch on/off and dimming status were also calculated (Fig. 5.41). Figure 5. 41 Using recorded data from the whole year’s hourly illuminance distribution (luminaires + daylighting) to calculate the average illuminance distribution (luminaires + daylighting). Finally, the yearly average illuminance distribution false-color maps (luminaires + daylighting), and each luminaire’s switch on/off and dimming average status during the whole year was calculated and generated with four types of sky conditions (Fig. 5.42) (Fig. 5.43) (Fig. 5.44) (Fig. 5.45). 223 Figure 5. 42 Case Study 1: the whole year average illuminance distribution false-color maps (luminaires + daylighting) and the witch on/off and dimming status of each luminaire using the climate-based sky file. Figure 5. 43 Case Study 1: the whole year average illuminance distribution false-color maps (luminaires + daylighting) and each luminaire’s switch on/off and dimming average status during the whole year using CIE Sunny with Sun Sky file. 224 Figure 5. 44 Case Study 1: the whole year y average illuminance distribution false-color maps (luminaires + daylighting) and each luminaire’s switch on/off and dimming average status during the whole year using CIE Intermediate with Sun Sky file. Figure 5. 45 Case Study 1: the whole year average illuminance distribution false-color maps (luminaires + daylighting) and each luminaire’s switch on/off and dimming average status using the whole year under CIE Cloudy Sky file. 225 The results of the proposed lighting controls data and illuminance distribution false-color maps were imported to Microsoft Power BI to create the dashboards. They were used to create interactive data visualizations that could provide the decision-makers with intuitions and insights into the energy savings with the implementation of the proposed simulation-based switch on/off and dimming control. 5.2.2. Link False-color Maps All the generated illuminance distribution false-color maps were uploaded to AWS S3 in order to get Uniform Resource Locators (URLs) (Fig. 5.46). In this way, all the image data can be formatted and stored into an Excel file. It was later imported to the Power BI dashboard for visualization of the illuminance data (luminaires + daylighting). Figure 5. 46 Case Study 1: Uploaded illuminance distribution false-color maps (under the climate-based sky) to AWS S3. 226 Figure 5. 47 Case Study 1: Acquired URL of each image (Noted that from “Frame_00000” to “Frame_08759”, the structure of each link is regular). After selecting the object, the URL of each image can be acquired (Fig. 5.47). It is noted that from “Frame_00000” to “Frame_08759”, the structure of each link is regular. So, by following this rule, 8760 URLs (the whole year average illuminance distribution false-color maps) can be generated (Fig. 5.48). 227 Figure 5. 48 Using Python code to generate 8760 URLs (whole year) for the illuminance false-color map under the climate- based sky. The process of URL generation was in Grasshopper. The first URL link (https://kevin1916.s3.us-west-1.amazonaws.com/Climate_Based_Sky_Cafe/Frame_00000.png) was put into the panel. After URLs were generated, copy and paste these data to the same Excel sheet with the lighting control results (Fig. 5.49). Figure 5. 49 Case Study 1: Copy and Paste the URLs to the same Excel sheet with lighting control results. 228 Then, all the four Excel files from lighting controls results with different sky conditions were appended (Fig. 5.50). Figure 5. 50 Case Study 1: Appending all the results data from lighting controls in Excel. Ready for importing into Power BI. The appended Excel file (the results of lighting controls from four sky files) was imported to Microsoft Power BI. The purpose of transforming the data is to layer and correct the data type for interactive visualization and provide the user with the input selection (Fig. 5.51). Figure 5. 51 Importing the appended Excel file (the results of lighting controls from four sky files) to Power BI. 229 The timeline was duplicated as the quarter, month, day, hour, and time separately as columns to build up the relationship between each row and column in the data transforming process using Power Query (Fig. 5.52). In this way, the data structure can be organized and layered. Figure 5. 52 Case Study 1: Using the Power Query to add the date and time (organize and layer the data structure). The DAX function, formula, or expression in Power BI can be applied to calculate electricity cost and savings with dimming control and switching on/off control (Fig. 5.53). 230 Figure 5. 53 Case Study 1: Using DAX function in Power BI to calculate some Key Performance Indicators (KPI). 5.3. Results and Discussion Seven dashboards were created to interactively visualize the luminaries’ electricity usage, cost-saving, the lighting operation hours, and illuminance distribution false-color maps (luminaires + daylighting) under four sky conditions after implementing the proposed simulation-based switch on/off and dimming control algorithms for the case study 1 test room. Each dashboard can be selected under the bottom of the Power BI User Interface (Fig. 5.54). Users can switch the dashboard by clicking the tab. Seven dashboards are categorized into four 231 types: Lighting Electricity Usage Dashboard, Hourly Luminaires’ Status Dashboard, Lighting Operation Hours Dashboard, and Financial Analysis. Figure 5. 54 By clicking different tabs under the UI of Power BI to choose different dashboards. 5.3.1. Lighting Electricity Usage Dashboard The first dashboard (Overall Light Electricity Usage) is created to visualize the lighting electricity usage with the switch on/off and dimming control. The lighting electricity usage dashboard provides the user selection for the sky conditions, month, day, and hour with the switch on/off and dimming control (Fig. 5.55). It also contains the Lighting Power Density (LPD) (W/m 2 ) for the selected room in Manchester. 232 Figure 5. 55 Case Study 1: the lighting electricity usage dashboard to visualize the hourly, daily, and monthly lighting electricity usage with the climate-based sky file. The user can choose one sky condition from four given choices (daylight simulation results from four different sky files), the analysis time (monthly, daily, and hourly), and also change the daily operation period. The user can change the operation hours by modifying the slider and inputting the integer number. In addition, the “Select Operation Period (Hours)” slicers in all the created dashboards are synchronized. When the input value changes in one dashboard, the variables will also be applied to other dashboards. When there is no time selection, the results will be the sum of the whole year. In this case, the climate-based sky was selected without choosing the month, day, and operation period (the default setting is from 0:00 to 23:00). 233 Three more sky conditions were selected without choosing the month, day, and operation period (Fig. 5.56) (Fig. 5.57) (Fig. 5.58). Figure 5. 56 Case Study 1: the lighting electricity usage dashboard to visualize the hourly, daily, and monthly lighting electricity usage with CIE sunny with sun file. 234 Figure 5. 57 Case Study 1: the lighting electricity usage dashboard to visualize the hourly, daily, and monthly lighting electricity usage with CIE intermediate with sun file. Figure 5. 58 Case Study 1: the lighting electricity usage dashboard to visualize the hourly, daily, and monthly lighting electricity usage with CIE Cloudy sky file. 235 The different Daylight Factors (DF) with different sky conditions caused the discrepancy of illuminance values on the test surface. So, different hourly statuses of luminaires cause the fluctuation of lighting electricity usage with switch on/off and dimming controls. These four cases indicate that, in the case study 1 test room, the proposed dimming control has a greater potential to save the lighting electricity than switch on/off control (See the second dashboard for the quantitative analysis for the comparison of electricity usage and savings). In the time of the day lighting electricity usage chart, the whole year lighting electricity usage is summed up on the time of the day dimension. However, it was noted that during the whole year with four sky conditions, from 4:00 to 20:00, the lighting electricity usage could be significantly reduced with the proposed switch on/off and dimming control compared with baseline lighting mode for the test room in Manchester. In the month of year lighting electricity usage chart, it is noted that during April and September, the implementation of the proposed switch on/off and dimming controls could save more lighting electricity than October to March. From the day of month chart, the fluctuation of lighting electricity usage with two proposed lighting controls is subtle. The second dashboard (Lighting Electricity Comparison Summary) was created to represent the whole year's total lighting electricity usage and savings compared to the baseline lighting mode under four sky conditions and the whole year's average lighting electricity savings combined with four sky conditions (Fig. 5.59). 236 Figure 5. 59 Case Study 1: Whole year's total lighting electricity usage and savings to baseline under four sky conditions and the average combining with four sky conditions savings (operation hours from 0:00 to 23:00). In case study 1, the whole year's average lighting electricity savings (operation hours from 0:00 to 23:00) compared to baseline lighting mode combined with four sky conditions in switch on/off and dimming control was 42.67% and 70.93%, respectively. When the operation hours changed from 8:00 to 20:00, the whole year's average lighting electricity savings combined with four sky conditions in switch on/off and dimming control was 60.69% and 82.31% compared to baseline lighting mode (Fig. 5.60). 237 Figure 5. 60 Case Study 1: Whole year's total lighting electricity usage and savings to baseline under four sky conditions and the average combining with four sky conditions savings (operation hours from 8:00 to 20:00) 5.3.2. Hourly Luminaires' Status Dashboard The third dashboard (Hourly Luminaires' Status) provided the point-in-time validation for the space illuminance level satisfaction and the visualization of each luminaire's status with the implementation of the proposed switch on/off and dimming control algorithms. Each hourly illuminance distribution false-color map interacted with the user-select input time. It is demonstrated with four sky conditions on Sept 21 st at 8:00 (Fig. 5.61) (Fig. 5.62) (Fig. 5.63) (Fig. 5.64). 238 Figure 5. 61 Case Study 1: point-in-time validation for the space illuminance level satisfaction and the visualization of each luminaire's status with the implementation of the proposed switch on/off and dimming control algorithms – with the CIE Cloudy Sky file on Sept 21 st at 8:00. Figure 5. 62 Case Study 1: point-in-time validation for the space illuminance level satisfaction and the visualization of each luminaire's status with the implementation of the proposed switch on/off and dimming control algorithms – with the CIE Intermediate with sun sky file on Sept 21 st at 8:00. 239 Figure 5. 63 Case Study 1: point-in-time validation for the space illuminance level satisfaction and the visualization of each luminaire's status with the implementation of the proposed switch on/off and dimming control algorithms – with the CIE sunny with sun sky file on Sept 21 st at 8:00. Figure 5. 64 Case Study 1: point-in-time validation for the space illuminance level satisfaction and the visualization of each luminaire's status with the implementation of the proposed switch on/off and dimming control algorithms – with the Climate- based sky file on Sept 21 st at 8:00. 240 Each hourly illuminance level and distribution was from the daylight and luminaires with switch on/off and dimming control (the data was acquired from simulations). Each circle in the false-color map represented each luminaire's hourly status. For example, the pre-calculation results showed on Sept 21 st at 8:00 with the climate-based sky file, in the switch on/off control, the exciting circles represent the luminaire 4, 9, 10, and 12 should be turned on to achieve the recommendation illuminance level 250 lux and 99.26% of the sensor points (with 1 *1 meter grid) satisfy the 250 lux. In the dimming control, the radius of each circle represents the pre- calculated light output level of the luminaire 4, 5, 9, 10, 11, and 12 to compensate for the insufficient daylight illuminance level and 98.53% of the sensor points (with 1 *1 meter grid) satisfy the 250 lux. When applied with CIE intermediate with sun and CIE cloudy sky files, the light output level in luminaire 5 in the dimming control is nearly zero and nearly 10%, respectively. However, when applied with CIE sunny with sun sky file, only luminaire 9 and 12 should be turned on in switch control, and the light output level in luminaire 12 is nearly 30%. Interestingly, the satisfaction percentages of the illuminance level distribution have remained the same with four sky conditions except for CIE sunny with sun sky in dimming control (99.26%). 5.3.3. Lighting Operation Hours Dashboard The fourth and fifth dashboards (Light Hours of Operation in Switch Control and Light Hours of Operation in Dimming Control) included each luminaire operations hours and the whole year average illuminance distribution false-color map (luminaires + daylighting) for proposed switch on/off and dimming controls with four sky conditions and the manufacture of 241 selected luminaires. The user-select input included the sky condition, daily and day of week operation period selection. These two dashboards were created to visualize each luminaire pre- calculated lighting hours of the simulation-based lighting controls for the lighting operation and maintenance perspective. It is demonstrated with the four sky conditions and operation hours from 0:00 to 23:00 (Fig. 5.65) (Fig. 5.66) (Fig. 5.67) (Fig. 5.68) (Fig. 5.69) (Fig. 5.70) (Fig. 5.71) (Fig. 5.72). Figure 5. 65 Case Study 1: the lighting operations hours results for Switch On/Off control (with CIE Cloudy Sky and operation hours: 0:00 to 23:00). 242 Figure 5. 66 Case Study 1: the lighting operations hours results for Switch On/Off control (with CIE intermediate with sun Sky and operation hours: 0:00 to 23:00). Figure 5. 67 Case Study 1: the lighting operations hours results for Switch On/Off control (with CIE sunny with sun Sky and operation hours: 0:00 to 23:00). 243 Figure 5. 68 Case Study 1: the lighting operations hours results for Switch On/Off control (with Climate-based Sky and operation hours: 0:00 to 23:00). Figure 5. 69 Case Study 1: the lighting operations hours results for Dimming control (with CIE Cloudy Sky and operation hours: 0:00 to 23:00). 244 Figure 5. 70 Case Study 1: the lighting operations hours results for Dimming control (with CIE intermediate with sun and operation hours: 0:00 to 23:00). Figure 5. 71 Case Study 1: the lighting operations hours results for Dimming control (with CIE sunny with sun and operation hours: 0:00 to 23:00). 245 Figure 5. 72 Case Study 1: the lighting operations hours results for Dimming control (with Climate-based sky and operation hours: 0:00 to 23:00). In case study 1 test room, for four sky files, the lighting operation hours for luminaires 5 and 8 in switch on/off control are lower than other luminaires. However, in dimming control, luminaires 8 and 11 have lower lighting operation hours than other luminaires. In general, the 12 luminaires have fewer lighting operation hours in the proposed dimming control than in the proposed switch on/off control. In both switch on/off and dimming control, the results indicated that the pre-calculated lighting operations hours are highest when using CIE cloudy sky file. 5.3.4. Financial Analysis Dashboard The sixth and seventh dashboards (Overall Electricity Cost & Savings and Electricity Cost & Savings per Square Meter) provided the financial analysis for the proposed switch on/off and dimming control, including the whole year and quarterly electricity cost and saving, and electricity cost and savings per square meter analysis with proposed switch on/off and dimming 246 controls. It is noted that the cost and saving calculations were the average value under the use of four sky condition files. The electricity price in New Hampshire was acquired on the EIA website (Tabel 5.7). These two dashboards were demonstrated with the selection of the whole year (four quarts) with operation hours from 0:00 to 24:00 (Fig. 5.73) (Fig. 5.74). Table 5. 7 New Hampshire State Energy Profile – Electricity Price (EIA, 2021). 247 Figure 5. 73 Case Study 1: the whole year financial analysis dashboard to analyze the cost and saving of proposed switch on/off and dimming controls compared with baseline lighting mode for case study 1 room (operation hours: 0:00 to 23:00). The case study 1 virtual test room is around 143 square meters, with 12 luminaires 60- watts input. The selected location of the test room is virtually in Manchester, New Hampshire. When the operation hours were selected as 0:00 to 23:00, the pre-calculated annual lighting electricity cost for the baseline lighting mode was $1,033. The annual lighting electricity savings with the implementation of the proposed switch on/off and dimming control compared with baseline lighting mode is $ 440.87 and $732.82, respectively. The proposed dimming mode could save approximately 1.67 times to switch on/off mode with the case study 1 room. 248 Figure 5. 74 Case Study 1: the quarterly financial analysis dashboard analyzed the cost and saving per square meter of the proposed switch on/off and dimming controls compared with baseline lighting mode for case study 1 room (operation hours: 0:00 to 23:00). The annual lighting electricity savings per square meter when using the proposed switch on/off and dimming control compared with baseline lighting mode is $3.08 and $5.12, respectively. Two more demonstrations revealed lighting electricity cost and savings when the operation hours changed from 8:00 to 20:00 (Fig. 5.75) (Fig. 5.76). 249 Figure 5. 75 Case Study 1: the whole year financial analysis dashboard to analyze the cost and saving of proposed switch on/off and dimming controls compared with baseline lighting mode for case study 1 room (operation hours: 8:00 to 20:00). When the operation hours were selected as 8:00 to 20:00, the annual lighting electricity savings with the implementation of the proposed switch on/off and dimming control compared with baseline lighting mode is $339.6 ($2.37 per square) and $460.63 ($3.22 per square meter), respectively. However, the proposed dimming mode could save approximately 1.36 times to switch on/off mode with operation hours 8:00 to 20:00 during the whole year. 250 Figure 5. 76 Case Study 1: the quarterly financial analysis dashboard analyzed the cost and saving per square meter of the proposed switch on/off and dimming controls compared with baseline lighting mode for case study 1 room (operation hours: 8:00 to 20:00). Interestingly, for operation hours 0:00 to 23:00 and 8:00 to 20:00, with switch on/off control, the lighting electricity both has greater savings in quarters one and four. However, quarters two and three both have greater savings than quarters one and four for the dimming control. In general, without considering other financial factors such as the cost of the switch on/off and dimming control systems in reality and the switch on/off and dimming controls effects on the luminaire's lifespan, the results shows that when applied the proposed dimming control algorithm, the savings is greater than the use of the proposed switch on/off control algorithm. 251 5.4. Summary Chapter 5 introduced the application of proposed simulation-based switch on/off and dimming lighting control algorithms with case study 1 model. The study successfully the feasibility of the proposed control algorithms with the use of the daylight and lighting illuminance data from simulations. Seven data visualization dashboards demonstrate the lighting control results (Table. 5.8). The results of electricity savings for case study 1 are summarized in Table 5.9. 252 Table 5. 8 Summary of created seven dashboards. 253 Table 5. 9 The summary of electricity savings for case study 1. Electricity Savings Savings Per Square Meter Operation Hours Case Study 1 Switch On/Off 42.67% 3.08 $ Dimming 70.93% 5.12 $ Switch On/Off 60.69% 2.37 $ Dimming 82.31% 3.22 $ 0:00 to 23:00 8:00 to 20:00 254 Chapter 6 6. Case Study 2 This chapter describes how the results of the simulation-based switch on/off and dimming lighting control algorithms with the case study 2 model (Fig. 6.1). Then the results are visualized through dashboards and demonstrated. Finally, the validation process is explained. Figure 6. 1 Case Study 2 Model and Test Room. 6.1. Overall Case Study 2 Model The case study 2 building model was created in Rhino. This building is located in Monterey Park, CA (Fig. 6.2). The second-floor southeast open-floor-plan office and its lighting 255 plan were selected as the test room to run daylight and lighting simulations, and lighting control algorithms. Figure 6. 2 Case Study 2 building in Monterey Park, CA. (1) Axonometric view of entire building (2) Perspective view from Southeast. 6.1.1. Existing Building Model The test room is an open floor plan (Fig. 6.3). The ceiling height is 3 m. 256 Figure 6. 3 Case Study 2: the test room floor plan and its dimensions. Each element's settings of transmittance or reflectance in Grasshopper with Honeybee was summarized in Table 6.1. 257 Table 6. 1 Case Study 2: Summary of each element's detailed transmittance or reflectance. The weather file of Los Angeles, California was acquired from the EnergyPlus website (Fig. 6.4). The sky files for the whole year daylighting simulation are summarized in Table 6.2. Figure 6. 4 Case Study 2: Acquired weather file of Los Angeles, California from EnergyPlus website. Table 6. 2 Case Study 2: summary of weather file, location, and sky file. Case Study 2: Envelope Opaque or Transparent Reflectance or Transmittance Settings Ceiling Opaque 0.8 Interior Wall Opaque 0.7 Glazing Transparent 0.8 Floor Opaque 0.2 Exterior Shading Opaque 0.2 Exterior Context Opaque 0.2 Weather File Choice TMY3 EPW weather files Location Los Angeles, CA Sky File Climate Based Sky , CIE Sunny With Sun Sky, CIE Intermediate With Sun Sky, and CIE Cloudy Sky 258 The analysis surface is the same as the floor in the test room). And 535 sensor points were created at 0.75-meter height from the analysis surface (Fig. 6.5). The settings of the Radiance parameters are the same in Case Study 1. Figure 6. 5 Case Study 2: the analysis surface and created sensor points. 6.1.2. Daylighting and Lighting Data Acquisition Fourty-four luminaires were modeled in Rhino (Fig.6. 6). Each luminaire was numbered from 1 to 44. The selected IES files and LED light types were summarized (Table 6. 259 Figure 6. 6 Case Study 2: lighting plan and modeled luminaires in Rhino. Table 6. 3 Case Study 2: summary of the selected IES files and LED light type The test room information for study case 2 was summarized in Table 6. 4. The recommendation illuminance level for the open floor office is 500 Lux (Table 6. 5) Luminaire Description Cubic Max Suspended - P2 Lamp Description LED 80W 11370lm Luminaire Manufacture LIGHTNET IES File Format Type IESNA:LM-63-2002 Lumens per lamp 11370 Number of lamps 44 260 Table 6. 4 Case Study 2: Summary of test room information. Table 6. 5 IESNA Recommended illuminance level for Open Floor Office (IESNA, 2021). The lighting data were exported in two CSV files. One has 44 columns (the same number as selected luminaires) and 535 rows (the number of sensor points before culling) (Fig. 6.7). This data was used to visualize hourly illuminance distribution (luminaires + daylighting). Another Room Type Office Room Area (m2) 460.1 Square meters Window-to-Wall Ratio (WWR) 0.252 Window-to-Floor Ratio (WFR) 0.035 Recommendation illuminance Level 500 Lux Analysis Hours 8760 hrs (Whole year) Number of Sensor Points 535 Sensors points Height 0.76 Meters 261 one has 523 rows (the number of sensor points after culling) (Fig. 6.8). This data was used for linear programming of matrix multiplication. Figure 6. 7 Case Study 2: The results of lighting illuminance data from 44 luminaries and 535 sensor points in a CSV file. 262 Figure 6. 8 Case Study 1: The results of lighting illuminance data from 44 luminaires and 523 sensor points in a CSV file. 6.1.3. Lighting Control Algorithm The whole year's result of the switch on/off and dimming control for the Case Study 2 test room using the climate-based sky, CIE sunny with sun sky, CIE intermediate with sun sky, and CIE cloudy sky files were exported as Excel sheets after the running was finished (Fig. 6.9) (Fig. 6.10) (Fig. 6.11) (Fig. 6.12). 263 Figure 6. 9 Case Study 2: The whole year result of the switch on/off and dimming control for the Case Study 2 test room under the climate-based sky. 264 Figure 6. 10 Case Study 2: The whole year result of the switch on/off and dimming control for the Case Study 2 test room under CIE sunny with sun sky. Figure 6. 11 Case Study 2: The whole year result of the switch on/off and dimming control for the Case Study 2 test room under CIE intermediate with sun sky. 265 Figure 6. 12 Case Study 2: The whole year result of the switch on/off and dimming control for the Case Study 2 test room under CIE cloudy sky. 6.2. Data Visualization After the entire year hourly luminaires’ switch on/off, dimming status, and electricity usage were calculated and run from the proposed control algorithms, the hourly and the whole year average illuminance distribution false-color maps (luminaires + daylighting) were created with four sky conditions. Then, all the exported results data from lighting controls were imported to Power BI to create the analytical dashboards. 266 6.2.1. Lighting Control Results The whole year’s hourly false-color maps (luminaires + daylighting) were created with each sky condition. So, 4*8760 were generated and stored in the folder. This section only illustrates the whole year's hourly false-color maps for the climate-based sky (Fig. 6.13). Figure 6. 13 Case Study 2: the whole year's hourly illuminance false-color maps and the witch on/off and dimming status of each luminaire using the climate-based sky file. The yearly average illuminance distribution false-color maps (luminaires + daylighting), and each luminaire’s switch on/off and dimming average status during the whole year was 267 calculated and generated with four types of sky conditions (Fig. 6.14) (Fig. 6.15) (Fig. 6.16) (Fig. 6.17). Figure 6. 14 Case Study 2: the whole year average illuminance distribution false-color maps (luminaires + daylighting) and the witch on/off and dimming status of each luminaire using the climate-based sky file. 268 Figure 6. 15 Case Study 2: the whole year average illuminance distribution false-color maps (luminaires + daylighting) and each luminaire’s switch on/off and dimming average status during the whole year using CIE Sunny with Sun Sky file. Figure 6. 16 Case Study 2: the whole year y average illuminance distribution false-color maps (luminaires + daylighting) and each luminaire’s switch on/off and dimming average status during the whole year using CIE Intermediate with Sun Sky file. 269 Figure 6. 17 Case Study 2: the whole year average illuminance distribution false-color maps (luminaires + daylighting) and each luminaire’s switch on/off and dimming average status using the whole year under CIE Cloudy Sky file. 6.2.2. Link False-color Maps After all the URLs were generated, the data was put on the same Excel sheet with the lighting control results. Then all the four Excel files from lighting controls results with different sky conditions were appended (Fig. 6.18). The workflow is the same in chapter 5. 270 Figure 6. 18 Case Study 2: Appending all the results data from lighting controls in Excel. Ready for importing into Power BI. 6.3. Result and Discussion Similarly, seven dashboards were created to interactively visualize the luminaires’ electricity usage, cost-saving, the lighting operation hours, and illuminance distribution false- 271 color maps (luminaires + daylighting) under four sky conditions after implementing the proposed simulation-based switch on/off and dimming control algorithms for the case study 2 test room (Table 6.6). Table 6. 6 Summary of created seven dashboards. 272 6.3.1. Lighting Electricity Usage Dashboard The Overall Light Electricity Usage dashboard is created to visualize the lighting electricity usage with the switch on/off and dimming control for case study 2 room in Monterey Park, CA. Since the weather file for case study 1 is Los Angeles Intl AP (TMY3). The location in the dashboard is represented as Los Angeles. The whole year's hourly, daily and monthly sum- up of lighting electricity usage under four sky conditions is demonstrated (operation period is from 0:00 to 23:00) (Fig. 6.19) (Fig. 6.20) (Fig. 6.21) (Fig. 6.22). Figure 6. 19 Case Study 2: the lighting electricity usage dashboard to visualize the hourly, daily, and monthly lighting electricity usage with the climate-based sky file. 273 Figure 6. 20 Case Study 2: the lighting electricity usage dashboard to visualize the hourly, daily, and monthly lighting electricity usage with CIE sunny with sun file. Figure 6. 21 Case Study 2: the lighting electricity usage dashboard to visualize the hourly, daily, and monthly lighting electricity usage with CIE intermediate with sun file. 274 Figure 6. 22 Case Study 2: the lighting electricity usage dashboard to visualize the hourly, daily, and monthly lighting electricity usage with CIE Cloudy sky file. These four cases also indicate that, in the case study 2 test room, the proposed dimming control has a greater potential to save the lighting electricity than switch on/off control (See the second dashboard for the quantitative analysis for the comparison of electricity usage and savings). During the whole year, with four sky conditions, from 5:00 to 19:00, the lighting electricity usage could be significantly reduced with the proposed switch on/off and dimming control compared with baseline lighting mode for the test room. The use of the proposed switch on/off and dimming controls in February could save the most lighting electricity during the whole year under four sky conditions. The Lighting Electricity Comparison Summary dashboard for case study 2 is created (Fig. 6.23). 275 Figure 6. 23 Case Study 2: Whole year's total lighting electricity usage and savings to baseline under four sky conditions and the average combining with four sky conditions savings (operation hours from 0:00 to 23:00). In case study 2, the whole year's average lighting electricity savings (operation hours from 0:00 to 23:00) compared to baseline lighting mode combined with four sky conditions in switch on/off and dimming control was 11.34% and 23.29%, respectively. When the operation hours changed from 8:00 to 19:00, the whole year's average lighting electricity savings combined with four sky conditions in switch on/off and dimming control was 17.19% and 28.70% compared to baseline lighting mode (Fig. 6.24). 276 Figure 6. 24 Case Study 2: Whole year's total lighting electricity usage and savings to baseline under four sky conditions and the average combining with four sky conditions savings (operation hours from 8:00 to 19:00) 6.3.2. Hourly Luminaires’ Status Dashboard The Hourly Luminaires' Status dashboard for case study 2 is created to provide the point- in-time validation for the space illuminance level satisfaction and visualize each luminaire's status with the implementation of the proposed switch on/off and dimming control algorithms. It is demonstrated with four sky conditions on Sept 21st at 8:00 (Fig. 6.25) (Fig. 6.26) (Fig. 6.27) (Fig. 6.28). 277 Figure 6. 25 Case Study 2: point-in-time validation for the space illuminance level satisfaction and the visualization of each luminaire's status with the implementation of the proposed switch on/off and dimming control algorithms – with the CIE Cloudy Sky file on Sept 21 st at 8:00. Figure 6. 26 Case Study 2: point-in-time validation for the space illuminance level satisfaction and the visualization of each luminaire's status with the implementation of the proposed switch on/off and dimming control algorithms – with the CIE Intermediate with sun sky file on Sept 21 st at 8:00. 278 Figure 6. 27 Case Study 2: point-in-time validation for the space illuminance level satisfaction and the visualization of each luminaire's status with the implementation of the proposed switch on/off and dimming control algorithms – with the CIE sunny with sun sky file on Sept 21 st at 8:00. Figure 6. 28 Case Study 2: point-in-time validation for the space illuminance level satisfaction and the visualization of each luminaire's status with the implementation of the proposed switch on/off and dimming control algorithms – with the Climate- based sky file on Sept 21 st at 8:00. 279 The pre-calculation results showed on Sept 21st at 8:00 with four sky files, around 99% of the sensor points (with 1 *1 meter grid) satisfy the 250 lux in both the proposed switch on/off and dimming controls. The dashboard can clearly illustrate each luminaire on/off and dimming status when inputting the time, day, and month. 6.3.3. Lighting Operation Hours Dashboard Light Hours of Operation in Switch Control and Light Hours of Operation in Dimming Control dashboards also included each luminaire operations hours and the whole year average illuminance distribution false-color map (luminaires + daylighting) for proposed switch on/off and dimming controls with four sky conditions in case study 2. It is demonstrated with the four sky conditions and operation hours from 0:00 to 23:00 (Fig. 6.29) (Fig. 6.30) (Fig. 6.31) (Fig. 6.32) (Fig. 6.33) (Fig. 6.34) (Fig. 6.35) (Fig. 6.36). 280 Figure 6. 29 Case Study 2: the lighting operations hours results for Switch On/Off control (with CIE Cloudy Sky and operation hours: 0:00 to 23:00). Figure 6. 30 Case Study 2: the lighting operations hours results for Switch On/Off control (with CIE Intermediate with Sun Sky and operation hours: 0:00 to 23:00). 281 Figure 6. 31 Case Study 2: the lighting operations hours results for Switch On/Off control (with CIE Sunny with Sun Sky and operation hours: 0:00 to 23:00). Figure 6. 32 Case Study 2: the lighting operations hours results for Switch On/Off control (with Climate-based Sky and operation hours: 0:00 to 23:00). 282 Figure 6. 33 Case Study 2: the lighting operations hours results for Dimming control (with CIE Cloudy Sky and operation hours: 0:00 to 23:00). Figure 6. 34 Case Study 2: the lighting operations hours results for Dimming control (with CIE Intermediate with Sun and operation hours: 0:00 to 23:00). 283 Figure 6. 35 Case Study 2: the lighting operations hours results for Dimming control (with CIE Sunny with Sun and operation hours: 0:00 to 23:00). Figure 6. 36 Case Study 2: the lighting operations hours results for Dimming control (with Climate-based Sky and operation hours: 0:00 to 23:00). 284 In case study 2 test room, for four sky files, the lighting operation hours for luminaires 13 and 38 in switch on/off control are lower than other luminaires. However, in dimming control, luminaires 20, 25, and 35 have lower lighting operation hours than other luminaires. In both switch on/off and dimming control, the results indicated that the pre-calculated lighting operations hours are highest when using CIE cloudy sky file. 6.3.4. Financial Analysis Dashboard The Overall Electricity Cost & Savings and Electricity Cost & Savings per Square Meter dashboards provides the financial analysis for the proposed switch on/off and dimming control, including the whole year and quarterly electricity cost and saving, and electricity cost and savings per square meter analysis with proposed switch on/off and dimming controls for case study 2. The cost and saving calculations were average under four sky condition files. The electricity price in California was acquired on the EIA website (Tabel 6.7). Table 6. 7 California State Energy Profile - Electricity Price (EIA, 2021). 285 These two dashboards were demonstrated with the selection of the whole year (four quarts) with operation hours from 0:00 to 24:00 (Fig. 6.37) (Fig. 6.38). Figure 6. 37 Case Study 2: the whole year financial analysis dashboard to analyze the cost and saving of proposed switch on/off and dimming controls compared with baseline lighting mode for case study 2 room (operation hours: 0:00 to 23:00). The case study 2 test room is around 460 square meters, with 44 luminaires 80-watts input. The location of the building is in Monterey Park, California. When the operation hours were selected as 0:00 to 23:00, the pre-calculated annual lighting electricity cost for the baseline lighting mode was $6,253.38. The annual lighting electricity savings with the proposed switch on/off and dimming control compared with baseline lighting mode are $709.43 and $1456.29, respectively. The proposed dimming mode could save approximately 2.05 times to switch on/off mode with the case study 2 room. 286 Figure 6. 38 Case Study 2: the quarterly financial analysis dashboard analyzed the cost and saving per square meter of the proposed switch on/off and dimming controls compared with baseline lighting mode for case study 2 room (operation hours: 0:00 to 23:00). The annual lighting electricity savings per square meter when using the proposed switch on/off and dimming control compared with baseline lighting mode is $1.54 and $3.17, respectively. Two more demonstrations revealed lighting electricity cost and savings when the operation hours changed from 8:00 to 19:00 (Fig. 6.39) (Fig. 6.40). 287 Figure 6. 39 Case Study 2: the whole year financial analysis dashboard to analyze the cost and saving of proposed switch on/off and dimming controls compared with baseline lighting mode for case study 2 room (operation hours: 8:00 to 19:00). When the operation hours were selected as 8:00 to 19:00, the annual lighting electricity savings with the implementation of the proposed switch on/off and dimming control compared with baseline lighting mode is $537.37 ($1.17 per square) and $897.39 ($1.95 per square meter), respectively. However, the proposed dimming mode could save approximately 1.67 times to switch on/off mode with operation hours 8:00 to 19:00 during the whole year. 288 Figure 6. 40 Case Study 2: the quarterly financial analysis dashboard analyzed the cost and saving per square meter of the proposed switch on/off and dimming controls compared with baseline lighting mode for case study 2 room (operation hours: 8:00 to 19:00). During operation hours 0:00 to 23:00 and 8:00 to 20:00, with switch on/off and dimming control, the lighting electricity savings are relatively even in four quarters. Similar to case study 1, without considering other financial factors such as the cost of the installation of the switch on/off and dimming control systems in reality and the effects of lighting control on the luminaire's lifespan, the results show that the use of the proposed dimming control has greater lighting electricity savings than proposed switch on/off control. 6.4. Validation This section presents a validation of the proposed simulated-based daylight-linked switch on/off and dimming control algorithm for a shoebox model. The purpose is to check the rationality and accuracy of the lighting controls results in extreme conditions (nighttime and before sunset). 289 6.4.1. Validation Model A shoebox model is created to validate the proposed lighting control algorithm (Fig. 6.41). The glazing is only created on the south of the model. All the information for the shoebox model is summarized (Table. 6.8) (Table. 6.9). Figure 6. 41 Shoebox Model used for validation of proposed lighting control algorithm. 290 Table 6. 8 Model information used for the shoebox model for validation. Table 6. 9 Simulation Setting used for the shoebox model for validation. 6.4.2. Pathological Case The point-in-time daylighting simulations and switch on/off and dimming control algorithms run. The illuminance distribution false-color maps from daylighting and luminaires are created. For each simulation set, three false-color maps are created. The illuminance distribution includes only daylighting, only luminaires from switch on/off control, and only Room Area 143 Square meters Window-to-Wall Ratio (WWR) 0.155 Window-to-Floor Ratio (WFR) 0.146 Thershold Set in Algorithm 250 Lux Number of Sensor Points 136 Sensors points Height 0.76 Meters Weather File Choice TMY3 EPW weather file Location Manchester, NH Sky File Climate Based Sky Luminaire Description Cubic Max Recessed Module - M9 Luminaire Manufacture LIGHTNET IES File Format Type IESNA:LM-63-2002 Lumens per lamp 7340 Number of lamps in shoebox model 12 Shoebox Box: Envelope Opaque or Transparent Reflectance or Transmittance Settings Ceiling Opaque 0.8 Interior Wall Opaque 0.7 Glazing Transparent 0.8 Floor Opaque 0.2 Exterior Shading Opaque 0.2 Exterior Context Opaque 0.2 291 luminaires from dimming control. First, the simulation is run on March 23 rd at 23:00 (nighttime) (Fig. 6.42) (Fig. 6.43). Figure 6. 42 March 23rd 23:00 (nighttime). Figure 6. 43 March 23rd 23:00 (nighttime)- zoomed view. 292 On the left, the false-color map illustrates the daylight illuminance distribution. Obviously, during the nighttime, there is no daylighting. The middle and right false-color maps illustrate the light status for 12 luminaires. In this case, for the switch on/off control, 12 luminaires are turned on at nighttime. In the dimming control, each luminaire turned on at a certain level. The pathological case indicates that the control algorithms run properly in a natural human sense behavior. Then, the illuminance value provided from luminaires under switch on/off and dimming control is validated (Fig. 6.44) (Fig. 6.45) Figure 6. 44 March 23rd 23:00 (nighttime) validation. 293 Figure 6. 45 March 23rd 23:00 (nighttime) validation - zoomed view. In this case, the lighting control algorithm functions as it supposed to be. The results are 100% satisfied with the threshold level in both controls. Two more dates are selected to run: June 23 rd and December 23 rd at 23:00. Then, the illuminance value provided from luminaires under switch on/off and dimming control is validated (Fig. 6.46) (Fig. 6.47) (Fig. 6.48) (Fig. 6.49). 294 Figure 6. 46 June 23rd 23:00 (nighttime). Figure 6. 47 June 23rd 23:00 (nighttime) - zoomed view. 295 Figure 6. 48 December 23rd 23:00 (nighttime). Figure 6. 49 December 23rd 23:00 (nighttime) - zoomed view. 296 Still, the lighting control algorithm functions as it supposed to be. In both controls, the results are 100% satisfied with the threshold level. The results from these three days during the nighttime are the same (Fig. 6.50) (Fig. 6.51) (Fig. 6.52) (Fig. 6.53). Figure 6. 50 June 23rd 23:00 (nighttime) validation. 297 Figure 6. 51 June 23rd 23:00 (nighttime) validation - zoomed view. Figure 6. 52 December 23rd 23:00 (nighttime) validation. 298 Figure 6. 53 December 23rd 23:00 (nighttime) validation - zoomed view. Later on, the simulation is run on March 23 rd at 17:00, June 23 rd at 19:00, and December 23 rd at 16:00 (before sunset). On March 23 rd at 17:00, when it is close to sunset, the area under luminaires 1, 2, and 3, the daylighting is enough in terms of the threshold level. The switch on/off control illustrates that the luminaire 1, 2, 3, and 8 are turned off. For dimming control, luminaires 1, 2, and 3 are closed to dim to 0, and other luminaires dimmed evenly (Fig. 6.54) (Fig. 6.55). 299 Figure 6. 54 March 23rd 17:00 (before sunset). Figure 6. 55 March 23rd 17:00 (before sunset) - zoomed view. 300 Then, the daylight illuminance value on March 23 rd at 17:00 is added with luminaires illuminance value from switch on/off and dimming control (Fig. 6.56) (Fig. 6.57). Figure 6. 56 March 23rd 17:00 validation. Figure 6. 57 March 23rd 17:00 validation - zoomed view. 301 In this case, the lighting control algorithm functions as it supposed to be. The results are 100% satisfied with the threshold level in both controls. On June 23 rd , at 19:00, when it is close to sunset, the daylighting illuminance is enough under luminaires 2 and 3. However, the switch on/off control illustrates that luminaires 1, 2, and 8 are turned off (Fig. 6.58) (Fig. 6.59). Figure 6. 58 June 23rd 19:00 (before sunset). 302 Figure 6. 59 June 23rd 19:00 (before sunset) - zoomed view. Then, the daylight illuminance value on June 23 rd , at 19:00 is added with luminaires illuminance value from switch on/off and dimming control (Fig. 6.60) (Fig. 6.61). 303 Figure 6. 60 June 23rd 19:00 validation. 304 Figure 6. 61 June 23rd 19:00 validation - zoomed view. Similarly, the lighting control algorithm functions well. The results are both 100% satisfied with the threshold level in both controls. On December 23 rd , at 16:00, when it is close to sunset, the daylighting illuminance is enough under luminaires 2 and 3. The switch on/off control illustrates that luminaires 2, 3, and 8 are turned off. Still, the luminaire 8 is surprisingly turned off. Under the dimming control, luminaires 2 and 3 are dimmed to 0, and luminaire 1 is close to dim to 0 (Fig. 6.62) (Fig. 6.63). 305 Figure 6. 62 December 23rd 16:00 (before sunset). Figure 6. 63 December 23rd 16:00 (before sunset) - zoomed view. 306 Then, the daylight illuminance value is on December 23 rd , at 16:00 is added with luminaires illuminance value from switch on/off and dimming control (Fig. 6.64) (Fig. 6.65). Figure 6. 64 December 23rd 16:00 validation. Figure 6. 65 December 23rd 16:00 validation - zoomed view. 307 Still, the lighting control algorithm functions well. The results are both 100% satisfied with the threshold level in both controls. The pathological cases found that the proposed switch on/off and dimming control algorithm can both rationally and accurately calculate each luminaire on/off and dimming status based on the point-in-time daylighting illuminance level to satisfy the set threshold level. 6.5. Summary This chapter summarized the results of proposed simulation-based lighting control algorithms with the case study 2 model on seven data visualization dashboards. The results of electricity savings for case study 2 are summarized in Table 6.10. Table 6. 10 The summary of the electricity savings for case study 2. Additionally, the validation of the proposed switch on/off and dimming controls are conducted with the pathological cases. It is found that the proposed control algorithms can both rationally and accurately calculate each luminaire on/off and dimming status and satisfy the threshold illuminance level. Electricity Savings Savings Per Square Meter Operation Hours Case Study 2 Switch On/Off 11.34% 1.54 $ Dimming 23.29% 3.17 $ Switch On/Off 17.19% 1.17 $ Dimming 28.70% 1.95 $ 0:00 to 23:00 8:00 to 19:00 308 Chapter 7 7. Discussion and Future Work This chapter summarizes the overall methodology and discusses the limitations of the current workflow during the daylight-linked lighting control algorithms development. Furthermore, the chapter proposes the future potential of field-testing for the proposed lighting control algorithms and the improvements of the data visualization for the control results. 7.1. Discussion The principle of daylight-linked lighting control is when the amount of inside daylight reaches or falls below a certain threshold, the electric lights are turned off/dimmed or turned on/brightened. Previous research has proved that both closed-loop and open-loop daylight-linked dimming and switch lighting control systems have great potential to reduce energy consumption in buildings. It also provides solid foundations for the daylight simulation, the principle of the light sensors, and the logic of the daylight-linked switch on/off and dimming control. The overall methodology includes preparing the existing building model for daylighting simulation, daylighting data acquisition, lighting control algorithm, and data visualization dashboard (Fig. 7.1). 309 Figure 7. 1 Overall outline of the workflow diagram of Methodology and software and tools usage. Rhino and Grasshopper were used for creating the daylight and lighting simulation models with customized components written in Python (Table. 7.1). Table 7. 1 The summary of all the customized components. Component No. Name Function/Description 1 Climate_Based_Daylight_Analysis_Time 1_Input Work with Component 4 and the “Honeybee_Generate Climate Based Sky” component to output the hourly analysis date and months from sunrise to sunset (based on diffuse sky radiation value) 2 Climate_Based_Daylight_Analysis_Time 2_Input Work with Component 4 and the “Honeybee_Generate Climate Based Sky” component to output the hourly analysis date and rangee of months from sunrise to sunset (based on diffuse sky radiation value) 3 Climate_Based_Daylight_Analysis_Time (Whole Year)_Input Work with Component 4 and the “Honeybee_Generate Climate Based Sky” component to calculate the whole year hours from sunrise to sunset (based on diffuse sky radiation value) 4 Climate_Based_Daylight_Analysis_Time _OutPut Work with Component 1-3 and the “Honeybee_Generate Climate Based Sky” component to automatically calculate the whole year hours, dates and months from sunrise to sunset. from sunrise to sunset (based on diffuse sky radiation value) 5 Daytime_Hourly_Filter Process the time data for the daylighting simulation 6 Null_Value_Filter Filter the illuminance data for null value 7 Light_Number_String_Generator Numbering and naming each analysis luminaires under different control algorithms as a string 8 Switch On/Off Control Calculator Operate the linear matrix programming for the proposed daylight- linked switch on/off control algorithm 9 Dimming Control Calculator Operate the linear matrix programming for the proposed daylight- linked dimming control algorithm 10 LightingControl_Electricity_Calculator Calculate lighting electricity for hourly switch on/off and dimming control. 11 luminaire_Input_Watt & Lumens_Per_Watt Miner Extract the input watt and calculate lumen per watt of the selected luminaire 12 Timeline String Generator Generate timeline add an hourly timeline (one column) to the exported Excel 13 CSV Writer Write the organized lighting illuminance data as CSV files. 14 Whole Year Hourly Average illuminance (luminaire+daylight) Calculator Calculate the average hourly illuminance (from daylight and luminaires) distribution value for all the analysis hours 15 Text title creator Create text titles for illuminance distribution false-color map 16 Links String Generator From AWS S3 Generate link strings with the same structure 310 The simulation data was applied to develop and test the feasibility of proposed switch on/off and dimming control algorithms. The annual hourly controls were run in Visual Studio with two case studies models. The results were visualized and analyzed in the Microsoft Power BI dashboard to provide insights for decision-makers. Seven dashboards were created to interactively visualize the luminaries’ electricity usage, cost-saving, the lighting operation hours, and illuminance distribution false-color maps (luminaires + daylighting) under four sky conditions after implementing the proposed simulation-based switch on/off and dimming control algorithms for the case models (Table 7.2). 311 Table 7. 2 Summary of created seven dashboards. The created dashboards are applied in Case Study 1 (Fig. 7.2) (See Chapter 5, Section 5.3 for details) and Case Study 2 (Fig. 7.3) (See Chapter 6, Section 6.2 for details). 312 Figure 7. 2 The created dashboards applied in Case Study 1 (lighting operation hours dashboard only showed one type for on/off control here). 313 Figure 7. 3 The created dashboards applied in Case Study 2. (lighting operation hours dashboard only showed one type for dimming control here). The case study 1 test model is a virtual cafeteria room, around 143 square meters, with 12 luminaires 60-watts input. The selected location of the test room is virtually in Manchester, New Hampshire. When the operation hours were set from 8:00 to 20:00, the whole year's average lighting electricity savings combined with four sky conditions in switch on/off and dimming control was about 61% and 82% compared to baseline lighting mode. 314 The case study 2 test office room is around 460 square meters, with 44 luminaires 80- watts input. The location of the building is in Monterey Park, California. When the operation hours changed from 8:00 to 19:00, the whole year's average lighting electricity savings combined with four sky conditions in switch on/off and dimming control was about 17% and 29% compared to baseline lighting mode. Two case studies' annual daylight and lighting illuminance data can be processed with linear matrix programming, and the proposed lighting control algorithms can be run without any compiler errors. The hourly on/off and dimming status and electricity usage of each luminaire can be calculated and visualized for both test rooms, demonstrating the feasibility of the proposed lighting control algorithm run with different project locations, weather files, room size and layout, and multiple luminaires. Finally, the validation process found that the proposed control algorithms can rationally and accurately calculate each luminaire on/off and dimming status and satisfy the threshold illuminance level (Fig. 7.4) (See Chapter 6, Section 6.4 for details). 315 Figure 7. 4 The summary of validation for the Control Algorithms. 7.2. Evaluation and Limitations This section discusses the evaluation and limitations of the current workflow for the proposed lighting control algorithms and the data visualization for the control results. 316 7.2.1. Evaluation of current workflow The proposed simulated-based daylight-linked lighting control logic was abstracted as a mathematical problem. By applying the mathematical approach, the daylight and lighting illuminance data were first processed into matrixes. Then, the mathematical model was built with the linear matrix programming as proposed algorithms (Fig. 7.5) (Fig. 7.6). Figure 7. 5 The overall flowchart of the switch on/off control algorithm. Figure 7. 6 The overall flowchart for the dimming control algorithm. The overall methodology of simulated-based daylight-linked lighting control includes four main stages: preparing the existing building model for daylighting simulation, daylighting data acquisition, lighting control algorithm, and data visualization dashboard (Fig. 7.7). 317 Figure 7. 7 The overall Methodology diagram. However, from preparing the building geometry to conducting the control algorithms and finally to visualizing the results from lighting controls, many pieces of tools and software need to be involved. The daylight and lighting simulation process was in Grasshopper with the Rhino model to acquire the illuminance data. The whole year's hourly lighting control was calculated in Mircosoft Visual Studio with the linear matrix programming. The Mircosoft Power BI was used visualized control results. In addition, the illuminance false-color maps need to be stored in the cloud so that the Power BI can read these images as URLs, and the interactive process can work in this platform. 318 So, it is important to package all these pieces of tools and programs together into a single platform. Since lots of inputs are needed to complete the workflow and run the software, designing a user-friendly interface is addressed. So, the user can follow the instruction to input the building geometry, EPW weather files, analysis surface, IES files, size of the sensor grid, number of luminaires, and the threshold for the illuminance level. With the implementation of the proposed daylight-linked lighting control algorithms in two case studies, the whole year's hourly lighting controls patterns can be calculated. Since there is a huge amount of data is generated after running the algorithms. The necessity of the data analysis is apparent. As a result, the data visualization dashboards were created, each hourly luminaire's status can be visualized intuitively, the lighting electricity comparison between switch on/off control and dimming control with the baseline lighting mode can be analyzed in different dimensions, and the cost/savings for the lighting electricity can be analyzed. These pre- calculated control results can also be visualization interactively; the user can choose the different variables such as time, date, and sky condition to evaluate the result and make decisions. 319 7.2.2. Limitations of the current workflow Admittedly, there are still some limitations during the overall workflow (Fig. 7.8). Figure 7. 8 The overall flowchart of methodology workflow. First, the accuracy of the created building model in Rhino is important. However, the details in the case study models were not modeled, such as the window's mullions and interior furniture. Due to the level of detail in the building model, the daylight and lighting simulation results may vary. Furthermore, the discrepancy of the illuminance data may cause different outputs such as hourly switch on/off patterns and dimming levels for each luminaire. In addition, the case study room did not consider the adjustable interior shades, such as louvers that the occupants may adjust since the adjustable louver is a variable when simulating the interior daylight and lighting illuminance. As a result, the proposed lighting control algorithms are unsuitable for applying to buildings with adjustable interior shades. 320 Second, the surrounding building (and landscaping) context is another important factor that affects indoor daylight illuminance. Two case study models did not model the building surroundings, such as landscapes and adjacent buildings. Additionally, the reflectance of the modeled surrounding context in the two case studies may be inaccurate (they were set as 0.2 in two case studies). Third, the daylight and lighting illuminance data were acquired from simulations with radiance as the calculation engine. Radiance improves interpolations by utilizing the diffuse indirect computation in conjunction with the gradient information from each Monte Carlo evaluation (Chadwell, 1997). However, as a result of Radiance's hybrid calculating technique, simulation results will vary slightly between each run. In addition, simulation-related biases cause the prediction from simulation to generate a slightly higher or lower value. These biases can substantially impact the outcome and forecast of simulated daylighting and lighting illuminance value. These further cause the lighting control algorithms to output the slightly different lighting switch on/off and dimming patterns and energy consumption from electric lighting. Fourth, the threshold illuminance level is based on the IESNA space recommendation level, and the sensor points were faced upward at 0.76 meters (30 inches) distance to the floor when modeled to simulate horizontal illuminance by following the IESNA guideline. However, indoor lighting quality is also an important factor that needs consideration, such as glaring and circadian rhythm. 321 Fifth, the lighting electricity consumption from the dimming control algorithm uses the simplified method; it is assumed that the light output is linearly proportional to the power consumption. The more accurate formula to calculate the electricity usage for the specific dimmable luminaires needs to consult lighting engineers or electrical engineers. Sixth, when the control algorithm ran for the whole year in the Grasshopper, the “GH Python Remote” plugin was used for importing the external Python module to run the linear programming. However, it caused the program to crash and get stuck Grasshopper environment. As a result, Visual Studio was applied to run the whole year's hourly lighting control algorithms; it runs faster and more stable than runs in Grasshopper. But the illuminance data need to be prepared in Grasshopper and then input into Visual Studio to run. As a result, the overall workflow is not continuous. Finally, the proposed lighting controls algorithms applied linear matrix programming. It was written with Python language and run in the Visual Studio environment. The PuLP module can generate Mathematical Programming System (MPS), and Linear Programming (LP) files to solve linear problems. However, this module has its limitations. When defining variables with LpVariable and when adding constraints, the CPU load is fairly low since setting up the problem needs time. In other words, when increasing the analysis surface area, the sensor points increase, the constraints increase. Also, the number of variables increases when increasing the number of luminaires to run the proposed control algorithms. The algorithm running time for two case studies was summary in Table 7.3. 322 Table 7. 3 The running time of the proposed algorithm for two case studies. It is important to mention that the program running time may become longer when the proposed lighting control algorithms are applied to larger space areas with more luminaires. The possible solution is to run proposed control algorithms with different rooms simultaneously but parallelly. Otherwise, the optimization of the algorithm inside the PuLP module to reduce the time for setting up the LP problem would be one research domain in computer science. 7.3. Future Work This section proposes the potential improvements for the current workflow in terms of its limitations that can be achieved within a short period. In addition, the workflow and methods for the future field-test of the proposed lighting control algorithms are discussed. 7.3.1. Enhancements Currently, in order to run the whole year’s hourly switch on/off and dimming control algorithms and visualize the control results in dashboards. The overall workflows are separated into several stages with different tools and software to support them. The daylight and lighting simulation runs in Grasshopper with Ladybug and Honeybee plugins, and the Rhino geometry is read and input with Honeybee components to be processed as a daylight and lighting simulation model. Then, the illuminance data is processed in Visual Studio to run the proposed control algorithms. Finally, the control results and false-color maps are visualized in Microsoft Power Case Study # Analysis Suface Area (sq m) Number of Luminaires (variables ) Number of Sensor Points (constraints) Algorithm Running Time (include swicth on/off and dimming for entire year - 8760 hours) 1 143 12 136 0.48 hrs 2 460 44 535 5.42 hrs 323 BI. However, it is possible to package these programs to a single platform to create a user- friendly interface and layout all the inputs line by line. Since Ladybug and Honeybee are open- source, the source code can be acquired. Still, the input building model format keeps Rhino file format (.3dm). The Power BI data visualization engine can be embedded into the packaged platform. Finally, users can import the Rhino models, IES files, EPW weather files, lighting plans to the packaged new platform and defines the sensor grid's size, analysis surface, height, and direction of the sensor points. Then, the control results can be visualized inside this new packaged platform. Currently, there are several plugins and methods to visualize the building model and high-resolution images in Microsoft Power BI. The proposed workflow can visualize the false- color maps in image format when changing the different times and the date. However, the 3D geometry of the building in Power BI cannot be visualized parametrically till now. In the future, 3D viewer plugins may be created to visualize the 3D geometry of the building parametrically; when the variables change in Power BI, the 3D geometry can be changed and visualized. 7.3.2. Future Field-Test The daylight illuminance data was acquired from daylight simulation. The climate-based sky file and CIE sky files were processed from the EPW weather file in Honeybee plugins; these files were applied for the simulations. So, the proposed lighting controls algorithms were run with these simulated data to pre-calculate the lighting pattern for the luminaires. 324 The future work may apply current workflow from the methodology to test the feasibility of the proposed switch on/off and dimming control algorithms with an open-loop daylight-linked lighting control system (Fig. 7.9) Figure 7. 9 The proposed workflow to conduct the filed-test for the proposed control algorithm in future work. First, the test room will be selected in a real building; all these parameters such as building geometry, the optical properties, simulations parameters, and IES file for the luminaires in the test room will be modeled and set within the control algorithms. Second, the sky meter will be placed on the roof to measure the global illuminance value with a 15-minutes interval. This data will then be processed and inputted dynamically into the proposed control algorithms. 325 Third, with each 15 minutes interval, the proposed lighting control algorithms will run, and the data of control results data will be processed as a control signal and sent to the controller. The controller will switch on/off or dim the luminaires based on the control signal. Fourth, the test room will be put lux meters, the number of lux meters, the height, and the direction should be the same with the simulation model. Then, at each 15 minutes interval, the illuminance from lux meters will be recorded. Fifth, the results will be evaluated and compared with the threshold level and the simulated results. In addition, the post-occupancy survey would be beneficial to evaluate the satisfaction from the occupant about the visual comfort with the implementation of the lighting controls. Sixth, collecting the data of the whole year control results from lux meters with 15 minutes intervals and each luminaire’s switch on/off or dimming status would be beneficial. This dataset could be further applied to integrate the Machine Learning Algorithm (MLA) for the lighting controls. The evaluation will facilitate further optimization for the proposed lighting control algorithm and the commissioning for the control system (Fig. 7.10). 326 Figure 7. 10 The future work for integrating lighting control algorithms into Building Automation System (BAS). Finally, the proposed lighting control algorithms have the potential to integrate into Building Automation Systems (BAS). In addition, the sensor data, the real-time lighting electricity usage, and lighting status will be connected to the BAS dashboards control system to visualize the real-time data to facilitate facility management, operation, and maintenance (Fig. 7.11) (Fig. 7.12). Theoretically, the data could also be integrated with the building information model (BIM) earlier in the building’s design and construction and digital twin during operation and maintenance phases of the building’s lifetime. 327 Figure 7. 11 Lighting Control Dashboard (source: https://www.hvac-inc.com/commercial/building-automation/) Figure 7. 12 Neuron digital platform (source: https://www.arup.com/expertise/services/digital/arup-neuron) 328 7.4. Conclusion The main purpose of daylight-linked lighting control is to maximize the use of daylighting while minimizing the lighting consumption from electrical lightings. The integration of daylight and lighting illuminance can construct a simulated-based daylight-linked switch on/off and dimming control algorithm. The illuminance data was acquired from daylighting and lighting simulation. With the use of linear matrix programming, each hourly luminaire's switch on/off and dimming status can be calculated. The implementation of the proposed lighting control algorithm can facilitate sustainable design and leverage building lighting energy consumption. In addition, the control results can be visualized through data visualization dashboards to provide the owners, building energy management operators, and lighting designers the insight about proposed lighting control for decision making. The proposed lighting control algorithm has the potential to implement into energy simulation software in the lighting control part. According to California's Title 24, Part 6, the Building Energy Efficiency Standards (Energy Standards) mandate the installation of lighting controls for both residential and non-residential buildings (Title 24, Part 6 2019). Also, both ASHRAE 90.1 and IECC provide similar regulations for implementing lighting controls. 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Overall Grasshopper Workflow A.1.1.The overall Grasshopper flowchart (separated into three legible images) 345 346 347 348 A.1.2.Color-coded by different groups (Ladybug, Honeybee, TT Toolbox plugins, Python, and C# scripts 349 A.1.3.Customized component highlighted and numbered in red 350 A.2. Customized Grasshopper Component A.2.1. Climate Based Daylight Analysis Time1 Input import rhinoscriptsyntax as rs #input_Months # Analysis Month Period at one certain Day (From Start Month to End Month) During_Month = int(End_Month) - int(Start_Month) + 1 s = int(Start_Month) e = int(End_Month) Output_Month = [] while s<e+1: l = [int(s)]*24 s +=1 for i in l: Output_Month.append(i) if s == e+1: break Output_Day = [int(input_Day)]* During_Month * 24 Output_Hour = [i for i in range(0,24)] * During_Month print (Output_Month) print(Output_Day) print(Output_Hour) A.2.2. Climate Based Daylight Analysis Time2 Intput import rhinoscriptsyntax as rs #input_Months # Analysis Typical Month (s) at one certain Day (From Start Month to End Month) Output_Month = [int(Typical_Month_s)] * 24 Output_Day = [int(input_Day)] * 24 Output_Hour = [i for i in range(0,24)] print (Output_Month) 351 print(Output_Day) print(Output_Hour) A.2.3. Climate Based Daylight Analysis Time (Whole Year) Intput #input_Months #Analysis the Whole Year Hours #Output_Month = [int(Month_s) for i in ] * 24 M_JAN = [1] * 31 *24 M_FEB = [2] * 28 *24 M_MAR = [3] * 31 *24 M_APR = [4] * 30 *24 M_MAY = [5] * 31 *24 M_JUN = [6] * 30 *24 M_JUL = [7] * 31 *24 M_AUG = [8] * 31 *24 M_SEP = [9] * 30 *24 M_OCT = [10] * 31 *24 M_NOV = [11] * 30 *24 M_DEC = [12] * 31 *24 Month_s = M_JAN+M_FEB+M_MAR+M_APR+M_MAY+M_JUN+M_JUL+M_AUG+M_SEP+M_OCT+M_NOV+M_DEC D31 = range(1,32) D30 = range(1,31) D28 = range(1,29) D_JAN = [i for i in D31 for _ in range(24)] D_FEB = [i for i in D28 for _ in range(24)] D_MAR = [i for i in D31 for _ in range(24)] D_APR = [i for i in D30 for _ in range(24)] D_MAY = [i for i in D31 for _ in range(24)] D_JUN = [i for i in D30 for _ in range(24)] D_JUL = [i for i in D31 for _ in range(24)] D_AUG = [i for i in D31 for _ in range(24)] D_SEP = [i for i in D30 for _ in range(24)] D_OCT = [i for i in D31 for _ in range(24)] D_NOV = [i for i in D30 for _ in range(24)] D_DEC = [i for i in D31 for _ in range(24)] Day_s = D_JAN+D_FEB+D_MAR+D_APR+D_MAY+D_JUN+D_JUL+D_AUG+D_SEP+D_OCT+D_NOV+D_DEC H_JAN = ([24] + range(1, 24)) * 31 H_FEB = ([24] + range(1, 24)) * 28 H_MAR = ([24] + range(1, 24)) * 31 H_APR = ([24] + range(1, 24)) * 30 H_MAY = ([24] + range(1, 24)) * 31 H_JUN = ([24] + range(1, 24)) * 30 H_JUL = ([24] + range(1, 24)) * 31 352 H_AUG = ([24] + range(1, 24)) * 31 H_SEP = ([24] + range(1, 24)) * 30 H_OCT = ([24] + range(1, 24)) * 31 H_NOV = ([24] + range(1, 24)) * 30 H_DEC = ([24] + range(1, 24)) * 31 Hour_s = H_JAN+H_FEB+H_MAR+H_APR+H_MAY+H_JUN+H_JUL+H_AUG+H_SEP+H_OCT+H_NOV+H_DEC A.2.4. Climate Based Daylight Analysis Time Output import rhinoscriptsyntax as rs import re text = radiationValues #print (re.findall(r"SearchWords: \d+\.?\d*",text)) Rad= re.findall(r"(?<=| Diffuse: )\d+\.?\d*",text) Rad_f = [float(i) for i in Rad] indices =[1] Rad_Diffuse = Rad_f[1] #print(Rad_Diffuse) if Rad_Diffuse > 0: month = int(in_month) day=int(in_day) hour=int(in_hour) print(month) print(day) print(hour) A.2.5. Daytime Hourly Filter if (M is None) and (D is None) and (H is None): pass else: Out_M = M Out_D = D Out_H = H A.2.6. Null Value Filter 353 if in_results is None: results = [0] * int (float(Total_Sensor_Points)) elif in_results is not None: results = in_results A.2.7. Light Number String Generator import rhinoscriptsyntax as rs Total_Lights_List=[int(Total_lights_num)]*int(Total_Analysis_Hours) NumberL = [i for i in range(1,int(Total_lights_num)+1)] L_Number_str = ["L" + str(i) for i in NumberL] L_Number_str_SW = ["S_L" + str(i) for i in NumberL] L_Number_str_DM = ["D_L" + str(i) for i in NumberL] A.2.8. Switch On/Off Control Calculator import rhinoscriptsyntax as rs import scriptcontext as sc import ghpythonremote scipy = sc.sticky['scipy'] np = sc.sticky['numpy'] optimize = sc.sticky['scipy.optimize'] pulp = sc.sticky['pulp'] pd = sc.sticky['pandas'] future_fstrings = sc.sticky['future_fstrings'] ############# # -*- coding: future_fstrings -*- m = pulp.LpProblem("0-1 decision problem", sense=pulp.LpMinimize) c=[1]*int(Total_lights_num) L_n= int(int(Total_lights_num)+1) l = [i for i in range(1,L_n)] a = list(map(float, Daylight)) 354 b= list(map(int,a)) D = [int(item) for item in b] L_indx =[] L_code = int(Threshold) for i in range(len(D)): Daylight[i] = L_code - D[i] dtD = [int(item) for item in Daylight] data=pd.read_csv('Lighting_Data.csv',header=None) A=data.values.tolist() x = [pulp.LpVariable('x{}'.format(i), cat='Binary') for i in l ] m += pulp.lpDot(c, x) for i in range(len(A)): m += (pulp.lpDot(A[i], x) >= dtD[i]) m.solve() #print(m.name) #print("Status: You can:", pulp.LpStatus[m.status]) num = [] for v in m.variables(): #print(v.name, "=", v.varValue) L_indx.append(v.name) num.append(v.varValue) #on_off=["on" if float(i)==1.0 else "off" for i in num] Ls_On = sum(num) Ls_Off = int(Total_lights_num) - Ls_On L_fliter = [s.replace("x", "") for s in L_indx] New_L_fliter = [(int(i)-1) for i in L_fliter] ON_OFF = [0] * len(num) for i in range(len(num)): ON_OFF[New_L_fliter [i]] = num[i] A.2.9. Dimming Control Calculator import rhinoscriptsyntax as rs import scriptcontext as sc import ghpythonremote import re scipy = sc.sticky['scipy'] np = sc.sticky['numpy'] optimize = sc.sticky['scipy.optimize'] pulp = sc.sticky['pulp'] pd = sc.sticky['pandas'] future_fstrings = sc.sticky['future_fstrings'] # -*- coding: future_fstrings -*- j = pulp.LpProblem("0-1 decision problem", sense=pulp.LpMinimize) c=[1]*int(Total_lights_num) L_n= int(int(Total_lights_num)+1) l = [i for i in range(1,L_n)] a = list(map(float, Daylight)) b= list(map(int,a)) D = [int(item) for item in b] L_code = int(Threshold) 355 for i in range(len(D)): Daylight[i] = L_code - D[i] #d = [300-c for c in Daylight] dtD = [int(item) for item in Daylight] data=pd.read_csv('Lighting_Data.csv',header=None) A=data.values.tolist() y = [pulp.LpVariable('y{}'.format(i), cat='Continuous') for i in l ] j += pulp.lpDot(c, y) for i in range(len(A)): j += (pulp.lpDot(A[i], y) >= dtD[i]) for i in range(len(y)): j += y[i] >= 0 j += y[i] <= 1 j.solve() #print(j.name) #print("Status: You can:", pulp.LpStatus[j.status]) nume = [] Dimming_Va = [] L_OutVa = [] L_indxy = [] for v in j.variables(): #print(v.name, "=", v.varValue) L_Output = "%.2f"% float(v.varValue) #L_Dimming = (1- float(v.varValue)) #L_Dimming2f = "%.2f"% L_Dimming L_indxy.append(v.name) nume.append(v.varValue) #Dimming_Va.append(L_Dimming2f) L_OutVa.append(L_Output) L_fliter = [s.replace("y", "") for s in L_indxy] New_L_fliter = [(int(i)-1) for i in L_fliter] #Dimming_Value = [0] * len(Dimming_Va) #for i in range(len(Dimming_Va)): # Dimming_Value[New_L_fliter [i]] = Dimming_Va[i] L_OutValue = [0] * len(L_OutVa) for i in range(len(L_OutVa)): L_OutValue[New_L_fliter [i]] = L_OutVa[i] A.2.10. Lighting Controls Electricity Calculator import rhinoscriptsyntax as rs SW_Power_Usage = Ls_On * float(Input_Watts) / 1000 MassValue = 0 PartialValues = [] L_Out = [float(i) for i in L_OutValue] for v in L_Out: MassValue += v Addition = float(MassValue) DM_Power_Usage = Addition * float(Input_Watts) / 1000 A.2.11. luminaire Input Watt & Lumens Per Watt Miner 356 import rhinoscriptsyntax as rs import re text = luminaireDetails #print (re.findall(r"Input Watts: \d+\.?\d*",text)) Watt= re.findall(r"(?<=Input Watts: )\d+\.?\d*",text) Input_W = [float(i) for i in Watt] for Input_Watt in Input_W: print Input_Watt L_n= re.findall(r"(?<=Number of Lamps: )\d+\.?\d*",text) L_num = [int(i) for i in L_n] for L_number in L_num: print L_number Lumen_lamp = re.findall(r"(?<=Lumens per lamp: )\d+\.?\d*",text) Lumens_Per_l = [float(i) for i in Lumen_lamp] for Lumens_Per_lamp in Lumens_Per_l: print Lumens_Per_lamp #L_number = int(L_n) Input_Watts = L_number * Input_Watt Lumens_Per_Watt = Lumens_Per_lamp / Input_Watts A.2.12. Timeline String Generator #input_Months #Output_Month = [int(Month_s) for i in ] * 24 M_JAN = [1] * 31 *24 M_FEB = [2] * 28 *24 M_MAR = [3] * 31 *24 M_APR = [4] * 30 *24 M_MAY = [5] * 31 *24 M_JUN = [6] * 30 *24 M_JUL = [7] * 31 *24 M_AUG = [8] * 31 *24 M_SEP = [9] * 30 *24 M_OCT = [10] * 31 *24 M_NOV = [11] * 30 *24 M_DEC = [12] * 31 *24 Month_s = M_JAN+M_FEB+M_MAR+M_APR+M_MAY+M_JUN+M_JUL+M_AUG+M_SEP+M_OCT+M_NOV+M_DEC D31 = range(1,32) D30 = range(1,31) D28 = range(1,29) D_JAN = [i for i in D31 for _ in range(24)] D_FEB = [i for i in D28 for _ in range(24)] D_MAR = [i for i in D31 for _ in range(24)] D_APR = [i for i in D30 for _ in range(24)] 357 D_MAY = [i for i in D31 for _ in range(24)] D_JUN = [i for i in D30 for _ in range(24)] D_JUL = [i for i in D31 for _ in range(24)] D_AUG = [i for i in D31 for _ in range(24)] D_SEP = [i for i in D30 for _ in range(24)] D_OCT = [i for i in D31 for _ in range(24)] D_NOV = [i for i in D30 for _ in range(24)] D_DEC = [i for i in D31 for _ in range(24)] Day_s = D_JAN+D_FEB+D_MAR+D_APR+D_MAY+D_JUN+D_JUL+D_AUG+D_SEP+D_OCT+D_NOV+D_DEC H_JAN = range(0, 24) * 31 H_FEB = range(0, 24) * 28 H_MAR = range(0, 24) * 31 H_APR = range(0, 24) * 30 H_MAY = range(0, 24) * 31 H_JUN = range(0, 24) * 30 H_JUL = range(0, 24) * 31 H_AUG = range(0, 24) * 31 H_SEP = range(0, 24)* 30 H_OCT = range(0, 24) * 31 H_NOV = range(0, 24) * 30 H_DEC = range(0, 24) * 31 Hour_s = H_JAN+H_FEB+H_MAR+H_APR+H_MAY+H_JUN+H_JUL+H_AUG+H_SEP+H_OCT+H_NOV+H_DEC print(str(Month_s)+"/"+str(Day_s)+" "+str(Hour_s)+":00") A.2.13. CSV Writer using System; using System.Collections; using System.Collections.Generic; using Rhino; using Rhino.Geometry; using Grasshopper; using Grasshopper.Kernel; using Grasshopper.Kernel.Data; using Grasshopper.Kernel.Types; using System.IO; using System.Linq; using System.Data; using System.Drawing; using System.Reflection; using System.Windows.Forms; using System.Xml; using System.Xml.Linq; using System.Runtime.InteropServices; using Rhino.DocObjects; 358 using Rhino.Collections; using GH_IO; using GH_IO.Serialization; /// This class will be instantiated on demand by the Script component. public class Script_Instance : GH_ScriptInstance { #region Utility functions /// Print a String to the [Out] Parameter of the Script component. /// String to print. private void Print(string text) { /* Implementation hidden. */ } /// Print a formatted String to the [Out] Parameter of the Script component. /// String format. /// Formatting parameters. private void Print(string format, params object[] args) { /* Implementation hidden. */ } /// Print useful information about an object instance to the [Out] Parameter of the Script component. /// Object instance to parse. private void Reflect(object obj) { /* Implementation hidden. */ } /// Print the signatures of all the overloads of a specific method to the [Out] Parameter of the Script component. /// Object instance to parse. private void Reflect(object obj, string method_name) { /* Implementation hidden. */ } #endregion #region Members /// Gets the current Rhino document. private readonly RhinoDoc RhinoDocument; /// Gets the Grasshopper document that owns this script. private readonly GH_Document GrasshopperDocument; /// Gets the Grasshopper script component that owns this script. private readonly IGH_Component Component; /// /// Gets the current iteration count. The first call to RunScript() is associated with Iteration==0. /// Any subsequent call within the same solution will increment the Iteration count. private readonly int Iteration; #endregion /// This procedure contains the user code. Input parameters are provided as regular arguments, /// Output parameters as ref arguments. You don't have to assign output parameters, /// they will have a default value. /// private void RunScript(string Path, DataTree DataSet, bool Run, ref object A) { if (Run) { List<List<string>> MyData = new List<List<string>>(); for (int i = 0; i < DataSet.Branches.Count; i++) { List<string> tempList = new List<string>(); foreach (var item in DataSet.Branches[i]) { tempList.Add(item.ToString()); } MyData.Add(tempList); } 359 if (MyData != null && MyData.Count > 0 && Path != String.Empty) { using (var writer = new CsvFileWriter(Path + ".csv")) { // Write each row of data foreach (var myList in MyData) { writer.WriteRow(myList); } } } } } // <Custom additional code> /// Determines how empty lines are interpreted when reading CSV files. /// These values do not affect empty lines that occur within quoted fields /// or empty lines that appear at the end of the input file. public enum EmptyLineBehavior { /// Empty lines are interpreted as a line with zero columns. NoColumns, /// Empty lines are interpreted as a line with a single empty column. EmptyColumn, /// Empty lines are skipped over as though they did not exist. Ignore, /// An empty line is interpreted as the end of the input file. EndOfFile, } /// Common base class for CSV reader and writer classes. public abstract class CsvFileCommon { /// These are special characters in CSV files. If a column contains any /// of these characters, the entire column is wrapped in double quotes. protected char[] SpecialChars = new char[] { ',', '"', '\r', '\n' }; // Indexes into SpecialChars for characters with specific meaning private const int DelimiterIndex = 0; private const int QuoteIndex = 1; /// Gets/sets the character used for column delimiters. public char Delimiter { get { return SpecialChars[DelimiterIndex]; } set { SpecialChars[DelimiterIndex] = value; } } /// Gets/sets the character used for column quotes. public char Quote { get { return SpecialChars[QuoteIndex]; } set { SpecialChars[QuoteIndex] = value; } } } /// Class for writing to comma-separated-value (CSV) files. public class CsvFileWriter : CsvFileCommon, IDisposable { // Private members private StreamWriter Writer; private string OneQuote = null; private string TwoQuotes = null; 360 private string QuotedFormat = null; /// Initializes a new instance of the CsvFileWriter class for the /// specified stream. /// The stream to write to public CsvFileWriter(Stream stream) { Writer = new StreamWriter(stream); } /// Initializes a new instance of the CsvFileWriter class for the /// specified file path. /// The name of the CSV file to write to public CsvFileWriter(string path) { Writer = new StreamWriter(path); } /// Writes a row of columns to the current CSV file. /// The list of columns to write public void WriteRow(List<string> columns) { // Verify required argument if (columns == null) throw new ArgumentNullException("columns"); // Ensure we're using current quote character if (OneQuote == null || OneQuote[0] != Quote) { OneQuote = String.Format("{0}", Quote); TwoQuotes = String.Format("{0}{0}", Quote); QuotedFormat = String.Format("{0}{{0}}{0}", Quote); } // Write each column for (int i = 0; i < columns.Count; i++) { // Add delimiter if this isn't the first column if (i > 0) Writer.Write(Delimiter); // Write this column if (columns[i].IndexOfAny(SpecialChars) == -1) Writer.Write(columns[i]); else Writer.Write(QuotedFormat, columns[i].Replace(OneQuote, TwoQuotes)); } Writer.WriteLine(); } // Propagate Dispose to StreamWriter public void Dispose() { Writer.Dispose(); } } // </Custom additional code> } A.2.14. Whole Year Hourly Average illuminance (luminaire+daylight) Calculator 361 import rhinoscriptsyntax as rs from itertools import islice import scriptcontext as sc import ghpythonremote np = sc.sticky['numpy'] numpy = sc.sticky['numpy'] y = int(Total_Analysis_Hours) Ln = int(Lights_num) L_SW = [float(i) for i in Recorded_SW_ON_OFF] length_to_split = [Ln]* int(y) Input1 = iter(L_SW) Output1 = [list(islice(Input1, elem)) for elem in length_to_split] sumindex1 = numpy.array(Output1).sum(axis=0).tolist() Y_Aveg_SW = [i/int(y) for i in sumindex1] L_DM = [float(i) for i in Recorded_DM_L_Output] Input2 = iter(L_DM) Output2 = [list(islice(Input2, elem)) for elem in length_to_split] sumindex2 = numpy.array(Output2).sum(axis=0).tolist() Y_Aveg_DM = [i/int(y) for i in sumindex2] Sensors = int(Total_Sensors_Points) Lux_SW = [float(i) for i in Recorded_SW_illuminance_and_Daylit] length_to_split_sensors = [Sensors]* int(y) Input11 = iter(Lux_SW) Output11 = [list(islice(Input11, elem)) for elem in length_to_split_sensors] sumindex11 = numpy.array(Output11).sum(axis=0).tolist() Year_Ave_illuminance_SW = [i/int(y) for i in sumindex11] Lux_DM = [float(i) for i in Recorded_DM_illuminance_and_Daylit] Input22 = iter(Lux_DM) Output22 = [list(islice(Input22, elem)) for elem in length_to_split_sensors] sumindex22 = numpy.array(Output22).sum(axis=0).tolist() Year_Ave_illuminance_DM = [i/int(y) for i in sumindex22] A.2.15. Switch& Dimming Control Title Generator 362 # For Switch On/Off Control False-color map T= Sky_Condition + " | Time: " + Point_in_Time +":00 "+ Control_Type_Title + "\n Luminaires + Daylighting Satisfy (Threshold - " + Threshold +" Lux): "+ '%.2f'% (float(Switch) *100) +"%" # For Dimming Control False-color map T= Sky_Condition + " | Time: " + Point_in_Time +":00 "+ Control_Type_Title + "\n Luminaires + Daylighting Satisfy (Threshold - " + Threshold +" Lux): "+ '%.2f'% (float(Dim) *100) +"%" A.2.16. Links String Generator From AWS S3 import rhinoscriptsyntax as rs h = int(Hours) lst = list(range(0,h)) list_links = [links]* h #print(lst) #number = 1 #print("%05d") % number list = [] for i in lst: L= "{:05n}".format(i) list.append(L) #a = list new_string = [] for string in list_links: new_string = [string.replace("00000", i) for i in list] # new_strings.append(new_string) All_Links = new_string print (All_Links) 363 A.3. Lighting Control Algorithms – Switching On/Off and Dimming Control import sys import math import numpy as np from scipy import optimize as op from scipy.optimize import linprog import pulp import pandas as pd import xlsxwriter from openpyxl.workbook import Workbook from openpyxl import load_workbook ### **** UNCAST EACH OF CASE STUDY INPPUT BEFORE EACH TIME RUN! ### Inputs (Case Study 1) ##Total_lights_num = int(input("How many Lights?")) #Total_lights_num = 12 ##Threshold = int(input("Put the Lux Requirement here")) #Threshold = 250 ## Input Watt of Lights (From IES files) #Input_Watts = 60 ### Inputs (Case Study 2) ##Total_lights_num = int(input("How many Lights?")) #Total_lights_num = 44 ##Threshold = int(input("Put the Lux Requirement here")) #Threshold = 500 ## Input Watt of Lights (From IES files) #Input_Watts = 80 # Determining the number of variables by total number of luminaires c=[1]*int(Total_lights_num) L_n= int(int(Total_lights_num)+1) l = [i for i in range(1,L_n)] # L_Code is the recommendation illuminance level L_code = int(Threshold) #Importing Lighting Data as csv file format, each matrix run with daylighting data L_data=pd.read_csv('Lighting_Data.csv',header=None) A=L_data.values.tolist() ### Importing Daylighting Data as csv file format, each Column as an array run once in the loop **** UNCAST EACH OF FILE BEFORE EACH TIME RUN! ## 1.Climate Based Sky file, when run climate-based sky condition, use below code #D_data = pd.read_csv('EntireYearDaylight_Climate_Based_Sky.csv',header=None) ## 2.CIE_Sunny_With_Sun file, when run CIE sunny with sun sky condition, use below code #D_data = pd.read_csv('EntireYearDaylight_CIE_Sunny_With_Sun.csv',header=None) ## 3.CIE_Intermediate_with_Sun file, when run CIE intermediate with sun sky condition, use below code #D_data = pd.read_csv('EntireYearDaylight_Intermediate_with_Sun.csv',header=None) ## 4.CIE_Cloudy_Sky file, when run CIE cloudy sky condition, use below code #D_data = pd.read_csv('EntireYearDaylight_CIE_Cloudy_Sky.csv',header=None) D_data = D_data.T DL = D_data.values.tolist() ## Preparing for writting data and exporting ALL_Data = [] 364 ALL_DataToWrite = [] ALL =[] # For visual illumiance vaule of luminaires after two control algorithm # First import the original Lighting Data Lux_data=pd.read_csv('Lighting_DataT.csv',header=None) Lux_A = Lux_data.values.tolist() Lux_A = np.array(Lux_A) ALL_sw_dm_Lux = [] ALL_sw_dm_Lux_toExcel = [] # Swicth On/Off Control Algorithm. Using Pulp package for i in range(len(DL)): m = pulp.LpProblem("0-1 decision problem", sense=pulp.LpMinimize) Daylight = DL[i] for i in range(len(Daylight)): Daylight[i] = L_code -Daylight[i] dtD = [ float(item) for item in Daylight] # Defining the Domain of Variables. Binary is 0 or 1 x = [pulp.LpVariable('x{}'.format(i), cat='Binary') for i in l ] # Linear Programming of Matrix Operation m += pulp.lpDot(c, x) for i in range(len(A)): m += (pulp.lpDot(A[i], x) >= dtD[i]) m.solve() #print(m.name) #print("Status: You can:", pulp.LpStatus[m.status]) L_indx =[] num1 = [] for v in m.variables(): print(v.name, "=", v.varValue) L_indx.append(v.name) num1.append(v.varValue) num = [1 if i> 1 else i for i in num1] #print("Max f(x) =", pulp.value(m.objective)) #on_off=["on" if float(i)==1.0 else "off" for i in num] Ls_On = sum(num) Ls_Off = int(Total_lights_num) - Ls_On # Calculating the hourly electricity usage of each luminaire in Switch On/Off control SW_Power_Usage = Input_Watts * int(Ls_On) / 1000 L_fliter = [s.replace("x", "") for s in L_indx] New_L_fliter = [(int(i)-1) for i in L_fliter] ON_OFF = [0] * len(num) # To corresponding each luminaire for their sequence for i in range(len(num)): ON_OFF[New_L_fliter [i]] = num[i] TF = [i for i in ON_OFF] # For calculating illumiance vaule of luminaires after switch on/off control algorithm # Geting illumiance vaule of luminaires after switch on/off control Lsw = [] # xsw is the results of hourly switch on/off status of luminaires xsw = np.array(TF) # Calculating how much illumiance can provide after swicth on/off controls bsw = np.matmul(Lux_A,xsw) bsw = bsw.tolist() for i in bsw: Lsw.append(i) 365 Lux_Vaule_swf = ['%.2f' % float(i) for i in Lsw] Lux_Vaule_sw = [float(i) for i in Lux_Vaule_swf] ON_OFF.append(Ls_On) ON_OFF.append(Total_lights_num) ON_OFF.append(SW_Power_Usage) ## Dimming Control Algorithm. Using Pulp package j = pulp.LpProblem("0-1 decision problem", sense=pulp.LpMinimize) y = [pulp.LpVariable('y{}'.format(i), cat='Continuous') for i in l ] # Linear Programming of Matrix Operation j += pulp.lpDot(c, y) for i in range(len(A)): j += (pulp.lpDot(A[i], y) >= dtD[i]) # Defining the Domain of Variables (0 to 1) for i in range(len(y)): j += y[i] >= 0 j += y[i] <= 1 j.solve() #print(j.name) #print("Status: You can:", pulp.LpStatus[j.status]) nume = [] Dimming_Va = [] L_OutVa1 = [] L_indxy = [] for v in j.variables(): print(v.name, "=", v.varValue) L_Output = v.varValue L_indxy.append(v.name) nume.append(v.varValue) L_OutVa1.append(L_Output) L_OutVa = [1 if i> 1 else i for i in L_OutVa1] L_fliter = [s.replace("y", "") for s in L_indxy] New_L_fliter = [(int(i)-1) for i in L_fliter] L_OutValue = [0] * len(L_OutVa) # Corresponding each luminaire for their sequence for i in range(len(L_OutVa)): L_OutValue[New_L_fliter [i]] = L_OutVa[i] L_Opening = ['%.8f' % float(i) for i in L_OutValue] L_open_cal = [ float(i) for i in L_Opening] L_OutSum = sum(L_open_cal) L_OutPutValue = ['%.3f' % float(i) for i in L_OutValue] # Calculating the hourly electricity usage of each luminaire in Dimming control DM_Power_Usage = '%.4f' % float (Input_Watts * L_OutSum / 1000) # For calculating illumiance vaule of luminaires after dimming control algorithm # Geting illumiance vaule of luminaires after dimming control Ldm = [] # xdm is the results of hourly light output (dimming) status of luminaires xdm = np.array(L_open_cal) # Calculating how much illumiance can provide after dimming controls bdm = np.matmul(Lux_A,xdm) bdm = bdm.tolist() for i in bdm: Ldm.append(i) Lux_Vaule_dmf = ['%.2f' % float(i) for i in Ldm] Lux_Vaule_dm = [float (i) for i in Lux_Vaule_dmf] # Adding all the results data from lighting controls L_OutPutValue.append(DM_Power_Usage) 366 ALL_Data = ON_OFF + L_OutPutValue ALL_DataToWrite.append(ALL_Data) #print(L_OutValue) # Add switch on/off and dimming control luminaires' illumiance vaule ALL_sw_dm_Lux = Lux_Vaule_sw + Lux_Vaule_dm #ALL_sw_dm_Lux = Lsw + Ldm ALL_sw_dm_Lux_toExcel.append(ALL_sw_dm_Lux) ## Write Switch On/Off results to Excel # Adding the Headers NumberL = [i for i in range(1,int(Total_lights_num)+1)] L_Number_str = ["L" + str(i) for i in NumberL] SWtitle = ["S_L" + str(i) for i in NumberL] + ["Lights On", "Total Lights", "SW Electricity Usage (KWh)"] DMtitle = ["D_L" + str(i) for i in NumberL] + ["DM Electricity Usage (KWh)"] SWtitle_DMtitle = SWtitle + DMtitle ALL.append(SWtitle_DMtitle) ALL.extend(ALL_DataToWrite) #DM_ALL.append(DMtitle) #DM_ALL.extend(DM_Data) #print(SW_ALL) #print(DM_ALL) #print(ALL) #print(ALL_sw_dm_Lux_toExcel) # Creating Excel Sheet for Light status data workbook = xlsxwriter.Workbook('SW_DM.xlsx') worksheet1 = workbook.add_worksheet() # Creating Excel Sheet for illuminance data after lighting controls workbook_illuminance = xlsxwriter.Workbook('SW_DM_illuminance.xlsx') worksheet2 = workbook_illuminance.add_worksheet() # Writting Light status data to excel for row_num, row_data in enumerate(ALL): for col_num, col_data in enumerate(row_data): worksheet1.write(row_num, col_num, col_data) workbook.close() # Writting illuminance data after lighting controls to excel # Formatting and Structuring data for row_num, row_data in enumerate(ALL_sw_dm_Lux_toExcel): for col_num, col_data in enumerate(row_data): worksheet2.write(row_num, col_num, col_data) workbook_illuminance.close() Excel_To_csv = pd.read_excel ('SW_DM.xlsx') Excel_To_csv.to_csv ('SW_DM.csv' , index = None, header =True) # Adding the data to lighting control results pd.read_csv('SW_DM.csv', index_col=[0]) aa = pd.read_csv('SW_DM.csv') wb = load_workbook('AddTimelines.xlsx') ws = wb.active col_a = ws["A"] col_b = ws["B"] 367 col_c = ws["c"] Timeline = [cell.value for cell in col_a] Analysis_Area = [cell.value for cell in col_b] Sky_Condition = [cell.value for cell in col_c] aa.insert(0, column ="Timeline" , value = Timeline ) aa.insert(1, column ="Analysis_Area" , value = Analysis_Area ) aa.insert(2, column ="Sky_Condition" , value = Sky_Condition ) aa.head() aa.to_csv('SW_DM.csv', index = False) # Reading the csv file df_new = pd.read_csv('SW_DM.csv') # saving xlsx file and close Exce = pd.ExcelWriter('SW_DM.xlsx') df_new.to_excel(Exce, index = False) Exce.save()

Abstract (if available)

Abstract Electric lighting consumed approximately 8% of total electricity consumption in the US residential and commercial sectors combined in 2020, accounting for an estimated 6% of total US electricity consumption (EIA, 2021). Reducing electricity consumption while maintaining an acceptable illuminance level is critical to achieving environmental sustainability and developing net-zero energy buildings. Lighting control systems, which have become widely adopted in residential and commercial buildings, aim to adjust the electrical lighting levels depending on daylight penetration automatically. Automated lighting control strategies include daylight harvesting, time scheduling, occupancy sensing, institutional tuning, and others. These control strategies can substantially save lighting energy and cost for the owner. Two simulation-based lighting control algorithms (switch on/off and dimming control) have been developed to reduce lighting energy consumption and operational cost, integrating time scheduling and daylight harvesting strategies by implementing daylight and lighting simulations. The intent was to create two control algorithms for the luminaires that predetermined their hourly on/off and dimming status based on pre-calculated daylight and lighting illuminance data from simulations.
The proposed daylight-linked control algorithms were tested with two Rhino models for case studies. The luminaires and IES files were imported to the Rhino model for the lighting simulations. March 21st, June 21st, and December 21st with the climate-based sky file were first chosen as test dates to run the algorithm. The EPW weather file was acquired from the EnergyPlus website. Then, the annual hourly analysis was run with Python in Microsoft Visual Studio using four sky files (climate-based sky, CIE sunny with sun, CIE intermediate with sun, and CIE cloudy sky) to determine if having an actual sensor determining the overall sky condition could help in producing more accurate results. The Ladybug and Honeybee packages in Grasshopper were used to analyze the hourly illuminance from daylighting and luminaires. The illuminance data was structured with an array and matrix by applying Python scripts in Grasshopper with a linear programming mathematics method to calculate the maximum luminaire off and dimming levels while maintaining the desired illuminance level. The result shows that each luminaire's hourly on/off and dimming status and the reduced energy consumption can be theoretically calculated. The first case study showed that about 61% and 82% of the electricity could be reduced with switch on/off and dimming control algorithm compared with no lighting control mode when operation hours are from 8:00 to 20:00. The second case study showed that the electricity could be reduced by about 17% and 29% when operation hours are from 8:00 to 19:00.
The proposed lighting control algorithm can be run by integrating hourly daylight and lighting simulation illuminance data and theoretically applied for the open-loop lighting controls for achieving electric lighting energy savings. Finally, seven dashboards were created in Microsoft Power BI to interactively visualize the luminaries’ electricity usage, cost-saving, the lighting operation hours, and illuminance distribution false-color maps (luminaires + daylighting) under four sky conditions after implementing the proposed simulation-based switch on/off and dimming control algorithms to assist better decision-makings.

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Yang, Shaobo (author)

Core Title Simulation-based electric lighting control algorithm: integrating daylight simulation with daylight-linked lighting control

School School of Architecture

Degree Master of Building Science

Degree Program Building Science

Degree Conferral Date 2022-05

Publication Date 04/19/2022

Defense Date 03/09/2022

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag daylighting simulation,dimming control algorithm,ladybug and honeybee,lighting control algorithm,lighting simulation,OAI-PMH Harvest,switch on/off

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Kensek, Karen (committee chair), Konis, Kyle (committee member), Schiler, Marc (committee member)

Creator Email kevinyang1916@gmail.com,shaoboya@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC111018859

Unique identifier UC111018859

Document Type Thesis

Format application/pdf (imt)

Rights Yang, Shaobo

Type texts

Source 20220419-usctheses-batch-929 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu