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Environmental adaptive design: building performance analysis considering change

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Environmental adaptive design: building performance analysis considering change

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Content 1 ENVIRONMENTAL ADAPTIVE DESIGN Building performance analysis considering change By Yiyu Chen Thesis Chair: Karen Kensek Committee members: Joon-Ho Choi; Marc Schiler; Gerg Collins A Thesis Presented to the FACULTY OF THE SCHOOL OF ARCHITECTURE UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirement for the Degree MASTER OF BUILDING SCIENCE May 2015 Copyright 2015 Yiyu Chen 2 DEDICATION This thesis is dedicated to the Chase L. Leavitt Graduate Building Science Program. This program is where I developed my interest and improved my skill. It is the two years’ study in this program that significantly enlarged my knowledge base, allowing me to discover that my interest is sustainable design, and it helps me to deeply understand what career path that I would like to pursue after graduation. These two years in MBS program are one of the most valuable time so far in my life, I appreciate that I am a member of this group. 3 ABSTRACT Buildings account for up to 40% of the total energy use and 72% of the total electricity consumption and have a strong negative impact on climate change in the world (SBCI 2009). The change in climate will also affect how buildings perform; it will significantly influence the building’s behaviour in the long run both through changing the amount of energy consumption (perhaps drastically in certain climate zones) and by changing the thermal level of comfort that occupants’ experience. A building that meets the current energy consumption standard has the potential to become energy inefficient in the future, especially because the occupants’ comfort level is largely influenced by the outdoor environment if passive design strategies were originally used. Therefore, making a building that is adaptive enough for climate change is a critical issue that architects and engineers should take into consideration especially when designing a building that includes passive strategies. Two specific consequences of climate change’s influence on buildings are analysed under changing climate scenarios: the building lifetime energy use and the occupants’ thermal comfort. To estimate the building’s future performance, several projected future climate conditions are created (for low, moderate, and severe climate change) in three climate zones for three different time periods (2020s, 2050s and 2080s). These future weather files were calculated from a world climate change weather file generation tool that was developed at the University of Southampton. By analysing the case study building thermal performance for multiple climate change scenarios and in different climate zones, it is possible to inspect the resilience of design strategies that include Window-Wall-Ratio (WWR), solar heat gain co-efficient (SHGC) for glazing, natural ventilation, and shading for south facing windows etc., for the different case studies to discover which passive strategies might work best in the long term. A trade-off to be considered is the increase in energy use to compensate for the changes in the weather versus the lessening of the occupants’ thermal comfort when the passive strategies behave differently in the future. Adaptive comfort model and predicted percentage of comfortable hours during the occupied time in will also be used to evaluate the strategies. In addition, building life-cycle energy use will be simulated through energy modelling iteration based on different future climate conditions, a relationship model between weather variables with building life-cycle energy performance is developed and can be used to explore building energy use. 4 ACKNOWLEDGEMENTS I would first and foremost like to thank my committee members, Professor Karen Kensek, Professor Joon- Ho Cohi, Professor Marc Schiler and Greg Collins. It is because all of your help, I am able to complete my thesis on time. There has been many difficulties during this year, but with all your support and guidance, I am able to solve the challenges and continually making progress. I also would like to thank the Discipline Head for Building Science, Professor Douglas Noble who has been keep making effort to improve the MBS program and keep our team as a family. I would like to thank my MBS classmates, former and present, who has been stay with me during this year and continually give me support. Their strong academic skill and experience has been providing me a lot of help for my thesis and solved many problems that I was not able to overcome by myself. I would also like to give special thanks to my parents. With their strong support, I was able to join the MBS big family, meeting all my professor and friends here. 5 Contents DEDICATION .................................................................................................................................................. 2 ACKNOWLEDGEMENTS ................................................................................................................................. 4 ABSTRACT ...................................................................................................................................................... 3 Chapter 1. Introduction to climate change and building performance .................................................. 4 1.1 Importance: how climate change influences overall building performance ................................ 1 1.1.1 Observed evidence of climate change: past, current, and future ........................................ 1 1.1.2 Relationship between climate and building performance .................................................... 2 1.1.3 Research contribution ........................................................................................................... 5 1.2 Terminology .................................................................................................................................. 5 1.3 Study boundaries: Define the research scope .............................................................................. 7 1.3.1 Building energy use for climate change ................................................................................ 7 1.3.2 Thermal comfort analysis for climate change ....................................................................... 7 1.3.3 Geographic scope of research............................................................................................... 8 1.3.4 Passive strategies selection................................................................................................... 8 1.4 Deliverable: A process of designing climate adaptive buildings ................................................... 8 1.5 Hypothesis Statement ................................................................................................................... 9 1.5.1 Hypothesis statement ........................................................................................................... 9 1.5.2 Objectives .............................................................................................................................. 9 Chapter 2. Background for climate change, building energy modeling, and thermal analysis ............ 10 2.1 Climate change: past, current, and future .................................................................................. 10 2.1.1 Introduction ........................................................................................................................ 10 2.1.2 Observed phenomenon in the past .................................................................................... 10 2.1.3 Review of climate change scenarios based on HadCM3 model ......................................... 12 2.1.4 Current climate: climate zones in United States ................................................................. 13 2.1.5 Weather file and its role in energy simulation ................................................................... 15 2.2 Building energy simulation fundamentals .................................................................................. 18 2.2.1 Energy simulation and their application ............................................................................. 18 2.3 Thermal comfort analysis ............................................................................................................ 20 2.3.1 Introduction ........................................................................................................................ 20 2.3.2 Predicted Mean Vote (PMV) ............................................................................................... 21 2.3.3 Predicted percentage of dissatisfied (PPD) ......................................................................... 21 6 2.3.4 Adaptive comfort model ..................................................................................................... 22 2.3.5 Conclusion ........................................................................................................................... 23 Chapter 3. Method and progress of studying climate change’s influence on building performance .. 24 3.1 Introduction: development of main workflow ........................................................................... 24 3.2 Climate change scenarios projection: pattern-scaling from A1F1-medium high emission scenarios ................................................................................................................................................. 25 3.3 Sensitivity analysis: testing the influence of climate change ...................................................... 27 3.4 Future climate map generation for visualizing future climate zones ......................................... 29 3.4.1 What is a climate zone map ................................................................................................ 29 3.4.2 How does climate zone effect energy calculations ............................................................. 30 3.4.3 How to create the new climate map ......................................................................................... 31 3.4.3 Interpolating weather stations ........................................................................................... 32 3.5 Building energy use throughout its life cycle .............................................................................. 32 3.5.1 Selection and description of baseline model ...................................................................... 32 3.5.2 Scope of the study: selection of regions for analysis .......................................................... 34 3.6 Thermal comfort analysis ............................................................................................................ 37 3.6.1 Sensitivity test and selection of passive strategies ............................................................. 38 3.6.2 Calculation of numbers of hours in comfort zone for natural ventilated building ............. 38 3.7 Summary ................................................................................................................................. 38 Chapter 4. Future Climate Zone Map ................................................................................................... 39 4.1 New climate zone designations based on future weather files .................................................. 39 4.2 Creating the US map – future work ............................................................................................ 43 4.2.1 How to choose what weather file to use ............................................................................ 43 4.2.2 Important assumptions in the HadCM3 climate tool ......................................................... 43 4.2.3 Use of GIS for the final map ................................................................................................ 43 4.3 Conclusion ................................................................................................................................... 44 Chapter 5. Future Energy Use Based on Selected Variables................................................................. 45 5.1 Future Building energy consumption overview .......................................................................... 45 5.1.1 Case study of seven counties in California .......................................................................... 45 5.1.2 Future building energy use across the U.S by HadCM3 model ........................................... 47 5.1.3 Los Angeles, San Antonio, and Miami – CCEI (Climate change energy index) Calculation . 54 5.2 Solar heat gain coefficient (SHGC) .............................................................................................. 56 5.3 Overhang and its impact on building energy performance in the future ................................... 59 7 5.3.1 The selection of the optimal overhang depth for three locations ...................................... 59 5.3.2 The study of overhang depth from 0 ft to 10 ft under different climate change scenario 61 5.3.3 Future building energy use with the overhang ................................................................... 62 5.3.4 The impact of overhang on building energy consumption and its effectiveness in the future 63 5.3.5 Selection of optimal overhand depth to minimize the life cycle energy use ..................... 63 5.4 Conclusion ................................................................................................................................... 67 Chapter 6. Future Comfort and Energy Use Based on Ventilation Modes ........................................... 68 6.1 Natural ventilation: Two windows, same wall; cross ventilation; and stack ventilation ........... 68 6.1.1 Two Windows on the same side ......................................................................................... 68 6.1.2 Cross ventilation ................................................................................................................. 71 6.1.3 Stack ventilation .................................................................................................................. 73 6.2 Mixed-mode ventilation ............................................................................................................. 76 6.3 Mechanical ventilation ................................................................................................................ 77 6.4 Summary of the ventilation type’s impact on building energy use ............................................ 78 Chapter 7. Future Work and Conclusion .............................................................................................. 81 7.1 Test of more variables for energy consumption and thermal comfort ...................................... 81 7.2 Using different weather file to analyze the building future performance ................................. 82 7.3 7.3 Providing strategies for future climate zones ....................................................................... 82 7.4 Analyzing building life cycle cost considering climate change .................................................... 82 7.5 Conclusion ................................................................................................................................... 82 Bibliography ................................................................................................................................................ 84 Appendix A Percentage of energy use change in the future by cliamte zones .......................................... 86 1 Chapter 1. Introduction to climate change and building performance 1.1 Importance: how climate change influences overall building performance According to Intergovernmental Panel on Climate Change (IPCC), climate change is defined as A change in the state of the climate that can be identified (e.g. using statistical tests) by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer. It refers to any change in climate over time, whether due to natural variability or as a result of human activity. (Barker 2007) 1.1.1 Observed evidence of climate change: past, current, and future As is discussed in IPCC’s fourth assessment report of climate change, the world is currently experiencing a drastic climate change, evidence of which has been seen during the past century. A 0.15 F average surface temperature increase per decade has been documented from 1901 until 2014, and it is recorded that 2001-2010 was the warmest period of time in the last 200 years. In the United States, the average surface temperature increased by 0.14 F from 1901 to 2014, which means approximately 1.6 F increases of temperature happened during the last century (IPCC 2014). Apart from temperature, there are many more indicators that can identify the track of climate change. The amount of precipitation is another strong piece of evidence supporting this theory; an average rate of 0.2 percent increase of global precipitation per decade has been recorded during the period from 1901 to 2014. What is more significant is the increase of U.S. precipitation, which increased by an average rate of 0.5 percent per decade 1901 (IPCC 2014). In addition, satellite data also shows that the Arctic sea ice extent has decreased by 2.7 percent per decade, and a decline of seasonally frozen ground has observed (Barker 2007). During the past century, other observations have been witnessed: the size of glacial lakes have increased, some permafrost areas in mountain regions became even more unstable because of the increased temperature, and ecosystems in Antarctic and Arctic oceans are being significantly influenced by the changed weather(Barker 2007) conditions. Other observed effects of climate change include: an increase of heat wave, more inland flood, extensive species loss, the reduction of crop productivity in Europe, and death rate increases because of the associated floods and droughts. Although debates still continue about the rate and cause of climate change, it is clear that changes in the climate are occurring. This evidence indicates that climate change including global warming is occurring and if measures are not enacted will continue. An increase of temperature of about 2 to 11.5 F during the next century is projected. Specifically, according to the General Circulation Models (GCMs) that were developed in London Hadley Centre, the temperature increases from 2010 to 2039 will be ranging between 1 and 3 F(Hulme et al. 2002) . The EPA noted that that the temperature has increased by 1.5 degree during the last century, and it is projected to increase another 2 to 11.5 degree in the next century(Barker 2007) . Since the relatively small change of temperature during the past years, it is predictable that the influence is getting greater, the ecosystem, industry, human health and the whole society need to take action to mitigate or adapt to the upcoming challenge of climate change. Temperatures will increase in the future, rather than stay the same, so that it has a potential influence on building performance (Fig. 1.1). 2 Figure 1.1: increasing temperature as predicted, until 2100, there will be a big temperature increase compared with the current condition 1.1.2 Relationship between climate and building performance It is known that buildings are one of the largest sources of emissions, especially carbon dioxide, which is a contaminant that is believed to be a cause of global warming(Larson, Rajkovich, and Leighton 2011), other greenhouse gases include Methane, Nitrous Oxide and others . It is estimated that buildings are responsible for 40% of total energy consumption (SBCI 2009)and 72 % of the total electricity use in the world, which makes buildings become the main cause of world energy depletion. According IPCC fourth assessment report for climate change, in the entire year of 2004, the carbon dioxide emission produced from building reaches 8.6 million metric tons, which makes it very urgent to reduce the emission from the building sector, which is relatively easier compared with other sectors (SBCI 2009). Building performance relies on two main factors, the building physics and weather conditions. Building physics are the building information including the envelope type, occupancy type, building system information and others. Weather conditions can be represented in a TMY weather file. Either of these two factors will affect the performance analysis results (Fig. 1.2). Shows Figure 1.2. Variables related with the building physics and weather file 70 72 74 76 78 80 82 84 1901 2014 2100 Past, current and future for temperature Lowest temperature increase Highest temperature increase TMY weather file Building physics Temperature Envelope Humidity Occupants profile Solar Radiation Internal Load Wind System Precipitation … … 3 Predicting future building performance requires the future weather file, and assuming the building physics stay the same throughout its life, the influence of building physics alone can be tested and recommendations to avoid any negative effect can be provided. Weather files are used to provide the weather conditions for the location the building will be built. It is a necessary part for building performance analysis, since the building is environmentally related. By using the weather file, the temperature, humidity, wind, solar radiation and other climate factors can be derived. Climate change will, in turn, effect building performance in terms of energy performance and thermal performance. In addition, the effect is not only in energy and thermal performance, but also indoor air quality, daylighting performance, and so on. Some of this can be reflected by changing the weather file that will be used in the energy simulations software. A well designed passive strategy applied to a building will lessen building energy consumption and still keep a good indoor thermal condition. The selection of the strategy to use is very dependent on the local climate. For example, in hot and dry places, where the daytime is hot and the night cold, thermal mass could be a great idea to be implemented in the building since heat transfer process could be largely delayed, which provide a relatively more comfortable indoor environment and also save building energy use due to a lower peak cooling load (Talyor and Miner 2014). It provides an opportunity to achieve desirable load-levelling and peak-shifting behaviour with passive components which can be widely deployed in new and existing structures (Talyor and Miner 2014). With the consideration of global warming, a higher future average temperature is projected, which will result in a reduction of heating energy use during the winter, but a significant increase of cooling energy demand, the overall building energy performance will be completely different. It is predicted that the Cooling Degree Day (CDD) will increase by 30 to 60 percent in Chicago by 2070 (Perez 2009). Due to the increased average temperature, the monthly cooling load for a building is higher when compared with the cooling load calculated based on current weather conditions, which means there is a potential that the system does not have the capability to cover the heat gain from the outdoor environment in the future. Global warming will have an influence on building cooling and heating load (Fig. 1.3). The cooling load will be increased if the temperature keeps increasing in the future. However, the heating load is predicted to be lower since the higher temperature in the winter is beneficial to reduce heating energy use (Fig. 1.3 and 1.4). 4 Figure 1.3: Monthly cooling load for 2014 and 2080 Figure 1.4: monthly heating load for 2014 and 2080 Therefore, in order to keep the building performing as good as it is now, it is crucial to take the future climate change into consideration. The first step of building performance analysis is to acquire weather data that can most realistically reflect the local microclimate weather conditions (see chapter 2, section 2.1.5 for more details about weather files). Based on different local conditions, specific passive strategies could be applied in order to achieve 0 20 40 60 80 100 120 140 160 180 200 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Monthly Cooling Load 2014 2080 0 20 40 60 80 100 120 140 160 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Monthly Heating Load 2014 2080 5 a high performance building in terms of energy efficiency and thermally comfort. Therefore, if the weather conditions will be different in the future and one plans not to do major retrofitting, it is necessary to reconsider the design strategies (passive and active) and ensure that they not only meet code and perform well in the current situation, but also in the future. By using tools to predict future weather conditions, it is possible to generate future weather files for building performance analysis. Both building energy performance and thermal comfort analysis will be affected by the future weather condition. 1.1.3 Research contribution Sustainable design requires that architects and engineers fully understand the interaction between the climate and building performance and weather files and predicted building performance. However, it is easy for designers to overlook that the climate is a dynamic system that changes over time. More research in this field can provide designers valuable recommendations that they can use during the conceptual design stage and help the decision making process by taking the future climate conditions into consideration. This kind of more integrated design process can better ensure the building performance not only for current code compliance or client’s requirements but also for the future climate conditions. The results and improvement recommendations can also be taken as suggestions for the amendment of code or design guidelines including ASHRAE 90.1, LEED reference manual from USGBC, Title 24, Living Building Challenge, etc. Most design standards consider the climate conditions as a factor, but the impact of climate change is usually ignored. Therefore, once the suggestions to achieve a high performance building are investigated and quantified, they can be adopted into the code and design guidelines, from which the designers can reference and create buildings that are better for the changing environment. The research will also serve as a stepping stone and potential methodology for the future study of building performance analysis as more scientific conclusions about the probability of the details of climate change are made. 1.2 Terminology Building performance analysis is a process designers use to fully understand the outcome of their design decisions. Usually this analysis is influenced by a lot of factors, some of which are unpredictable. Weather conditions, completely uncontrollable by people, can have significant impact on a building’s performance. Therefore, the undergoing climate change has a potential impact on building’s energy performance. Even though the future climate models themselves will probably change as more information is learned, a methodology for studying variability and climate’s influence on building design can be determined. The study of climate change’s impact on building performance is necessary and can provide valuable recommendation to designers. Important terms are defined below.  Climate Change. Climate change refers to a significant change of weather properties during a long period of time, the variables that may change in the weather system include temperature, humidity, solar radiation, precipitation, etc. Regardless of the causes of climate change, the emphasis will be on the results in building performance that are caused by this phenomenon.  Emission Scenario. Emission scenarios that will be used in the analysis are from HadCM3 climate change model, which is associated with carbon dioxide and is a set of different patterns of climate change during the same period of time. The emission scenarios that will be used are the following: 6 low emission scenario (B1), medium emission scenario (B1), medium high scenario (A2) and high emission scenario (A1F1) (Barker 2007). All the scenarios are derived from the IPCC fourth report of climate change (Barker 2007) .  Energy Use Intensity (EUI). EUI is defined as the energy consumption per unit (e.g. square foot) per year in a project. For example, a typical value for EUI is expressed as following: 52 kBTU/sq.ft/year.  HadCM3. HadCM3 stands for “Hadley Centre Coupled Model, version 3,” which is a climate change model developed in Hadley Centre in United Kingdom (Hulme et al. 2002) . HadCM3 is a combination of atmosphere model and ocean model and has a higher resolution than the HadCM2 model, which is the previous version developed by IPCC in the past. The model is based on the physics describing the transport of mass (including moisture) and energy; these equations are solved at intervals (typically 30 minutes) at a number of points forming a grid over the globe of 2.5º in latitude by 3.75º in longitude (Hulme et al. 2002).  Life-Cycle Cost Analysis (LCCA). LCCA is a method of estimating the total cost of a project during its entire life-cycle. The factors it covers include initial cost, energy cost, maintenance, disposal, etc. The LLCA allows designers to take many factors into consideration and have a more accurate prediction of the total net expenses of the project (Han, Srebric, and Enache-Pommer 2014) especially to society as a whole.  Life-Cycle Energy Use. Life-cycle energy use is the total energy that will be consumed during a building’s entire life. This will be a method to evaluate climate change’s impact on building energy performance over time.  Passive Strategy. A passive strategy is one that uses the shape, materials and functions to responsive to local climate and microclimate in order for the building to function; no additional active systems are needed to provide power for the building (Rodriguez-Ubinas et al. 2014). In another word, passive strategy consumes no energy from any source. Passive strategies are complementary to active strategies such as regular building active strategies that includes air conditioning, heating, mechanical ventilation, artificial lighting, etc. Passive strategies heavily rely on the local environment, and their best use varies from one climate zone to another climate zone. Some examples of passive strategies are natural ventilation, passive solar heat gain, daylighting, and thermal mass.  Pattern-Scaling. Based on the HadCM3 climate change model, it is possible to project the future climate weather data that are transferred from TMY historical climate condition. The weather data generated from an existing tool developed in University of Southampton is used only for one scenario, which is Medium High Emission (A1)(Barker 2007), it is important to determine more scenarios , which are B1 (Low emission scenario), B2(Medium low emission scenario), and A1F1(High emission scenario)(Barker 2007), so that the impact of climate change can be studied more comprehensively. To drive climate data for the other three scenarios, a feasible method is to use pattern-scaling, which uses factors that are associated with the magnitude of each scenario. Uncertainties are unavoidable, but assessments of the pattern-scaling technique have concluded that it is reasonable to make these assumptions for the present generation of General Circulation Models (GCMs) in Hadley Centre (Hulme et al. 2002). 7  Predicted Mean Vote (PMV). PMV is one of the metrics that represent people’s perception or sensation, of surrounding thermal environment. There are six levels to roughly describe the occupants’ thermal comfort, which are -2, -1, 0, 1, 2, which are respectively too cold, cold, neutral, hot and too hot. The six factors that can impact occupants’ thermal comfort are air speed, temperature, radiant temperature, humidity, clothing insulation, and metabolic rate. The PMV method to estimate occupant’s thermal comfort is developed by Fanger (Fanger 1970) .  Predicted Percentage of Dissatisfied (PPD). PPD reveals the percentage of occupants who feel thermally uncomfortable because of the indoor environmental quality, which include air temperature, radiant temperature, air speed, humidity, metabolic rate and clothing insulation(Brager and Dear 2001). It is related and can be predicted based on PMV. In ASHRAE 55- 2010, a good indoor thermal condition requires 80% of the total occupants to stay comfortable (Turner et al. 2013). The PPD will be used to evaluate the indoor thermal condition that might be changed under the stress of climate change.  Typical Meteorological Year (TMY). TMY is one type of weather file that picks hourly weather data for a period of one month from a pool of years to constitute a typical rather than extreme weather condition. It was developed especially for solar energy conversion and building performance analysis (Marion and Urban 1995). Historically, there are three types of TMY weather files available: TMY (1952-1975), TMY2 (1961-1990) and TMY3 (1976 -2005) (Jentsch, Bahaj, and James 2008). Typically, TMY2 is the most commonly used weather file for building performance analysis nowadays.  Weather File. A weather file is a format of weather data collected and reorganized from a weather station, and it can be used for many purposes. 1.3 Study boundaries: Define the research scope Building performance analysis includes many aspects, and a good building overall performance does not only include energy performance and thermal condition, but also covers lighting performance, structure performance, acoustic performance, and material properties. Energy and thermal are more sensitive to the climate change compared with other aspects and will be focused on. 1.3.1 Building energy use for climate change Energy performance is projected to be different when the interaction between building and outdoor environment changes drastically, especially in terms of heat gain and heat loss, which will finally result in a different cooling and heating energy use. Life-cycle energy use will be used as the criterion to evaluate the building energy use. It reflects the building energy consumption during its entire life cycle. Energy Use Intensity (EUI) is no longer the best metric to analyse building energy use if the climate change is considered since the climate change is going to result in a different EUI year by year, and it is necessary to take the total energy use for a building’s entire life cycle to determine if the building is consuming more or less energy over time. 1.3.2 Thermal comfort analysis for climate change The thermal conditions can impact the effectiveness of a certain passive strategy that is applied to the building. It would be helpful to understand how the climate change is forcing the building to behave. The 8 typical metric to evaluate the thermal conditions is the predicted mean vote (PMV) or predicted percentage of dissatisfied (PPD). According to ASHRAE 55, a good indoor thermal condition requires 80% of the total occupants to be satisfied (Turner et al. 2013). When a certain passive strategy is applied, occupants usually do not have control over the indoor environment since the passive strategy heavily depends on the outdoor environment. The evaluation method to determine if the indoor environment is thermally comfortable is to calculate the percentage of the numbers in comfort zone. 1.3.3 Geographic scope of research Although climate change happens all over the world, only the continental U.S. region will be inspected. This region stretches widely from east to west and also reaches Mexico to the south of the U.S. and Canada to the north. The main reason that the U.S. is studied is that this country covers almost every typical climate condition that needs to be investigated. Three representative cities are Chicago (cold and dry), Los Angeles (hot and dry), and Miami (hot and humid). These three cities will be used to study the life- cycle energy use with the consideration of climate change. 1.3.4 Passive strategies selection The application of passive strategies is one of the most efficient ways to reduce the energy use from the power plant the source. In another words, passive strategies can help to reduce the energy demand. Since passive strategies totally rely on the environment, they should be sensitive to the climate, which is a dynamic system. The passive strategies chosen are Window-Wall-Ratio (WWR), solar heat gain co-efficient (SHGC) for glazing, natural ventilation, and shading for south facing windows. Once these passive strategies are selected, the building location is also determined, since passive strategies are location specific. 1.4 Deliverable: A process of designing climate adaptive buildings Climate change has a different effect for each region in the U.S.; some places may experience a large increase of temperature, while some areas may suffer a big jump of solar radiation, all of these mainly depend on the location of that region. Since the HadCM3 model has already generated a climate change grid across the world, and each grid has its own climate change pattern applied, it is possible to generate a map to visualize the climate change pattern for each grid across the U.S. The U.S. climate change map can help designers to get familiar with the local weather condition at current stage and future stage. The building life-cycle energy use and future thermal condition results have their own value to the industry. The new research finding is not the only deliverable, but also even more important is that the overall outcome of the thermal and energy research is more about a set of reference design guidelines in which the climate change is considered. These results can be adopted by architects and engineers who are pursuing high performance building. Specifically, the results of a thermal comfort study would be an evaluation of passive strategies’ performance. From the energy point of view, the relationship model between climate variables and building life-cycle energy performance will be developed, and life-cycle cost calculation tool which take 9 climate change into consideration will be developed, the tool can provide a more realistic result for designers and help them to become more confident when providing energy saving guarantee to the client. 1.5 Hypothesis Statement 1.5.1 Hypothesis statement As climate change influences building performance, design decisions made based on the current climate condition could have a tendency to become detrimental to life-cycle building performance. By analysing the building performance in terms of energy and thermal comfort based on different climate change scenario, this research provides recommendation for architects and engineers during the design early stage to avoid the detrimental consequence that might be caused by climate change. 1.5.2 Objectives There are three main objectives: Objective 1: For 7 climate zones, discover which passive strategy applied in the current time will have the best energy savings potential over time with respect to the climate change scenarios. The climate zones are from ASHRAE, and totally there will be 3 climate zones that need to be analysed the passive strategies are Window-to-Wall-Ratio (WWR), solar heat gain co-efficient (SHGC) for glazing. natural ventilation, and shading for south facing windows The climate change scenarios are B1, B2, A1and A1F1, which are proposed from IPCC fourth climate change assessment report(Barker 2007) Objective 2: Predict future building energy use in each different HadCM3 model block. Objective 3: Determine occupants’ thermal comfort levels when specified passive strategies are applied with respect to climate change models. 10 Chapter 2. Background for climate change, building energy modeling, and thermal analysis In order to explore the relationship between climate change and building performance, there must be a firm background to support the analysis. This chapter introduces background research for climate change, climate zone classification, importance and use of weather files, building energy modelling basics, and thermal comfort concepts. 2.1 Climate change: past, current, and future By looking at the situation of climate change during the past time and now, it is possible predict the future climate change and their relation with the building performance. 2.1.1 Introduction A better understanding of HadCM3 model is the foundation to explore its climate change’s impact on building energy use. 2.1.2 Observed phenomenon in the past The term “climate change” is defined by IPCC as “A change in the state of the climate that can be identified (e.g. using statistical tests) by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer. It refers to any change in climate over time, whether due to natural variability or as a result of human activity. (Barker 2007) Many phenomena have been observed by researchers during the past several years that support the theory of climate change. During 1995 to 2006, there are 11 years are ranked among the 12 warmest years in the history record. From 1956 to 2005, the temperature increase rate is 0.13 C per decade and the average ocean temperature increase reaches 3000m deep under the sea(Barker 2007). There is also indirect evidence that has been observed:  Sea level: the sea level increased a lot during the last 100 years, and it is consistent with the temperature increase. The average global sea level increase at a rate of 1.8 mm per year(Barker 2007)  Snow and ice: for the ocean iceberg, the records has showed an average 2.7% shrink per decade since 1978; for the mountain glacier has been decreasing by an average value of 7% since 1900(Barker 2007).  Precipitation: significant increase of precipitation has been observed in parts of North and South America(Barker 2007). 11  Extreme weather: cold days and nights has become less, and the hot days and nights become more; more heat waves s observed; more heavy precipitation is experienced (Barker 2007) IPCC fourth assessment report of climate change published in 2007 shows the record of historic weather variables. The first graph shows the increased temperature since 1850, increased sea level, and decrease of northern hemisphere snow cover (Fig. 2.1). The increased sea level and decreased snow level are all related with the increased temperature. It is predictable that based on historical data, the temperature will keep increasing and causing other weather variables to change correspondingly. Figure 2.1. Temperature, sea level and snow cover change during 1850 to 2000 (Barker 2007) Not only the climate change itself is observed during the past several years, but also the effect of climate change is observed by researchers. Specifically under the influence of global warming, many human systems are affected, the effect includes:  The management of agriculture is modified to meet the changing climate (Panel et al., n.d.)  The heat related death rate increase in Europe (Panel et al., n.d.) 12  More human activity are observed in Arctic(Panel et al., n.d.) Many studies have been carried out to quantify the magnitude of climate change. In addition, many scholars started to envision the future climate change based on the observed historical data, and a lot of climate change models are proposed to predict the path for climate change in the future. 2.1.3 Review of climate change scenarios based on HadCM3 model Since the climate change has been observed during the past years, many of climate change models have been developed to represent the change pattern and predict the future possible climate condition (Table 2.1). Model Name/Version Country Grid Spacing Transient climate response CCCma Canada 340 2.0+ CSIRO Mk2 Australia 360 2 CSM 1.0 USA 250 1.6 DOE PCM USA 250 1.3 MPI/DMI Germany 250 1.4 GFDL R30c USA 500 2 HadCM3 UK 265 2 MRI2 Japan 250 1.1* NIES-CCSR v2 Japan 490 3.1* Table 2.1 Climate change models developed in different counties. + means the response of this model is unknown, * means the model is not used by IPCC in defining the full possible range of future global warming(Hulme et al. 2002) Among all the climate change models, HadCM3 model is adopted to explore the climate change’s impact on building energy use, since this model provides the finest resolution for climate change across the world. The model is divided by 96 longitudes and 73 latitudes, and in each block formed by the grid lines, a specific local scenario is provided to accurately reflect the climate change level in that specific region. In the analysis, each block will be studied separately and a unique result can be carried out (Barker 2007). The model is developed in Hadley Centre in United Kingdom (Collins, Tett, and Cooper 2001). One advantage is that this model has a relatively smaller grid spacing, which means the simulation resolution is much higher in HadCM3 model compared with other models, and this model is also adopted in IPCC for future global warming trend projection, which will take the past and current change condition and based on these trend, the approximate future situation will be estimated. HadCM3 model is not a stand-alone model, it is a coupled model which take HadAM3H model and HadRM3 model into consideration. HadAM3H model is atmosphere model, which addresses the climate change by predicting the change in atmosphere. Before the development of HadCM3 model, there are two previous versions of this model in Hadley Centre. The biggest improvement from HadCM2 to HadCM3 is the introduction of new radiation scheme(Hulme et al. 2002). 13 Since the model solves the equation in each block, the climate change degree is also different from place to place, each location will be affected by its local change scenario, and this high resolution can result in a more precise prediction. The following figure shows the development of HadCM3 model (Figure 2.2), the physical question that addresses the mass and energy transfer is solved horizontally by 96 X 73 longitude and latitude ( 3.75  and 2.5 ) and by vertically by19 hybrid vertical levels above the ocean and 20 levels below the sea level.(Collins, Tett, and Cooper 2001). Figure 2.2 The development of HadCM3 model which divide the atmosphere into 19 levels and the ocean into 20 levels (Barker 2007) 2.1.4 Current climate: climate zones in United States Climate zone map is a map that provide people with climate zones, which can be used for sustainable design ( Figure 2.3) 14 Figure 2.3 There are totally 8 climate zones in U.S., each of them has very unique climate condition The intention of developing this IECC climate map zone map is to provide people with a simplified and generally accepted map that can be used to implement code(DOE 2010). There are totally 24 climate designations according to the IECC climate zones, based on the temperature and humidity difference, the entire U.S can be divided into 8 zones as listed below:  Subarctic – Climate zone 8: Subarctic region has more than 12,600 HDD (65  basis), the only place in U.S that is defined as subarctic is Alaska(DOE 2010)  Very cold – Climate zone 7: When the heating degree day is between 9,000 and 12,600 (65  basis), it will be considered as a region that is very cold, which is in climate zone 7.(DOE 2010)  Cold – Climate zone 5 and 6: When the heating degree day is within 5,400 and 9,000, the region will be defined as in climate zone 5 and 6, which is cold climate.(DOE 2010)  Mixed-Humid – Climate zone 4A and 3A: There are three factors that define a place as Mixed- Humid: more than 20 inches annual precipitation; 5400 heating degree days or fewer; outdoor average monthly temperature is below 45 F during the winter.(DOE 2010)  Mixed-Dry – Climate zone 4B: There are three factors that define a place as Mixed-Humid: less than 20 inches annual precipitation; 5400 heating degree days or fewer; outdoor average monthly temperature is below 45 F during the winter.(DOE 2010)  Hot-Humid – Climate zone 2A and 3A: The hot and humid areas have more than 20 inches annual precipitation. In addition, during a consecutive 6 months of a year, there should be more than 3,000 hours has wet bulb temperature that is higher than 67 F, or more than 1,500 hours has web bulb temperature higher than 73 F, or both.(DOE 2010) 15  Hot-Dry – Climate zone 3B: Hot-Dry is defined as less than 20 inches annual precipitation and the average monthly temperature is above 45 F throughout the year.(DOE 2010)  Marine – All counties with a “C” moisture regime: The region defined as Marine meets all the following requirements: the average temperature for the coldest month should within 27 F and 65 F; average temperature of the warmest month is less than 72 F; at least 4 month’s average temperature is above 50 F; The month with the heaviest precipitation in the cold season has at least three times as much precipitation as the month with the least precipitation in the rest of the year (DOE 2010). The purpose of defining climate zones for this thesis, is to implement code that can improve building energy efficiency. Each climate zone has their own unique climate condition, which requires the building to have different features, from construction to system, to consume the minimum energy. These requirements are all included in AHRAE 90.1, which mainly addresses the building energy use issue. 2.1.5 Weather file and its role in energy simulation A weather file is a record from a weather station that contains very detailed weather data at a specific location. The weather file can reflect the weather conditions very accurately since it has a wide range of weather variables what are recorded as hourly data. There are many different types of weather files, each of them has different information recorded, and each of them serves different purposes. For example, Typical Meteorological Year (TMY) weather data is the most suitable data set can be used for building performance analysis, but it is not suitable for building system design since TMY file contains typical weather data as its name suggested, it does not reflect extreme weather condition.(Marion and Urban 1995) A weather file may contain different data sets, but also may be in different file formats. Based on different formats, the file can be used in different programs. For example, a CSV file is the one that can be read by spreadsheet and it is editable, EPW format can be used by energy simulation software that uses EnergyPlus as simulation engine. The following table shows the classification of different weather data sets, each of them are from different organizations and cover different number of locations across the U.S. (Marion and Urban 1995): Data set Full name Derived from Covered locations GBRI GRIdded Binary Standardized by the World Meteorological Organization to store historical and forecast weather data TMY Typical Meteorological Year US NOAA NCDC data sets 238 US locations TMY2 Typical Meteorological Year 2 US DOE NREL data sets 238 US locations TMY3 Typical Meteorological Year 3 US DOE NREL datasets 1,200 US locations 16 TRY Test Reference Years US NOAA NCDC data sets 60 US locations IWEC International Weather for Energy Calculations US NOAA NCDC data sets 227 locations outside the USA and Canada WYEC ASHRAE Weather Year for Energy Calculations US NOAA NCDC data sets 51 US locations WYEC2 ASHRAE Weather Year for Energy Calculations US NOAA NCDC data sets 51 US locations Table 2.2 Different weather file data set (Hong, Chang, and Lin, n.d.) The TMY file is one that is commonly used for analysis. TMY file stands for Typical Meteorological Year data; it is a data set of hourly value recorded. The TMY file contains monthly weather selected from different years to represent a most typical weather condition throughout a year. It is not a good indicator for a specific year’s weather or a future prediction, but it represents a typical weather condition over a long period of time, such as 30 years (Marion and Urban 1995). That’s why building performance analyses usually take TMY file as simulation weather files. There are three different types of TMY weather files: TMY3 = 1976 -2005, TMY2 = 1961-1990, TMY = 1952-1975 (Holmes and Ap 2011). The TMY weather file contains the following information: Extra-terrestrial Horizontal Radiation; Extra- terrestrial Direct Normal Radiation; Global Horizontal Radiation; Direct Normal Radiation; Diffuse Horizontal Radiation; Global Horizontal Illuminance; Direct Normal Illuminance; Diffuse Horizontal Illuminance; Zenith Luminance; Total Sky Cover; Opaque Sky Cover; Dry Bulb Temperature; Dew Point Temperature; Relative Humidity; Atmospheric Pressure; Wind Direction; Wind Speed; Horizontal Visibility; Ceiling Height; Present Weather; Perceptible Water; Broadband Aerosol Optical Depth; Snow Depth; Days Since Last Snowfall(Marion and Urban 1995). All the weather data that will be used in building performance analysis are included in these variables. Across the entire U.S, there are 239 weather stations that are recording TMY weather data, all of them are available and can be downloaded online. CSV (comma separated values) file is format that can be opened with spreadsheet, it allows user to see the detailed information within a weather file and if it is necessary to edit the file, CSV is suitable. Some weather files can be saved as CSV files. These files typically record about 8760 fixed data records. There are about 7 formats that are popularly used in the industry (Table 2.3). Format Description Application Download .EPW EnerygPlus weather files EnergyPlus, ESP-r EnergyPlus .DDY ASHRAE Design Condition Design Day Data file - EneryPlus .BIN Binary data eQuest, DOE-2 DOE2.com 17 .STAT Summary report Directly read from notepad EnergyPlus .CSV Spreadsheet data for TMY3 files Spreadsheet-based and home-coded tools NREL .WEA Weather tool data originally developed by Autodesk for Ecotect Ecotect, Weather tool Install Ecotect .TMY2 TMY2 data in ASCII format TRANSYS NREL Table 2.3 Formats and application of weather files (Hong, Chang, and Lin, n.d.) One can use existing weather files, especially those that are easily edited in CSV format to create a weather file with values for a predicted future weather scenario that can be used to analyze future building performance. Based on the future weather variables projection considering the IPCC climate change scenario, the UK Climate Impacts Programme in 2002 (UKCIP02) proposed that the weather variables that will be changed in the future include temperature, precipitation rate, wind speed, snowfall rate, humidity, total cloud, surface long wave flux, surface shortwave flux, soil moisture content, sea level pressure. The following table shows all the variables that could be changed (Figure 2.4): Figure 2.4. The weather variables will be different in the future (Belcher, Hacker, and Powell 2005) There are specific algorithms applied to each variable to project future values, the method is called “weather data morphing”. There are three basic equations used to morph the data:  Shift – x = x0 + xm  Stretch – x= amx0  Shift and stretch – x=x0 + xm + am X ( x0 - <x0>m)(Belcher, Hacker, and Powell 2005) With the basic equations to describe the change in each weather variable, the future weather variable can be calculated based on a mathematical method. In the TMY weather file, the data includes the value for each variable throughout the year, which has 8760 hours. The HadCM3 model will be eventually transformed to mathematical equations and each value in weather file will be modified and be replaced by the new values for future weather condition (Belcher, Hacker, and Powell 2005). The result of this calculation is a new weather file which contains new weather data, which means every variable value is replaced by new ones and this file will be used for the building performance analysis. 18 2.2 Building energy simulation fundamentals The building energy simulation is a based on the climate and building information. It is necessary to understand the basic workflow about building energy simulation. Building energy simulation, also known as building energy modeling (BEM), is a developed technique that allows people to analyze building energy use based on the given information during the design process, such as building geometry, envelope material, occupancy information, lighting power density and equipment power density. Energy modeling is based on environment and building information; using a simulation engine (such as DOE 2), software is able to predict the building energy consumption based on the given information. Building energy simulation is widely used in this industry. It is one of the two paths that help people to evaluate their project and achieve code requirements or purse a sustainability certificate. One path is called prescriptive design, which forces designers to follow the code requirements and achieve building energy efficiency. The other path is called whole building energy simulation, which allows designers to rely on the software to do energy simulation and further evaluate building energy performance. The following figure shows the simplified workflow of building energy modeling. Basically, once the building physics and environmental condition are determined, the building energy consumption can be estimated (Figure 2.5): Figure 2.5. With the known information of building geometry, system type, occupancy schedule, envelope materials and climate condition, building energy use can be predicted 2.2.1 Energy simulation and their application Energy simulation is a technique that is powerful enough to estimate the building energy use in the future. It can be used to predict future building energy use? 2.2.1.1 Construction The building envelope is one of the biggest factors that influences building energy consumption, since the building façade is directly related with the outdoor environment and is very sensitive to the climate. The selection of building construction type should be careful and a good decision can help to minimize the building energy consumption. The effects of building construction can result from many different factors, which can be the material of the envelope, the thickness of the insulation, R-value of the insulation, thickness of the air gap in a double pane glass, thermally massive material, surface reflectance of the envelope, however, the ones that can influence the building energy use are insulation, thermal mass and surface reflectance (Talyor and Miner 2014) the following factors: 19  Insulation: Insulation should be modelled in an energy simulation process, since it can reduce the amount of heat that will be transferred from outdoor to indoor, usually, the thicker the insulation is, the more heat will be blocked from outside. Therefore, an accurate input of thickness and type of insulation is important to improve the result accuracy.  Thermal mass: Thermal mass is a property of construction materials, the best material to apply thermal mass strategy to a building is concrete, since concrete has the capability to store heat and largely delay the time the heat will be transferred from outdoor to indoor. (Al-Sanea, Zedan, and Al-Hussain 2012)  Reflectance: Reflectance is the metric showing a material’s ability to reflect solar radiation from its surface, the more radiation is reflected, less radiation will be absorbed and transferred into the indoors. During an energy simulation process, construction is one of the most important factors. The correct input of building construction information will provide more realistic results. Study of climate change especially requires accurately input building envelope information. 2.2.1.2 Internal loads and schedule The input of internal information is also very important because, for an office building, the internal load accounts for about 28% of the total building energy use (Heidarinejad et al. 2014). The building energy consumption can be break down to the following several categories: plug loads, interior lighting, exterior lighting, hot water, fans, pumps and HVAC controls, space cooling, and space heating. The interior lighting energy use, equipment energy use, plug energy use will all contribute a large part of building total energy consumption. The cooling energy accounts for 10 percent of the total energy, heating accounts for 58 percent, lighting accounts for 18%, equipment accounts for 10 percent and other energy use accounts for 4% of total energy use (Fig. 2.6). 20 Figure 2.6. Typical office building energy use breakdown (Energy 2003) Therefore, it is important to input correct internally related information before the building energy use is analysed. The internal load can be divided into three components: occupants load, lighting load and equipment load. For lighting and equipment, two factors should be considered, one is the operation schedule and the other one is power density. Typically, for an office building, the Lighting Power Density (LPD) is about 1 - 2.8 w/ft 2 , and the equipment power density is about 1.3 w/ft 2 .(Grondzik et al. 2011). 2.2.1.3 Input of weather file Building energy simulation cannot be done without providing a proper weather file, since the simulation engines need environmental data to calculate the external load for the building. However, the requirement of weather is different in each software, it is important to select a correct weather file format before the energy simulation. For example, the software that use EnergyPlus as a simulation takes EnergyPlus weather file for simulation, these software include IES VE, Design Builder, and others. However, the software that is using DOE-2 as simulation engine require weather to be .BIN format, these software includes Green Building Studio, eQuest, and others. Among all the variables included in building energy simulation, weather data is the one that is most uncontrollable, since these data is obtained from the real record from weather station. Therefore, the accuracy of weather data can highly influence the building energy use. When the input weather data accuracy is different, the building energy use can vary by ±7% and the predicted monthly loads can vary by ±40% (Bhandari, Shrestha, and New 2012). 2.3 Thermal comfort analysis 2.3.1 Introduction Thermal comfort is one of the five components in Indoor Environmental Quality (IEQ), the other four components are: acoustics performance; lighting condition; Indoor Air Quality (IAQ); spatial condition. Specifically, for thermal comfort alone, there are six components that can decide the occupants’ satisfaction, these variables include: air velocity; temperature; radiant temperature; relative humidity; clothing insulation; metabolic rate(Turner et al. 2013). The first hour are non-human variables, and the last two are related to the human body. 21 2.3.2 Predicted Mean Vote (PMV) The Predicted Mean Vote (PMV) index reflects the mean response of a larger group of people according to the ASHRAE thermal sensation classification: An index that establishes a quantitative prediction of the percentage of thermally dissatisfied people determined from PMV (Turner et al. 2013). The calculation of PMV can be expressed as following: PMV = (0.303 e-0.036M + 0.028) L (Fanger 1970) Where PMV = Predicted Mean Vote; M = Metabolic rate; L = Thermal load The thermal load is defined as the difference between the internal heat production and the heat loss to the actual environment(Fanger 1970). Specifically, for a person at comfort skin temperature and evaporative heat loss by sweating at the actual activity level. The following table shows the scale of thermal comfort (Table 2.4): Value Sensation -3 Cold -2 Cool -1 Slightly cool 0 Neutral 1 Slightly warm 2 Warm 3 Hot Table 2.4 Sensation level of PMV(Fanger 1970) 2.3.3 Predicted percentage of dissatisfied (PPD) The definition of PPD is An index that establishes a quantitative prediction of the percentage of thermally dissatisfied people determined from PMV.(Turner et al. 2013) The PPD (predicted percentage of dissatisfied) index is related to the PMV. It is based on the assumption that people voting +2, +3, –2, or –3 on the thermal sensation scale are dissatisfied and on the simplification that PPD is symmetric around a neutral PMV. The relationship between PMV and PPD shows that when people have different sensation level and direction, the percentage of dissatisfied people will be different (Fig. 2.7). 22 Figure 2.7. Relationship between PMV and PPD(Turner et al. 2013) The relationship between PPD and PMV can also be described in the following expression: Where PMV = Predicted mean vote This model is based on air speed that is below 50 ft/m, and an acceptable thermal environment for general comfort is PPD less than 10 and PMV ranges from -0.5 to 0.5 (Turner et al. 2013). PPD is a normally used metric to evaluate occupants’ comfort level in a building, since it simply uses the percentage of unsatisfied people to show the results and it is easy to be understood. According to ASHRAE, an acceptable indoor thermal environment requires 80 percent of occupants in the building feeling satisfied, therefore, this metric is also used in this thermal comfort analysis study to determine the satisfaction level of the occupants (Turner et al. 2013) . It is more difficult to use as a predictor. 2.3.4 Adaptive comfort model Usually, the building is mechanically ventilated, and occupants cannot open and close the windows. Natural ventilation is the best way to access outdoor air, it is one of the passive design strategies that can reduce building energy use. Occupants can adapt themselves to the environment, since the occupants have access to adjust the opening, and change their metabolic rate to adapt to the environment and become comfortable. Often, buildings are mechanically ventilated, and occupants cannot open or close the windows. Natural ventilation, a passive design strategy, is a good way to access outdoor air. When allowing occupants to control their environment, ASHRAE allows for a wider comfort zone. This can help reduce building energy use. With natural ventilation, ASHRAE allows for “adaptive comfort;” the occupants thermal comfort zone is extended and becomes more flexible (Fig. 2.8). There are two levels for thermal comfort determination. 23 One is the 80% range, which is more typical, and the 90% range is for higher thermal comfort requirements.(Turner et al. 2013) Figure 2.8. Acceptable operative temperature ranges for naturally conditioned spaces (Turner et al. 2013) By following the adaptive thermal comfort model, one can not only improve occupants’ thermal comfort, but also take the advantage of natural ventilation to minimize the building energy use. However, the use of natural ventilation should also be re-considered once climate change is considered as a factor, since the global warming will continue to increase air temperature – this might be disadvantageous is some places and perhaps advantageous in others. 2.3.5 Conclusion It is important to firstly understand how the building energy simulations work and how the climate is actually affecting the building energy performance. It addition, the basic understanding of climate change model HadCM3 plays a key role in the analysis, since the only variable that is taken into consideration in the analysis will be weather file. 24 Chapter 3. Method and progress of studying climate change’s influence on building performance 3.1 Introduction: development of main workflow The climate change’s impact on building performance can effect many different aspects, including building energy performance, indoor thermal condition, building life cycle cost, and others. Building energy performance effects the other issues directly and is sensitive to changing climate conditions. A sub-study on thermal analysis focuses on exploring how much the future weather will impact occupants’ thermal comfort level. In addition, since the global warming effect will finally result in a change in Heating Degree Day (HDD) and Cooling Degree Day (CDD), it is possible to create a future climate zone map. The current climate map that can accurately reflect the local weather condition for each place may be invalid in the future and can no longer be used to determine the climate related building features, which includes U value for building façade, Solar Heat Gain Coefficient (SHGC) value for fenestration, baseline building system type and so on. Therefore, a future climate zone map for 2050s time period can be developed based on the calculation from 2050s weather file. The workflow has three main steps and then a related part for visualizing climate maps (Fig. 3.1). Figure 3.1. Main research workflow for each part The part 1 and part 2 are studied at the same time, since they share the same weather file and essentially the two parts are highly related with each other. 25 3.2 Climate change scenarios projection: pattern-scaling from A1F1-medium high emission scenarios The core of climate change building energy simulation is the weather file, since every calculation and simulation is related with the use of future weather data. A convenient representation of this data is the spreadsheet. Therefore, the preliminary work is to figure out a way to generate sufficient amount of weather file that can be used for any kind of analysis, including Heating Degree Day and Cooling Degree Day calculation, building energy simulation, and thermal comfort analysis. However, the amount of usable weather files is limited, it is necessary to prepare weather file for the following studies. The first step is to create weather data for every emission scenario. According to the IPCC climate change model “HadCM3” model, the future climate condition is simulated based on four different emission scenario, which are B1- Low Emission, B2-Medium Low Emission, A2-Medium High Emission and A1F1-High Emission (Barker 2007). However, the “CC world weather generator” developed in University of Southampton is only able to calculate the future weather condition for scenario A2, which is high-medium emission level scenario (Jentsch, Bahaj, and James 2008). Several projections of future climate for 2020s, 2050s, 2080s can be made (each with low, medium, and high emissions), but to start, only one scenario for each time period is created (Fig. 3.2). Fig. 3.2 it is possible to use the current TMY2 file and HadCM3 to predict the future climate weather file which may contain higher dry bulb temperature This limitation is originally from the HadCM3 model, which is used in the IPCC fourth assessment report and developed in Hadley Centre in the United Kingdom. This limitation is due to the sophisticated and time consuming simulation process. However, IPCC does provide the way to calculate the weather data for the other emission scenarios. The method provided from IPCC is “pattern scaling”, in which the climate change pattern for each emission scenario is derived from a single set of master patterns. The master set of pattern is from the HadCM3 simulation results using A2 scenario. For example, if the magnitude for A2 (Medium High Emission) is 80, the magnitude for B1, B2 and A1F1 would be 50, 70 and 100 respectively (Hulme et al. 2002). When four emission scenarios are calculated using pattern scaling, more future weather files can be created, and it can help to better understand the influence of future weather condition (Fig. 3.3). 26 Figure 3.3. More scenarios for each time period are calculated and the analysis scope becomes larger These patterns are used to predict the other three emission scenarios; each value represents the magnitude of the change (Table 3.1). Table 3.1. Patter scaling factors(Barker 2007) By using the pattern scaling factor, it is possible to generate weather files for the other three scenarios for each time-slice in the weather file by calculating the magnitude of change for each weather parameter. However, the file initially generated from the spreadsheet is not a usable weather file. In order to use the weather data, it is necessary to further convert the file to another format which can be read by energy simulation software. There are two commonly used weather files in the industry, EPW file and BIN file. EPW file can be used in almost any kind of energy simulation program; however, BIN file is the file that is been used in eQuest. To make the weather file more universal, it is necessary to convert the spreadsheet to EPW format. The tools that can be used for conversion are CC world weather generator and EnergyPlus weather file convertor, which can be downloaded from their website: list the website. In “CC world weather generator”, it is very simple to convert the file format from CSV (a file format that can be exported by a spreadsheet program) by using the function in the weather generator to generate climate change EPW weather file (Fig. 3.4). Time-slice Low Emissions Medium-Low Emission Medium-High Emission High Emission 2020s 0.24 0.27 0.27 0.29 2050s 0.43 0.5 0.57 0.68 2080s 0.61 0.71 1 1.18 27 Figure 3.4. Screen shot of interface of CC world weather generator 3.3 Sensitivity analysis: testing the influence of climate change Before the analysis, it is necessary to find out the sensitivity of the variables to make sure that there will be a noticeable change in the results. To analyse climate change impact, the preliminary work is to test if the future weather conditions will influence the building performance. The first variable is the weather file, since the weather file plays a key role in studying the future climate condition. Combing all the time-slice and emission scenarios, for each specific location, there are 16 sensitivity tests that were done so that it can provide a more comprehensive understanding of the climate change’s impact in the future, which ranges from the lowest impact ( B1 scenario in 2020s) to the highest impact (A1F1 scenario in 2080s). Since the influence of climate change is different from place to place, the sensitivity of climate change should be different. Therefore, it is necessary to test more locations. In this case, three locations were selected: Los Angeles, Miami, and Boston. The three cities are representative for three very distinct climate characteristics, which are hot-dry, hot-humid and cold- dry. Each time-slice will have four emission scenarios, and totally there are three future time-slice and a current time slice (Fig. 3.5). 28 Figure 3.5. 16 sensitivity tests to determine the climate change’s impact In addition, the sensitivity for each weather variable will help to decide which variable has more value to be studied, since it is not necessary to analyse the parameters that have no impact on building energy consumption when the climate changes. The parameters that can have significant influence on building energy performance can be divided into two groups, the internal factors and external factors. The internal factors can be excluded from the climate change related study since the internal load does not respond to the outdoor environment. For example, at this stage, there is no need to analyse the occupants’ schedule’s response to the climate change and its influence on building performance concerning climate change, since the building occupants profile is determined by the building type rather than climate condition. This could change as consumption patterns change with climate change. Italians do not currently use their buildings the same way that Norwegians do. The schedules are very different. Therefore, it is important to eliminate the internal variables and test the climate related variables. The building façade features are sensitive variables to the climate change since the different weather conditions will generate different loads on building façades. The following façade parameters will be tested before analysing their influences:  Window-Wall-Ratio (WWR)  solar heat gain co-efficient (SHGC) for glazing  natural ventilation  shading for south facing windows 29 3.4 Future climate map generation for visualizing future climate zones 3.4.1 What is a climate zone map Climate zone maps show each defined climate zone on a map in different colors to separate a certain area into many regions that has distinct climate conditions. The climate map is useful for building performance analysis, since the determination of building physical variables is heavily related to the climate conditions and is selected based on the climate map. For example, the determination of Solar Heat Gain Coefficient (SHGC) is different from climate zone to climate zone, based on the location of the project, the proper SHGC value should follow the climate zone requirement. In a climate zone map, there will be several regions which represent a specific climate zone. For each climate zone, it consists of one alphabetic letter and one number. The alphabetic letter represent the temperature level of that area, which is reflected by HDD and CDD; the number represents the humidity level of that area, which is reflected by the precipitation. For example, the climate zone 3B, 3 means the HDD is between 4,500 to 5,400 … and CDD is less than 5,400…., the B means it is close to the marine. By calculating the future weather’s temperature and humidity level, it is possible to change the letter and plot new climate map on the U.S. map. The most commonly used climate map for United States is ASHRAE (or IECC) climate map, in which the entire U.S is divided into 7 climate zones based on the temperature difference, the numerals in a climate zone name represent the heating degree day and cooling degree day ranges, which are used to determine the temperature division of a place. The alphabetic letter in a climate zone name represent the humidity of a certain area, there are only three category of humidity, Marine (C), Dry (B) and Moist (A). The (DOE 2010)following figure shows the U.S climate zone map from ASHRAE ( or IESS).(DOE 2010) The IECC climate map was developed by the U.S. Department of Energy based on research at Pacific Northwest National Laboratory (Fig. 3.6).(DOE 2010) This map is also adopted by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) and included in the ASHRAE 90.1 standard to provide guidance for the architects and engineers’ design decision-making. The map plays a key role in any project that is pursing LEED certification, since it determines the properties of the envelope, which has a huge impact on building energy performance 30 Figure 3.6. The ASHRAE (or IECC) climate map for United States(DOE 2010) There are seven climate zones in the Unites States according to the temperature difference from region to region, and the precipitation levels also vertically separate the U.S. into three parts with different humidity levels. More importantly, the climate zone boundary matches the counties boundary, which means if two counties have very big temperature difference, they will be defined as two different zones. Specifically, the determination of the temperature characteristics for each county is based on the local Heating Degree Day (HDD) and Cooling Degree Day (CDD), and the humidity level for each county is determined by the local precipitation. The thermal criteria sets the climate zone (Table 3.2). Table 3.2. Thermal criteria to determine climate zones, they are Hot-humid, Mixed-humid, Hot-dry, Mixed-dry, Cold, Very-Cold, Subarctic, Marine 3.4.2 How does climate zone effect energy calculations Since the designation of climate zone is based on the CDD and HDD, it is possible to envision the future climate condition if the CDD and HDD values can also be calculated from the future weather files. This Zone CDD(50 Base) HDD(65 Base) 1 > 9,000 - 2 6,300-9,000 - 3 4,500-6,300 - 4 <4,500 <5,400 5 - 5,400-7,200 6 - 7,200-9,000 7 - 9,000-12,600 Thermal Criteria 31 approach is reliable since the weather file provides hourly temperature throughout a year and the future CDD and HDD can be predicted from a simple calculation in a spreadsheet. 3.4.3 How to create the new climate map Three tools were used to output the data for the new climate map: CC world weather generator, Emission scenario generator, and EnergyPlus weather file convertor. Additional software was used for tabulating the results (Excel) and drawing the map. EnergyPlus weather file convertor plays an important role in this step, since the weather itself has a lot of different formats, and not every file format can be used to do calculations in a spreadsheet. For example, normally the most commonly used weather file for energy simulation is EPW format, which stands for EnergyPlus Weather file, this kind of weather file can be read by energy simulation that relies on EnergyPlus as an engine. However, the EPW file is not editable in Excel, which means it is necessary to convert it to another format that can be used in spreadsheet and analyzed by users. The file that can edited is the CSV file, which is suitable for analysis. The following image shows the workflow about how to calculate further HDD and CDD in existing TMY weather file. Figure 3.7. Workflow to predict future HDD and CDD Once the future climate zone is determined for each county, a future climate zone map was developed to visualize the future climate condition using ArcGIS. Geographical Information System (GIS) is a technology that enables the normal map to contain information that has geographical related information. Then, based on the difference of HDD, CDD and precipitation between each two counties, the GIS map was created. 32 3.4.3 Interpolating weather stations In a weather file, most data related to the local weather condition are recorded. There are important weather data included in the weather file and they can be used for predicting future climate zones. The precipitation included 3.5 Building energy use throughout its life cycle 3.5.1 Selection and description of baseline model As discussed previously, the building energy simulation is a product of both the building physical information and the climate. Since the climate contains the only variables that will be studied in the analysis, it is important to keep other factors the same during the simulation. The best to way to keep the building physics all the same throughout the study is to create a baseline model and use different weather files for each climate zone. There are many baseline model references can be used to decide a most proper model for simulation. For example, if the study only focuses on California, Title 24 should be the best reference to create the baseline model since it has prescriptive design requirement that can be used as default baseline model information. However, since the analysis is across the entire U.S. and 7 climate zones, the reference selected is ASHRAE 90.1. ASHRAE 90.1 address the building energy issue across the 7 climate zones, and there is detailed requirements for each climate zone. Specifically, the section that will be used to develop baseline model is ASHRAE 90.1 Appendix G, which is section in ASHRAE standard that specifically addresses the development of baseline model that will be used to compare against the proposed model simulation result. There are many different types of building, including office, school, hotel, single family, hospital, and others. It is important to study all the building types to have a comprehensive understanding of the climate change’s impact on buildings that serve different purpose and group of people. However, at this point, the building type selected is medium office building, which is relatively representative for all the office buildings. The selected baseline medium office building is an ASHRAE building that is adopted by U.S. Department of Energy as a prototype building. The office prototype building is included in a prototype baseline model package downloaded from DOE website: http://www.energycodes.gov/commercial-prototype-building- models. The package includes a medium office, large office, strip mall, primary school, medium hotel, high-rise apartment, and others. The medium office model, shown in the IES VE energy simulation software, is a three stories office building with 33% window-to-wall ratio; the total floor area is 53,600 sq.ft. (Fig. 3.8). 33 Figure 3.8. Medium office baseline model from ASHRAE prototype buildings This model will be used in each climate zone and each county during the analysis. The geometry will stay the same throughout the entire process. However, the building variables will be different for each climate zone to create another baseline model that meets the AHRAE 90.1 standard, which has specific requirements for building in different climate zones. For example, the wall construction, fenestration, system type, and others will be changed (Table 3.3) for the baseline model. Exterior walls Construction Steel-Frame Walls (2X4 16IN OC) 0.4 in. Stucco+5/8 in. gypsum board + wall Insulation+5/8 in. U-factor (Btu / h * ft 2 * °F) and/or R-value (h * ft 2 * °F / Btu) ASHRAE 90.1 Requirements Nonresidential; Walls, Above-Grade, Steel-Framed Dimensions based on floor area and aspect ratio Tilts and orientations vertical Roof Construction Built-up Roof: Roof membrane + Roof insulation + metal decking U-factor (Btu / h * ft 2 * °F) and/or R-value (h * ft 2 * °F / Btu) ASHRAE 90.1 Requirements Nonresidential; Roofs, Insulation entirely above deck Dimensions based on floor area and aspect ratio Tilts and orientations horizontal Window Dimensions based on window fraction, location, glazing sill height, floor area and aspect ratio Glass-Type and frame Hypothetical window with weighted U-factor and SHGC U-factor (Btu / h * ft 2 * °F) ASHRAE 90.1 Requirements Nonresidential; Vertical Glazing SHGC (all) Visible transmittance Operable area 0 Skylight Dimensions Not Modeled Glass-Type and frame NA U-factor (Btu / h * ft 2 * °F) 34 SHGC (all) Visible transmittance Foundation Foundation Type Slab-on-grade floors (unheated) Construction 8" concrete slab poured directly on to the earth Thermal properties for ground level floor U-factor (Btu / h * ft2 * °F) and/or R-value (h * ft2 * °F / Btu) ASHRAE 90.1 Requirements Nonresidential; Slab-on-Grade Floors, unheated Thermal properties for basement walls NA Dimensions based on floor area and aspect ratio Interior Partitions Construction 2 x 4 uninsulated stud wall Dimensions based on floor plan and floor-to-floor height Internal Mass 6 inches standard wood (16.6 lb/ft²) Air Barrier System Infiltration Peak: 0.2016 cfm/sf of above grade exterior wall surface area (when fans turn off) Off Peak: 25% of peak infiltration rate (when fans turn on) Table 3.3: Basic architectural information regarding to this baseline model based on 3.5.2 Scope of the study: selection of regions for analysis To envision the future building energy consumption, the ideal study scope is to cover the entire U.S. to better understand climate change’s impact on building performance across the entire country. Additionally, based on the climate change “HadCM3” model, which divides the earth with 96 longitudes and 73 latitudes, it makes sense to analyze the entire country because for each HadCM3 model block, a unique local climate change scenario is used to predict future climate condition. For U.S., there is approximately 90 blocks each potentially with a different level of climate change. Specifically, the weather variables that change in the HadCM3 model are temperature, humidity, atmospheric pressure, window speed, solar radiation, and precipitation. Only these variables will be taken into account during the energy simulation process; if the degree of these changes is different from one area to another area, it is necessary to study both places to distinguish the future building energy use. The HadCM3 model divides the U.S. into a grid (Fig. 3.9). Specifically, the grid is 2.5  in latitude by 3.75  in longitude, which, in distance, is approximately 165 miles by 186 miles. The gird model has a high resolution climate change information, in each block, a unique climate change scenario is proposed. Each unique climate change in a certain area is calculated and simulated by using local physical phenomenon and equation, which is generally equations regarding to the heat, mass and energy transfer in that specific area. (Hulme et al. 2002) 35 Figure 3.9. Grid model covering the entire U.S. However, it is extremely time consuming if all the climate change blocks are analysed, since for each block there are four emission scenarios (low emission, medium low emission, medium high emission, and high emission) and 4 future time period (current, 2020s, 2050s and 2080s), which means that for each block, there is at least 12 runs of simulation, and once the design strategies that will be tested is selected, more simulation is needed. A more realistic plan for analysis is to select three regions that have very distinct climate condition so that they can be representatives for other parts that has similar climate. The proposed three states are California, New York, and Florida which contain areas that are Hot-dry, Cold- dry and Hot- humid. In each state three cities were analysed, which include Los Angeles, New York and Miami. Energy simulation: prediction of future building energy consumption. The energy simulation for future climate requires two sets of input information: the building physical information for building physics and the weather file. The building physics is determined by the selected baseline model, which is essentially from AHSRAE 90.1 standard, the future weather file is derived from the weather file generation tools, which can provide future weather file for three time period: 2020s, 2050s and 2080s. The energy simulation tool used is IES VE, a building performance program developed by “Integrated Environmental Solutions” company. To better understand the climate change’s impact, the life cycle building energy use was calculated based on the simulation results. One drawback in calculating the building life cycle energy use is that the simulation results are only for four time periods: 2014, 2020, 2050 and 2080. It means the building energy use between each two time period is unknown. Therefore, in this case, it is assumed that the climate change between each of two time period is linear. This assumption contains a certain uncertainty level, but it will help to make the calculation much easier (Fig. 3.10). It is assumed that the overall trajectory is relatively continuous. 36 Figure 3.10. Life cycle energy use – Assuming the climate change is liner, the shared area under each line is the energy used throughout its life cycle For energy simulation, not only the baseline model will be analyzed, but also a modified baseline model, which is using different design strategies, will be simulated, so that it can help to find out the best strategies that can keep the building energy efficient throughout its life cycle. Based on the sensitivity test, the most sensitive building features were determined and were used in the building energy simulation.  Fenestration SHGC  Window-to-wall ratio  Window Shading The workflow for the building life cycle energy use study started by creating many new weather files, making 3 baseline buildings, and running simulations for each of the variables. (Fig. 3.11). 37 Figure 3.11. Workflow of building life cycle energy use calculation The climate model is the source for predicting the future weather file. Three time periods were generated: 2020s, 2050s and 2080s. Under each specific time period, there are four proposed emission scenarios that can result in four different climate change levels. Because of the limited time, it is difficult to test all 12 cases; the selected time period and emission is 2080s and scenario A1F1. One reason to select these two extreme cases is that it can help to better understand the climate change’s impact by analyzing the worst scenario (2080s and A1F1). Under these specific cases, each design strategy was analyzed. Since the climate is dynamic, the selection of building façade feature or system requirement should not be a fixed value. A decision may be beneficial for the building under current weather condition, but may have a different effect on the building in the future when the climate keep changing. Therefore, life cycle energy use is the best way to evaluate the effectiveness of a specific design decision. For example, from ASHRAE 90.1 requirement, the SHGC value for building in Boston (Climate zone 5) is 0.4, but if solar radiation changes in the future and the temperature keeps increasing, 0.4 may not be the best value, it is necessary to test different values and calculate life cycle energy use so that a comparison can be made to evaluate which value should be the most beneficial one for building life cycle energy use. 3.6 Thermal comfort analysis Thermal comfort analysis mainly focuses on the study of passive strategies, which in this case would be natural ventilation. By introducing natural ventilation, people’s thermal comfort can be largely different based on their own activity. And with the increase of outdoor temperature in the future, it is critical to see how the future weather will influence people’s thermal comfort when the natural ventilation is applied. 38 3.6.1 Sensitivity test and selection of passive strategies As the temperature increase, passive strategies will be highly affected since they are environmentally related. According to HadCM3 model, the weather variables that will change include temperature, humidity, precipitation, solar radiation, total sky cover, sown cover, and illuminance. (Barker 2007). Therefore, certain strategies that are relevant to these variables may be influenced and were evaluated again before using them as passive strategies. Sensitivity tests were necessary to determine the passive strategies that will be analysed under climate change condition. According to the calculation in the weather file, the following parameters change a lot compared with current conditions: temperature; humidity and solar radiation, which can be seen that in the weather file spreadsheet, the temperature increases by a certain degree, relative humidity also changes within a certain range, the same thing happens to other variables. The selection of passive strategies should follow the calculation, which means that the strategies related with temperature, humidity and solar radiation should be evaluated. The selected strategies are listed as below:  Natural ventilation  Mixed-mode ventilation  Mechanical ventilation 3.6.2 Calculation of numbers of hours in comfort zone for natural ventilated building Natural ventilation is an environmentally sensitive strategy, since the purpose of providing natural ventilation is to supply cool air without using an HVAC system so that the energy use will be decreased. However, if the temperature increases in the future, natural ventilation may not be a good option in certain climate zones because occupants may have uncomfortable feeling due to the high temperature of outside air. The study of natural ventilation focused on calculating the numbers of hours in the thermal comfort zone for occupants to evaluate the effectiveness of natural ventilation in the future. The method determines if the occupants are likely to be satisfied with their interior thermal environment; AHSRAE 55 indicates that the standard of a thermally comfortable indoor environment is that there is more than 80% of the occupants are satisfied with the indoor temperature(Brager and Dear 2001). In addition, the total hour during a year when people feel comfortable was calculated to directly show the difference between 2014, 2020s, 2050s and 2080s. 3.7 Summary The method proposed can fully cover each study point in the research. Generally, the analysis was based on the simulation results which will be derived from IES-VE. And the calculation part for the future climate map envisioned will be done with the spreadsheet and the software CC world weather generator. 39 Chapter 4. Future Climate Zone Map In Chapter 3, the methodology of the research was established. Chapters 4, 5, and 6 discuss the data, results, and interpretation for the future climate zone map, energy use based on selected variables, and comfort and energy use based on three ventilation modes. This chapter discusses specifically the new climate zone designations based on future weather files, demonstrates the feasiblity of the method with a case study of seven counties in California, and discusses a future US climate zone map. 4.1 New climate zone designations based on future weather files The future CDD and HDD of each county in California can be calculated based on the future weather file, which contains the necessary data include dry bulb temperature and precipitation. Based on the IECC method that defines each different zone, it is possible to predict future climate zone for each county (Table 4.1) Year County Average Temp CDD HDD Precipitation ASHRAE Climate zone(Calculated) ASHRAE Climate zone(From ASHRAE) 2014 Iyon 58 3684 4052 2.067 4B 4B 2050 63 5150 3063 0 3B 2014 Imperial 74 8852 842 2.262 2 2 2050 79 10517 428 0 1 2014 Sacramento 60 4553 2586 0.546 3B 3B 2050 67 6148 1795 0 3B 2014 Shasta 62 4873 2694 72.3 3B 3B 2050 69 6896 1871 0 2 2014 Siskiyou 54 2589 5498 6.9 5 5 2050 59 4228 3927 0 4 2014 Tulare 63 4924 2464 11.8 3B 3B 2050 59 7103 1525 0 2 2014 LA 63 4724 1186 0 3B 3B 2050 68 6662 459 0 2 2014 Modoc 48 1933 6457 9.048 5 5 2050 55 3429 5077 0 4 2014 Bakersfield 65 5869 2013 1.833 3B 3B 2050 55 7823 1351 0 2 2014 Concord 59 3959 2487 20.787 4B 3B 2050 65 5899 1656 0 3B 2014 Crescent 52 983 4893 31.863 4A 4A 2050 56 2508 3179 0 4A 2014 Daggett 67 6866 1929 0.936 3B 3B 2050 74 8849 1257 0 2 2014 Fresno 64 5541 2327 1.053 3B 3B 2050 71 7760 1465 0 2 2014 Hayward 59 3635 2328 5.3 3B 3B 2050 63 5344 1424 0 3B 2014 Lake Tahoe 44 1057 7848 6.08 6 3B 40 2050 50 2365 5913 0 5 2014 Lemoore Reeves 62 5040 2590 2.262 3B 3B 2050 68 6931 1735 0 2 2014 Modesto 62 4934 2211 8.736 3B 3B 2050 68 6852 1423 0 2 2014 Napa 56 2779 3181 12.987 4 3B 2050 62 4645 1991 0 3B 2014 Oxnard 60 3646 2016 0.9 4 3B 2050 65 5735 890 0 3B 2014 Palm Springs 73 8404 1054 3.78 2 3B 2050 79 10381 537 0 1 2014 Paso Robles 59 3982 2556 3.7 4 3B 2050 64 5658 1691 0 3B 2014 Red Bulff 62 4988 2781 16.4 3B 3B 2050 68 6732 2009 0 2 2014 Salina 56 2771 2857 10.14 4 3B 2050 61 4497 1634 0 4 2014 San Diego 64 5197 1019 3.7 3B 3B 2050 69 6959 370 0 2 2014 San Jose 59 3712 2304 10 4 3B 2050 64 5370 1447 0 3B 2014 Santa Ana 63 5001 1127 6.3 3B 3B 2050 68 6963 423 0 2 2014 Santa Maria 56 2805 2796 1.6 3B 3B 2050 61 4546 1536 0 3B 2014 Santa Rosa 57 3164 2861 22 4B 3B 2050 62 5105 1829 0 3B 2014 SF 57 2934 2737 8 3B 3B 2050 62 4641 1573 0 3B 2014 Stockton 61 4613 2494 11 3B 3B 2050 67 6464 1653 0 2 2014 Travis Fld 60 4494 2713 16 4B 3B 2050 66 6292 1954 0 3B 2014 Ukiah 58 3740 2985 22 3B 3B 2050 63 5515 2060 0 3B 2014 Yuba 62 4781 2532 11 3B 3B 2050 67 6652 1771 0 2 2014 Blue Canyon 52 2676 5179 46 4B 5 2050 57 4155 4134 0 4B Table 4.1. The future climate zone for each county is calculated, and most of the counties are shifted to other climate zones In Chapter 2, section 2.1.5, the importance of the weather file and its role in building energy simulation has be described. Generally, climate zone is used decide the building construction type and building system requirements. The climate zone let designers to know what is the U-value for that specific project 41 and what kind of system the project should use. Therefore, to determine all these variables, it is necessary to understand the climate zones. Since the future HDD and CDD is different in the future, it will definitely affect the building energy use in the figure when the outside temperature keep changing. It is necessary to understand how the building energy performance will be like in 2020s, 2050s and 2080s., is this case, only the time slice of 2050 will be analysed. The climate condition for most of the counties will be totally different in the future, and therefore, the IECC climate change should be different as well. It is necessary to re-organize the climate zone map so that the decision made based on climate one can match the correct weather condition. However, not every county will have a climate condition in the future, some counties will stay the same. The preliminary test was for the counties in California as it has several climate zones. Based on the calculation results, not all the counties will be shifted to another climate zone. The climate change is not dramatic enough to change the HDD and CDD, which is the metric to determine which climate zone each county will be considered. Totally, there are 29 counties in California will have a climate zone change for 2050 (Table 4.2) Table 4.2. From the table, it is clear that every county would be shifted from a colder climate zone to a warmer climate zone, which is caused by the global warming effect according to the IPCC prediction. Based on the calculation and future climate zone prediction results, it is possible to visualize the future comfort zone map so that it can give a direct understanding that how the climate zone will be shifted in the future ( Figure 4.1 and Figure 4,2) 4B 6 4B 3B 3B 3B 5 3B 2 2 2 3B 4B 4B 1 2 3B 3B 3B 3B 4B 3B 2 2 3B 2 5 4 3B 4B 4 3B 2 3B 3B 4 4B 3B 2 3B 3B 2 3B 2 4B 3B 2 1 3B 2 5 4 3B 4 4 3B 2 3B Travis Fld Ukiah Yuba Santa Ana Santa Maria Santa Rosa SF Sktckton Paso Robles Red Bulff San Diego San Jose Lemoore Reeves Modesto Napa Oxnard Palm Springs Fresno Hayward Lake Tahoe Tulare LA Modoc Bakersfield Concord Iyon Imperial Shasta Siskiyou 42 Figure 4.1 Current ASHRAE climate zone map in California, totally there are 4 kind zones in California, which are climate zone 2,3,4,5 Figure 4.2 Future ASHRAE climate zone map in California for 2050s, some counties are moved from one climate zone to another 43 4.2 Creating the US map – future work 4.2.1 How to choose what weather file to use It has bee discussed in Chapter 3, the development of current climate map is based on each county, it can be see that each border of climate zone is also the boundry of each county. If the HDD and CDD of a county can be caclulated, the future climate zone map can be developed. However, it is critical to select a weather file that is representative enough for the county to perform the calculation. The way how to decide the weather file is to select a city first, an then a closest weather station will be selected. The TMY weather file provided from the weather station will be used to perform the calcautlion for HDD and CDD. Other method to select weather file are as followings: pick a radom city and use its weather file, this way is less realiable because it is unknown if the selected city is representative enough to cover all the other weather files in this county; pick a city that is in the middle of the county, this is a geological related way, it assumes that the city in the center of the county will contain the most trustable weather data that can cover all the cities in that county; take all the weather file from the county and calculate the average hourly weather variables, this would be the best way to select a proper weather file to analyze, however, the only problem with this method is that it is difficult and time-consuming to calcualte the average temperature from the data in all the weather files which are available in that county; or select the city that has the most sufficient weather data, not all the weather has all the weather data that we need, it is possible to check if the data in weather file is sufficient enough so that it can be reliable. 4.2.2 Important assumptions in the HadCM3 climate tool The future weather file generate tool will be used to produce future weather file. The created file can be opened as CSV format, which can be used to calculate futue HDD and CDD, based on the dry bulb temperature. However, if the future weather file generator will be used, it is important to understand its assumptions. The weather file generator is based on the HadCM3 model, which is a climate change model adopted by IPCC. Based on the location coodinates provided from the original weather file from weather station, the tool will find out where the city is and decide which grid block the city is located in, and then a specific climate change scenario will be applied for that block to calculate the future weather file variables. HadCM3 assumes the change from year to year is linear. The linear process might not be that accurate and resonable, but it is the only choice so far to study the future weather condition. It should be noted that there are other climate change models available that can be used to predict the future climate condition. It is possible to use other climate change models, becides HadCM3 model. If other climate change model is used, the only different would be the future weather, since different model will predict different future weather condition. Therefore, only the weather file that will be used would make a different, the methodology for other parts of the research would be the same. And the results would also be expected to be different when other cliamte change models is used. 4.2.3 Use of GIS for the final map GIS maping is a method to present the calculated data for future weather condition. GIS maps are used to hold, interpret, and display data that is geographically related. The development of future climate map will depend on the software ArchGIS, since it is a tool that allow user to input the data to generate future climate map which will be colored by the software itself. A workflow might be like this: the names of all 44 the weather files and their counties are loaded in an Excel spreadsheet. A script reads these, feeds them into the future weather file tool, puts the new data back into Excel. There the HDD and CDD calculations take place and a new climate zone is documented for each county. The county name and climate zone are fed into a map of the US in ArcGIS to display the data. This can be repeated for any of the future years. 4.3 Conclusion Generally, it is possible to calcualte the future climate map if the future weather file is provided. The only needed variables are dry bulb temperature. and precipitation. Having known that the climate zone map will be different in the future, it can be noticed that future building energy use will also be different. Therefore, it is necessary to understand the furture building energy use under the effect of climate change. 45 Chapter 5. Future Energy Use Based on Selected Variables In Chapter 3, the methodology of the research was established. Chapters 4, 5, and 6 discuss the data, results, and interpretation for the future climate zone map, energy used based on selected variables, and comfort and energy use based on ventilation modes. This chapter discusses specifically the building energy use for medium office building. An overall understanding of the climate change’s impact on building energy is provided, and the performance of energy efficient design strategies in the future was also analyzed. The variables discussed are window wall ratio (WWR), SHGC, and overhang depth. 5.1 Future Building energy consumption overview 5.1.1 Case study of seven counties in California When the cities from low latitude to high latitude in California are selected, the energy simulation can be performed to understand the future energy use in each places. Each county has different climate change degree. Counties in low latitude will experience an energy use increase, however, cities in high latitude will have an energy use decrease (Figure 5.1). Figure 5.1 Energy use change in the future for 7 counties in California, the data shows that for counties in low latitude, the energy use tend to increase, however, for counties in high latitude, an energy use decrease can be seen By changing the weather file, the building energy use for 2020s. 2050s and 2080s is different compared with the energy use in current condition. The percentage of energy use difference is from place to place, for certain cities, the heating and cooling energy use can be increased by about 40%, however, for certain places, a more than 10% heating and cooling energy use decrease can be seen (Figure. 5.2). 0 100 200 300 400 500 600 700 Current 2020s 2050s 2080s Heating and cooling energy use (Mbtu) Life cycle cooling and heating energy use Imperial Los Angeles Tulare Inyo Sacramento Shasta Siskiyou Year 46 Figure 5.2. The heating and cooling energy use can reflect the difference of climate change in each selected location in California The biggest building energy use increase is predicted to happen in Los Angeles County; the energy use will increase more than 40%. The heating and cooling energy consumption for Los Angeles will be increased by 40% until 2080s.s However, in some places where is usually cold in California, the building energy use is actually decreasing since the global warming will help to reduce heating energy use in the winter. For each selected county, a different amount of energy use change in the future can be seen. The red cylinder represents the building energy use increase by 2080s, the blue cylinder represents the building energy use decrease by 2080s. Most of the selected counties are located in different grid squares, which are from HadCM3 grid map (Figure 5.3). This is based on the extreme case predictions, which is climate change scenario A1B1 among 4 the HadCM3 scenarios which has been described in Chapter 3. -20% -10% 0% 10% 20% 30% 40% 50% 60% 2020s 2050s 2080s 2020s 2050s 2080s 2020s 2050s 2080s 2020s 2050s 2080s 2020s 2050s 2080s 2020s 2050s 2080s 2020s 2050s 2080s Imperial Los Angeles Tulare Inyo Sacramento Shasta Siskiyou Percentage of energy use change Percantage of change of cooling and heating energy use Cooling&Heating Energy 47 Figure 5.3. For these seven counties, for the cities located in low latitude of California, the building energy use increase can be seen, however, cities in high latitude will experience building energy use decrease. 5.1.2 Future building energy use across the U.S by HadCM3 model The HadCM3 model is gridded 2.5 ° in latitude and 3.75 ° in longitude, which is approximately 165 miles by 187 miles (Hulme et al. 2002). Based on the HadCM3 climate change model, the entire U.S is divided by the grid lines and each cell formed by four grid lines can be considered as an independent location that has a unique local climate change scenario. An estimate of the future building energy use by a medium office type of building located in each block will provide an overall understanding of the effect of climate change’s impact across the entire U.S. In total, the U.S can be divided into 100 independent blocks, and a representative weather file will be selected for each block to perform the building energy simulation for 2020s, 2050s and 2080s (Fig. 5.4). Each grid cell has been labelled. From high latitude to low latitude, the blocks are numbered from A to J, and low longitude to high longitude, the blocks are numbered from 1 to 15. Figure 5.4 Grid cell numbers, each cell is given a code to make it easy to find the location of the city that will be studied Since there are approximately 1020 weather stations across the U.S that have a TMY weather file, there will be more than one weather file in some blocks and no weather files in some of the blocks. It is important to select a representative weather file to perform building energy simulation. 48 There are several methods that could be used to choose an appropriate weather file. As previously described in section 4.2.1, this method used for the weather file selection contains two steps: 1. Selection of the most representative weather file in the block. In this case, the way to select most representative weather file is based on the selected city. Once the most representative city in the county is selected, the weather file of that city would be used for analysis. For example, in Block G14, Los Angeles will be selected as the most representative city. 2. Selecting the nearest weather station (weather file) for each of the selected city For example, to select a weather file that can represent others for block G14, the city of Los Angeles would be selected as a representative city at first (Fig XXX5.5.), and the weather station that closest to Los Angeles would be selected at the next step. In this case, it is Los Angeles International Airport Weather Station, whose location is 33.93 N for latitude and -118.4 W for longitude with the 90 feet elevation, the weather file provided from this weather station is “72297 CA Los Angeles IPA. Epw” (Figure 5.5). Figure 5.5 Selection of the city of each block, Los Angeles is selected for on specific block and selection of the weather station that is nearest to the selected city Once the weather file is determined, the next step will be performing a building energy simulation for the case study medium office building in each block. The selected time slice includes current, 2020s, 2050s, and 2080s, which means the simulation will generate the result for each of these time four slices (Table 5.1). The emission scenario selected in this case is the A2-Medium High Emission scenario, which can be generated from the future weather file generator. Other scenarios will be not considered in this case since it is time-consuming to generate one more scenario, for each weather file, at least 20 minutes to generate a future weather file. The most time-consuming part it the conversion between files’ different format; it takes long time to convert a file from epw version to csv version and once convert them back once the calculation is done. Climate related energy use (Mbtu) Current 2020 2050 2080 Los Angeles 238.332 267.368 307.136 369.768 Miami 609.202 668.991 741.044 859.208 San Antonio 484.209 527.778 566.798 651.179 49 Houston 484.209 527.778 566.798 651.179 Orlando 497.131 539.636 588.414 671.215 El Centro 541.099 579.191 622.283 693.221 New Orleans 537.007 573.052 607.14 677.219 Phoneix 546.779 578.404 621.174 686.443 Jacksonville 504.544 532.331 568.172 632.832 Tallahassee 510.26 537.861 569.64 636.293 Baton Rouge 557.725 583.086 604.773 659.243 Tucson 493.591 510.726 536.083 583.84 Waco 612.262 629.074 640.354 682.648 Hattiesburg 524.449 538.374 551.429 590.642 San Angelo 551.237 564.869 571.976 603.882 Bakersfield 427.644 438.2 459.757 494.392 Birmingham 534.386 544.852 554.773 592.717 Midland 501.342 509.41 518.599 546.111 Wilmington 514.5 521.556 541.176 574.605 Columbia 542.837 549.646 563.022 596.254 Little Rock 658.754 664.844 666.071 687.544 Virginia Beach 509.121 513.147 530.436 553.177 T or C 455.375 458.752 470.626 497.051 Lawton 586.002 589.829 591.919 616.043 Las Vegas 499.566 501.996 522.144 561.49 Nashville 602.079 603.559 608.079 628.236 San Jose 300.812 301.505 315.249 337.262 Las Cruces 503.958 503.831 507.16 525.73 Atlanta 559.232 558.902 565.128 589.981 Knoxville 599.908 597.091 602.269 619.101 Greensboro 587.42 584.568 592.822 613.108 Hutchison 664.734 659.031 655.086 653.096 Lubbock 625.887 619.954 612.279 617.102 Tulsa 736.41 727.949 718.023 715.19 Jonesboro 815.549 802.474 784.642 778.891 Oklahoma City 757.159 744.398 726.178 714.078 Sacramento 413.973 406.881 418.499 435.204 St. Louis 723.381 705.47 688.272 664.655 Kansas City 802.817 782.79 761.845 730.308 Mitchell 2173.701 2116.1 2067.749 1977.717 Kennewick 615.501 598.659 600.635 588.472 Springfield 805.406 782.657 762.671 728.819 Charleston 673.748 654.529 648.97 642.315 Washington 617.289 599.645 595.026 586.686 Omaha 890.894 860.605 833.225 771.903 Grand Island 881.237 849.466 816.869 762.669 50 Reading 692.636 666.927 650.644 625.317 Chicago 805.42 775.347 747.342 698.022 Dalhart 675.406 650.184 624.061 604.322 Indianapolis 858.441 826.207 800.17 759.59 Rhinelander 1116.897 1074.868 1039.938 963.687 Spokane 739.346 709.805 697.623 664.214 Siskiyou 558.063 535.67 527.316 514.945 Grand Forks 1322.008 1266.927 1228.697 1145.848 Grand Junction 682.713 654.247 635.704 609.488 Portland 453.093 434.038 439.078 437.512 CraigCarig 946.69 905.916 867.35 814.544 Fort Wayne 929.077 888.504 850.748 787.882 New York 682.387 651.786 633.932 607.041 Dickinson 1284.143 1225.303 1186.283 1105.154 Bismarck 1287.374 1227.703 1188.063 1106.163 Devile Lake 1222.647 1165.581 1125.262 1042.434 Reno 634.814 604.498 590.035 568.836 Bozeman 994.197 946.223 908.166 843.519 Missoula 867.984 825.888 796.798 744.791 Denver 686.8 653.47 616.112 580.263 Milwaukee 965.971 918.632 879.648 798.985 Augusta 998.896 949.489 905.376 834.538 Dallas 806.523 766.548 722.671 682.439 Baker City 794.486 755.096 729.63 687.791 Boston 729.667 693.272 665.89 614.055 Pittsburgh 673.39 639.498 617.967 588.968 Fargo 1208.933 1148.015 1112.009 1032.721 Minneapolis 1085.82 1030.983 991.271 901.889 Sioux Falls 1071.083 1016.973 976.235 889.943 Eugene 508.904 483.082 475.571 462.668 Greeley 816.351 774.521 728.348 680.425 Grand Rapids 939.24 891.112 850.059 778.371 Billings 862.308 817.769 790.535 738.101 Mountain Home 753.924 714.637 685.539 643.743 Williston 1062.679 1007.27 972.859 897.213 Casper 912.414 863.902 825.666 764.855 Moab 681.192 644.888 622.414 599.809 Rapid City 914.822 865.322 826.886 763.003 Duluth 1157.264 1093.622 1051.947 961.154 Idaho Falls 959.277 905.751 866.101 804.313 Harve 1052.623 993.649 955.952 880.414 Santa Fe 625.88 590.701 564.746 544.262 Glasgow 1100.042 1037.953 1000.019 921.28 51 Rochester 886.923 835.826 804.248 739.801 Winnemucca 704.394 662.21 636.084 601.667 Kalispell 932.951 876.936 850.532 793.623 Millinocket 1019.487 957.153 908.444 824.762 Salt Lake City 660.787 619.308 592.294 561.291 Albany 869.167 813.485 775.169 715.638 Cedar Rapids 1033.568 961.443 961.443 698.414 Cedar City 671.241 624.191 598.066 575.604 Elko 806.194 749.67 711.896 661.767 Seattle 462.479 427.25 416.597 386.647 Flagstaff 698.807 639.637 599.296 557.064 Table 5.1 Future energy use The future climate change related energy use, which mainly includes the heating and cooling energy use, for medium office type of building in different locations across the U.S. The calculated value of each block represent the climate related energy use during a year, for which the unit is Mbtu/Year. Each cell is color coded based on their energy use, from 600 Mbtu/Year to 1000 Mbtu/year to 1400 Mbtu/Year, the cell will be colored as blue, from 1000 Mbtu/Year to 1400 Mbtu/year, the cell will be colored as yellow, and from 1400 Mbtu/Year to 2000 Mbtu/Year, the cell will be colored as orange (Figure 5.6, 5.7 and 5.8). And the value can be displayed on the U.S. map that has been divided based on the HadCM3 climate change model. Figure 5.6 Energy use for medium office in each block in 2020s, most places in U.S has energy use lower than 1000 Mbtu/year. 52 Figure 5.7 Energy use for medium office in each block in 2050s, the number of city that will have energy use more than 1000Mbtu/year decrease Figure 5.8 Energy use for medium office in each block in 2080s, even though more cities have energy use less than 1000 Mbtu/year, the cities in the south are experiencing a lot energy use increase 53 The future energy use will be different from place to place. In 92 HadCM3 blocks, one city is selected for each of them, an energy simulation will be performed for the medium office building in each city. And the percentage of energy use increase and decrease is calculated to show the significance of the impact of climate change in each city, it can be noticed that for climate zone 1 and 2, there will be an energy use increase, for climate zone 3 and 4, some places will have energy use increase and for other places, there will be an energy use decrease, for cities in climate zone 5 to 7, there will be energy use decrease in 2020s, 2050s and 2080s (Figure 5.9, 5.10, 5.11). Figure 5.9 For most of the place, there will be an energy use decrease, but for the rest, an energy use increase will be seen until 2020s (See Appendix A). Figure 5.10 Until 2050s, the percentage of locations that will be experiencing energy use will increase, and there will be less cities which will have energy use decrease (See Appendix A). 54 Figure 5.11 More and more places will have an energy use increase Until 2080s, the number of locations that will be experiencing energy use keep increasing, and the percentage of energy use increase will also increase by a lot, the highest will reaches more than 50% (See Appendix A).. The US map can be divided in to 90 small blocks and each block can be studied individually. This is the highest resolution to study the climate change’s impact on building energy use in US so far. The metric used to evaluate the significance is Climate Change Energy Index- CCEI, and he map will be inserted with data and represent the CCEI, the positive value represent an energy use increase and the block will be marked as red. The green represent a negative value of CCEI which means energy use decrease in a certain year in the future (Figure 5.12). Figure 5.12 The US map divided by HadCM3 model, each block is experiencing energy use increase (red) or decrease (green) in 2020s. 5.1.3 Los Angeles, San Antonio, and Miami – CCEI (Climate change energy index) Calculation Totally, there are about 32 blocks from HadCM3 model will experience energy use increase in the future. It is necessary to understand the difference between each block. From the 32 blocks, 32 cities are selected, and the highest energy use increase happens in three cities, which are Los Angeles, Miami and San Antonio. Therefore, these three cities would be good examples to study detailed energy use under the influence of climate change. 55 Each block that will experience use increase are marked as red. And each of them is given a code to easily make the comparison. The code is a combination of numeral letter and an alphabetical letter. For example, the block in which Los Angeles is located is coded as C1 (Figure 5.13). Figure 5.13 Each black in the south US are marked as red since they will experience energy use increase and they are coded for easy comparison The selected three cities in south US are Los Angeles, San Antonio and Miami. They have very distinct climate conditions. By comparing them, a better understanding of the significance of climate change in different location can be acquired (Figure 5.14, 5.15, 5.16). Figure 5.14 The CCEI for San Antonio until 2080s will be 0.34, which is the lowest among the three cities that has the highest energy use increase. 56 Figure 5.15 The CCEI for Los Angeles until 2080s will be 0.55, which is higher than it is in San Antonio. The climate change’s impact on Los Angeles is more significant. Figure 5.16 The CCEI for Miami until 2080s will be 0.4. 5.2 Solar heat gain coefficient (SHGC) To have an energy efficient building with the climate change predicted by the HadCM3 model, it is necessary to come up with some strategies to reduce the heating and cooling load. One strategy that has been widely accepted by the designers is to use low SHGC glass, which can reduce the solar solar radiation entering the building. SHGC is a value that determine glass’s ability to transmit solar radiation; the higher SHGC it is, the high ability the glass has to transmit solar radiation. Generally, it is desirable to have a low SHGC since it can help to reduce the cooling load from solar during the summer. The selected cities for studying the impact of varying SHGC values are different from the selected cities for understanding the building energy use across the entire U.S. Los Angeles and Boston are selected since 57 they have very different climate conditions. For Los Angeles, usually a lower SHGC is more desirable; however, for Boston, a higher SHGC is more desirable since it can reduce the heating load in the summer. Therefore, Los Angeles and Boston are the two cities will be tested in this case. ASHRAE 90.1 requires a maximum SHGC as 0.25 for cities in Climate zone 3, in which Los Angeles is located. However, it is still unknown, in the future, if the 0.25 will stay as effective as it is under current climate. More SHGC value is tested and building heating and cooling energy use has been calculated. Since Los Angeles is already in hot climate, the cooling consumes a lot more energy than heating does. While the temperature and SHGC increases, the increase of cooling energy is a lot more than decrease of heating energy use. Therefore, building life cycle use will also increase when SHGC becomes higher (Figure 5.17). Figure 5.17 The comparison of SHGC selection in Los Angeles, However, for Boston, ASHRAE 90.1 requires a maximum SHGC as 0.4 in climate zone 5. It is more tricky to select a right SHGC if climate change is considered. Since a high SHGC is more beneficial since it can help to transmit solar radiation as much as possible so that the heating load in the winter in Boston can be largely reduced. However, it is more beneficial to use low SHGC in the future, since when temperature increases, a low SHGC is more desirable. A value that can balance the trade-off of heating and cooling energy use should be found out. Normally, a building can last for about 70 years, which will be in 2080s from now, therefore, based on the “Low” emission scenario from HadCM3 model. Considering climate change and building life energy use, 0.7 should be the best SHGC value for medium office in Boston, it is 15 percent less than energy when ASHRAE 90.1 standard value is selected, this is important since ASHRAE 90.1 is revised every 3-4 years, and every time it will requires an 15% energy efficiency improvement compared to the latest version. (Fig. 5.18). MBtu 58 Figure 5.18 0.7 should be the best SHGC value for medium office in Boston It is necessary to use local climate change scenarios, and understand how they are different from each other and how they will affect building energy use in the future. The selection of solar heat gain coefficient depends on a building’s location. Assuming no changes to the building over time, the SHGC is a fixed value. Often this value is defined in codes and standards. However, this value might not be the best value if one considers the changing climate. Generally, for the places that are very hot and experiencing a lot of solar radiation, a lower SHGC is always desirable since it can help to reduce the amount of energy used to offset the cooling load. However, the selection of SHGC in cold place should be higher since it can help passive heating in the winter. Once climate change is considered, the selection of SHGC becomes even more different. Since some places which is relatively cold under current climate condition will have a potential to become a warm place, and absorbing different amount of solar radiation, the selection of SHGC can be different in the future. Therefore, the best way to select SHGC is to calculate the building life cycle energy use by testing different values of SHGC. The SHGC that will result in a least energy consumption should be the one that needs to be selected. In this case, for Los Angeles, it is always a lower SHGC will have a lower building energy use, not matter it is annual energy consumption or life cycle energy consumption. However, for Boston, it is not realistic to use the SHGC recommended from the standard since the weather will become hotter in the future and the amount of absorbed solar radiation is also different, which means it is better to use a higher SHGC for the current weather, but it is better to keep the SHGC low since the temperature is getting warmer. Finally, the selection of SHGC depends on the total energy use of a building’s life cycle, a typical building’s life span is between 50 years to 70 years, which has been covered by the generated future weather data . KBtu SHGC 59 5.3 Overhang and its impact on building energy performance in the future Selecting the proper SHGC for minimum life cycle energy consumption is one strategy. Another strategy is to consider the use of overhangs on the building (Figure 5.19). Figure 5.19 The medium office building will be installed with overhang, and the depth of overhang will change from ) ft to 10 ft. 5.3.1 The selection of the optimal overhang depth for three locations Before doing the analysis, it is important to select an overhang depth for each city for 2014 current year. The selection of best depth for each city is based on if how much energy will be consumed when a certain overhang depth is selected. The selected three locations are Los Angeles, Miami and New York since each of them has a unique local weather conditions. The selection of a proper overhang depth is based on the energy use. An annual energy consumption simulation is carried to determine the best overhang depth for each of the three locations. The intent is to pick a “best case” scenario for the selection of the overhang for the current year to see how it works in the future. In addition, all different overhang depth are analysed from current year to 2080s condition, and it will find out the most beneficial overhang depth for a certain location during a building’s life cycle. For Los Angeles, the overhang depth is tested from 1 ft to 10ft8 ft, for Miami the selected overhang depth would be from 1 ft to 10 8 ft, and for New York, the selected overhang depth would be from 1 ft to 10 8 ft. For each location, the overhang depth has different effect on building energy use. For Los Angeles, the building energy use goes down from 1 ft overhang to 5 ft overhang and then goes up again when overhang depth is more than 5 ft, therefore 5 ft is the most beneficial overhang depth under the current condition (Figure 5.20, 5.21, 5.22) 60 Figure 5.20 In Los Angeles, 5 ft is the most beneficial overhang depth under the current year, 2014condition In Miami, the overhang can block solar radiation, from 1 ft overhang to 9 ft overhang, the energy use goes down a lot and become more stable from 7 ft, which means after 7 ft, a deeper overhang will no long have huge impact on building energy use (Fig. 5.21). Figure 5.21, In Miami, the chosen depth is 9’ In New York, it is more desirable to use less overhang to take the advantage of passive heating in the winter. Therefore, 1 fr ft (or no overhang) will be the best scenario for the medium office building in New York (Fig. 5.22). 61 Figure 5.22 In New York, no overhang is best. Each city shows a curve to represent the energy use based on different overhang depth. For Los Angeles, the best overhang depth should be 5 ft when the window high is about 4 ft. For Miami, the best overhang depth would be 8 ft or 9 ft, and the different between 8ft and 9ft is not too big, and there is no practical consideration involved in this case, the selected value will still be 9ftit will block most of the solar radiation in the summer and largely reduce the cooling load for the building. However, it is better to keep a minimum overhang depth for the buildings in New York since it is beneficial to have nothing to block the passive solar heat gain in the winter so that the heating load can be minimized as well. 5.3.2 The study of overhang depth from 0 ft to 10 ft under different climate change scenario In order to find out the most beneficial length of the overhang during a building’s life cycle, it is necessary to test each different length from no overhang to 10 ft so that an overall understanding of the best overhang depth can be studied. The metric used to evaluate overhang depth is the heating and cooling, since both of them can be influenced by the climate (Table 5.2, 5.3). Table 5.2 Simulation results for different overhang depth in Los Angeles, Miami and New York, overall, 132 simulations have been performed. 0 ft 1 ft 2 ft 3 ft 4 ft 5 ft 6 ft 7 ft 8 ft 9 ft 10 ft Current 1267.653 1262.766 1259.591 1257.7 1256.741 1256.54 1256.573 1257.087 1257.232 1257.876 1257.606 2020s 1296.689 1291.155 1287.326 1284.786 1283.164 1282.415 1281.959 1282.047 1282.004 1284.322 1282.107 2050s 1336.457 1330.482 1326.178 1323.198 1321.134 1319.997 1319.997 1318.954 1318.773 1322.298 1257.606 2080s 1399.089 1392.703 1259.591 1384.426 1381.88 1380.255 1378.989 1378.363 1378.007 1382.917 1377.653 Current 1638.523 1638.522 1629.81 1627.275 1626.085 1625.13 1624.921 1624.593 1638.522 1624.269 1638.522 2020s 1698.312 1698.312 1689.367 1686.7 1685.334 1684.186 1683.88 1683.457 1638.522 1624.269 1698.312 2050s 1770.365 1770.365 1760.912 1758.001 1756.394 1755.006 1754.612 1754.113 1770.365 1753.705 1770.365 2080s 1888.529 1888.529 1878.627 1875.493 1873.692 1872.086 1871.61 1871.046 1888.529 1870.624 1888.529 Current 1711.678 1711.361 1713.313 1717.953 1723.31 1727.94 1732.205 1736.376 1738.684 1740.855 1742.443 2020s 1681.071 1711.361 1680.769 1684.404 1688.864 1692.78 1732.205 1699.965 1701.994 1703.825 1705.117 2050s 1663.207 1661.025 1661.057 1663.783 1667.431 1670.712 1673.864 1676.919 1678.806 1680.431 1681.533 2080s 1636.306 1711.361 1631.103 1632.251 1634.492 1636.781 1639.155 1641.456 1643.076 1644.354 1645.135 Timeslice City Los Angeles Miami New York Total (Mbtu) 62 Figure 5.3 To better understand climate change’s impact, it is clear to only look at the climate related energy use, which in this case is mainly heating and cooling energy use. 5.3.3 Future building energy use with the overhang The results showed the annual energy use in different location, based on the optimal depth of the overhang for each location, the energy use was simulated for the future climate in 2020s, 2050s and 2080s. To understand the different of using and not using overhang, a comparison has been made for cities, Los Angeles, Miami, and New York. The metric used to compare is energy use during a year, which has the unit of MBtu/Year. Color has been used to more clearly compare the results. Green represents the cooling energy use and red represents the heating energy use. The darker the green or red is, the higher the value is, which means the more energy is used (Table 5.7, 5.8, 5.9). For medium office in Los Angeles, no matter 5 ft overhang or no overhang is applied, the cooling energy use will increase when the temperature keep increasing, and the heating energy use will be decrease in the future (Fig. 5.7). Since Miami and Los Angeles are all in relative warm area, the application of overhang has similar effect, which can be seen in the table above, where the cooling energy increase in the future and heating energy will decrease in the future. The only difference is the desirable depth of overhang (Fig. 5.8). Generally, it is not desirable to use overhang in New York, since it can be seen in the table that no matter in current condition or in the future, the heating and cooling energy for not overhang applied building is always lower than the overhang applied building, even when the overhang is only 1 ft deep (Fig. 5.23 and 5.24) Figure 5.23 For medium office in Los Angeles, no matter 5 ft overhang or no overhang is applied, the cooling energy use will increase. Figure 5.24 Since Miami and Los Angeles are all in relative warm area, the application of overhang has similar effect, which can be seen in the table above 0 ft 1 ft 2 ft 3 ft 4 ft 5 ft 6 ft 7 ft 8 ft 9 ft 10 ft Current 330.653 325.766 322.591 320.7 319.741 319.54 319.573 320.087 320.232 320.876 320.606 2020s 359.689 354.155 350.326 347.786 346.164 345.415 344.959 345.047 345.004 347.322 345.107 2050s 399.457 393.482 389.178 386.198 384.134 382.997 382.997 381.954 381.773 385.298 392.342 2080s 462.089 455.703 452.255 447.426 444.88 443.255 441.989 441.363 441.007 445.917 440.653 Current 701.523 701.522 692.81 690.275 689.085 688.13 687.921 687.593 701.522 687.269 701.522 2020s 761.312 761.312 752.367 749.7 748.334 747.186 746.88 746.457 755.146 752.571 761.312 2050s 833.365 833.365 823.912 821.001 819.394 818.006 817.612 817.113 833.365 816.705 833.365 2080s 951.529 951.529 941.627 938.493 936.692 935.086 934.61 934.046 951.529 933.624 951.529 Current 774.678 774.361 776.313 780.953 786.31 790.94 795.205 799.376 801.684 803.855 805.443 2020s 744.071 745.278 743.769 747.404 751.864 755.78 759.67 762.965 764.994 766.825 768.117 2050s 726.207 724.025 724.057 726.783 730.431 733.712 736.864 739.919 741.806 743.431 744.533 2080s 699.306 695.735 694.103 695.251 697.492 699.781 702.155 704.456 706.076 707.354 708.135 Climate Related (Mbtu) Los Angeles Miami New York City Timeslice Energy Source Overhang Current 2020s 2050s 2080s Without Overhang 169.96 214.61 264.821 337.227 5 ft overhang 186.836 232.76 283.874 357.446 Without Overhang 149.963 131.134 118.464 106.275 5 ft overhang 144.277 127.39 116.043 105.103 Cooling Energy Heating Energy Energy Source Overhang Current 2020s 2050s 2080s Without Overhang 558.198 626.677 706.481 831.612 9 ft overhang 575.343 643.893 724.379 850.174 Without Overhang 129.524 119.826 110.62 102.37 9 ft overhang 126.639 117.88 109.446 101.815 Cooling Energy Heating Energy 63 Figure 5.25 Generally, it is not desirable to use overhang in New York, since it can be seen in the table that no matter in current condition or in the future, the heating and cooling energy for not overhang applied building is always lower than the overhang applied building 5.3.4 The impact of overhang on building energy consumption and its effectiveness in the future The selection of overhang depth depends on location, it is necessary to run a sensitivity to find out what’s the most beneficial overhang depth. Therefore, fore Los Angeles, Miami and New York should use the different overhang size. The energy simulation took 10 iteration to find out the best depth for each city, the iteration which has the lowest annul heating and cooling energy use will be selected. The building will be used for evaluation is ASHRAE prototype medium office building. For Los Angeles, best overhang depth would be 5 ft, for Miami, it would be 9 ft and for New York the value is 1 ft (Table 5.10). Table 5.4. 5 ft is the best overhang depth for Los Angeles; 9 ft is the best overhang depth for Miami and 1 ft is the best overhang depth for New York 5.3.5 Selection of optimal overhand depth to minimize the life cycle energy use The sensitivity study for the current year shows that the overhang has a big impact on building energy use since it can block amount of solar radiation, which can help to reduce building energy use in the hot places but increase building energy use in the cold places like New York. From sensitivity study for the year 2014, it shows that the best depth of overhang in Los Angeles should be 5 ft, the best depth of overhang in Miami should be 9 ft, and for New York, it is best not to use any overhang, since it will reduce the amount of solar radiation in the winter and ultimately increase the heating energy consumption (Figure 5.26). Energy Source Overhang Current 2020s 2050s 2080s Without Overhang 196.184 225.607 265.258 324.661 1 ft overhang 521.979 466.59 413.402 333.916 Without Overhang 198.736 229.154 270.063 331.389 1 ft overhang 575.343 643.893 724.379 850.174 Cooling Energy Heating Energy City Selected overhang type Los Angeles 5 ft Miami 9 ft New York 1 ft 64 Figure 5.26. From the life cycle point of view, the optimal value for the overhang depth should be 8 ft Comparing the energy use analysis that’s only considering the current climate condition, the optimal overhang depth is different, since under the changing climate, the annual energy use in the future will be different when different overhang depth is selected. Therefore, a better way to select overhang depth is to analyze the life cycle energy use so that it can make sure that the building is using least energy throughout its life cycle. If selecting overhang depth based on current condition, the optimal value should be 5 ft; however, once the climate change is considered, the optimal value should be 7 ft (Figure 5.27). Figure 5.27 If only the current year condition is considered, the optimal overhang depth is 5 ft. For Miami, it is not it is not true that the longer the overhang is, the more energy it will save. In this case, the selected overhang depth is from 0 ft to 10 ft. The results show that 7 ft overhang will have a higher building energy use during its life cycle. (Figure 5.28). 450 455 460 465 470 475 480 485 490 0 ft 1 ft 2 ft 3 ft 4 ft 5 ft 6 ft 7 ft 8 ft 9 ft 10 ft Life Cycle EUI L I F E C Y C L E E U I V E R S U S O V E R H A N G D E P T H ( L O S A N G E L E S ) 65 Figure 5.28. When the life cycle energy use is considered, the optimal value for the overhang depth in New York is 7 ft. However, the climate change is not considered, the best value for overhang depth would be different since it totally depends on the current climate condition. 7 ft as overhang depth should be selected if the climate change is considered and the optimal depth would be 9 ft, which is the deepest overhang among the iteration, if only the current weather is analyzed (Figure 5.29). Figure 5.29 If only the current year energy use will be considered, the best value of overhang depth should be 9 ft. 980 985 990 995 1000 1005 1010 1015 1020 0 ft 1 ft 2 ft 3 ft 4 ft 5 ft 6 ft 7 ft 8 ft 9 ft 10 ft Life Cycle EUI L I F E C Y C L E E U I V E R S U S O V E R H A N G D E P T H ( M I A M I ) 66 For New York, if only the current year is considered, the best depth for the overhang would be 1 ft, however, if the life cycle energy use and climate change is considered, the actual most beneficial value for overhang depth would be 2 ft. (Figure 5.30). Figure 5.30. The best value for the overhang depth for New York is about 2 ft if the life cycle energy use is considered Figure 5.31. The best value for the overhang depth for New York is about 1 ft if only the current year is considered 865 870 875 880 885 890 895 900 905 910 0 ft 1 ft 2 ft 3 ft 4 ft 5 ft 6 ft 7 ft 8 ft 9 ft 10 ft Life Cycle EUI L I F E C Y C L E E U I V E R S U S O V E R H A N G D E P T H ( N E W Y O R K ) 67 5.4 Conclusion The selection of overhang depth is very critical to the building energy use. It depends on different cities and the different weather conditions. Overall, for Los Angeles, 8 ft deep overhang would be the best for buidling life cycle energy use; For Miami, this value would be 7 ft; For New York, when the overhang deep is 2 ft, it would save the most of building life energy use. When pointing out the optimum over depth, it should be noted that there is no any practical consideration has been involved in this analysis ( Table 5.5). Table 5.5. The selection of overhang depth is different when life cycle energy use is considered Los Angeles Miami New York Overhang depth-Current 5 ft 9 ft 1 ft Heating and Cooling energy use (Mbtu) 319 687 718 Overhang depthClimate change considered 8 ft 7ft 2 ft Life cycle heating and cooling energy use (Mbtu) 1488 3185 2938 68 Chapter 6. Future Comfort and Energy Use Based on Ventilation Modes In Chapter 3, the methodology of the research was established. Chapters 4, 5, and 6 discuss the data, results, and interpretation for the future climate zone map, energy used based on selected variables, and comfort and energy use based on ventilation modes. This chapter focuses on ventilation. The ventilation modes discussed are natural ventilation (two windows – same wall, cross ventilation, and stack ventilation) mixed mode ventilation, and mechanical ventilation. Their effects on user comfort are also discussed. 6.1 Natural ventilation: Two windows, same wall; cross ventilation; and stack ventilation The selection of ventilation plays an important role in occupants’ thermal comfort. For example, the application of natural ventilation depends on the outdoor air temperature, it is very relevant to the surrounding environment. However, if the global warming keeps happening, natural ventilation will brings hot air from outdoor environment to interior space, which works against its original purpose. Therefore, it is really important to use future weather to simulate the effectiveness of each selected strategies to make sure that they will still perform well. The calculation of the hours in the comfort is based on the occupied time, and analysis period is only during the summer in this case, since the study mainly focuses on analysing ventilation’s performance as a cooling strategy. Three different types of ventilation are selected to fully understand the ventilation type’s impact on building energy use and space thermal comfort in the future. The three case studies were two windows on the same wall; cross ventilation, and stack ventilation (Figure 6.1). Figure 6.1. The three ventilation types are selected, which are ventilation through the two windows on the same wall, cross ventilation and stack ventilation 6.1.1 Two Windows on the same side As described in chapter 3, the adaptive comfort model will be used to analyse the effectiveness of natural ventilation. The thermal comfort zone in adaptive comfort model is different from the regular comfort zone, it consists of two lines, if the hours are within the two lines, it means in that hour, occupants will feel thermally comfortable, if the hour is outside of the zone, it means occupants will not be satisfied with the thermal comfort, the temperature might either be too high or too low. The percentage of the hours 69 in the comfort zone will be used to represent the effectiveness of the natural ventilation. The higher the percentage is, there will be more hours in the comfort zone. For Los Angeles, from current year to 2080s, there will be more hours stay in comfort zone during the occupied, for example, from current year to 2080s, the percentage of hours in the comfort zone would increase from 85% to 100%. For Miami, it is reversed, the percentage is actually decreasing from current year to 2080s. The same thing happens to New York (Table 6.1). Table 6.1 Percentage of the hours in comfort zone when the space is occupied, until 2080, a different percentage of comfortable hours can be seen compared to current year The comfort hours are plotted in the adaptive comfort model so that a general distribution of the hours will be understood better. The whole group of the comfort hours will be shifted to another location on the graph when the future climate changes. For each graph, all the hours during occupied time period will be plotted. If the hours are within the two lines that define the thermal comfort area, it means that hour is in the thermal comfort zone, on the other hand, if the hour is not included in the area within two lines, it means the temperature at that moment is either too high or too low to keep occupants thermally comfortable. The percentage of hours in the comfort zone will be calculated to represent the comfort level of the space, the higher the percentage is, the better thermal condition is. (Figure 6.2 to 6.4). Current 2020 2050 2080 M1 Los Angeles 85% 98% 100% 100% M2 Los Angeles 79% 95% 100% 100% M3 Los Angeles 79% 95% 100% 100% Medium Office Los Angeles 89% 99% 100% 100% M1 Miami 53% 38% 23% 10% M2 Miami 59% 43% 28% 14% M3 Miami 60% 44% 29% 15% Medium Office Miami 46% 30% 19% 10% M1 New York 74% 75% 69% 48% M2 New York 72% 75% 68% 51% M3 New York 72% 75% 68% 51% Medium Office New York 73% 73% 66% 45% Current 2020 2050 2080 Two openings on one side (Los Angeles) 85% 98% 100% 100% Cross Ventilation (Los Angeles) 79% 95% 100% 100% Stack Ventilation (Los Angeles) 79% 95% 100% 100% Medium Office (Los Angeles) 89% 99% 100% 100% Two openings on one side (Miami) 53% 38% 23% 10% Cross Ventilation (Miami) 59% 43% 28% 14% Stack Ventilation (Miami) 60% 44% 29% 15% Medium Office (Miami) 46% 30% 19% 10% Two openings on one side (New York) 74% 75% 69% 48% Cross Ventilation (New York) 72% 75% 68% 51% Stack Ventilation (New York) 72% 75% 68% 51% Medium Office (New York) 73% 73% 66% 45% 70 Figure 6.2. For natural ventilation (M1), more and more hours will be in the comfort zone from current year until 2080s. For Miami, fewer hours will be located in the comfort zone defined by adaptive comfort model Figure 6.3. The natural ventilation’s (M1) effectiveness in Miami will be getting worse when the outdoor temperature increase because of the global warming, less percentage of hours will be in comfort zone until 2080s. For New York, the number of hours located in comfort zone will also decrease, there is an increase of comfort hours from current year to 2020s. However, after 2020s, there is a decrease of comfort hours since there is hotter climate. 71 Figure 6.4 Natural ventilation (M1) in New York will perform better until 2020s, however, if the temperature keep increasing, the percentage of hours in comfort zone will also decrease until 2080s. Generally, for the natural ventilation, only the medium office building in Los Angeles will benefit from the weather changes predicted. The reason for this is that currently the outdoor temperature tends to be too cold sometimes in the morning or at night, which will cause thermally uncomfortable for the occupants. With the increased temperature, there will be warmer morning and night, which will increase the total hours in the comfort zone. 6.1.2 Cross ventilation Adaptive comfort model is used again to plot all the occupied hours in the chart. The percentage of hours in the comfor zone is slightly different between cross ventilation and the ventialtion type which has two openings on the same wall. The general trend of the shift of comfort hours is similar (Figure 6.5 – 6.7) 72 Figure 6.5 The cross ventilation (M2) has the same effect as two openings on the same wall in Los Angeles, there will be a higher percentage of hours in the comfort zone until 2080s. Figure 6.6 Cross ventilation (M2) will have a very bed effect on the building that is using natural ventilation in Miami until 2080s. Almost more than half of the occupied hours are out of the comfort zone 73 Figure 6.7 Cross ventilation’s (M2) effect in New York will be better in 2020s, but it will still get worse until 2080s when temperature gets too high. For model 2, which is cross ventilation, it has higher percentage of comfort hours during the occupied hours in the summer, since the cross ventilation is more effective to introduce the air from one side and exhaust it from the other side of the space. 6.1.3 Stack ventilation Stack ventilation has one opening on each side of the space, one opening on once side is used to introduce the outdoor air to cool the space, and there is another opening in the chimney of the building. The thermal stratification plays an important role in this kind of ventilation, since the hot air tend to move to the top of the buidling and will be exhausted from the opening in the chimney. This effect will be really helpful to throughly ventilate the space and increase the percentage of the comfort hours (Figure 6.8-6.10). 74 Figure 6.8 For stack ventilation (M3), it has the similar effect as cross ventilation, and the overall trend is still that more hours will be in comfort zone until 2080s in Los Angeles Figure 6.9 For stack ventilation (M3), Miami will has the worst condition when by 2080s when the stack ventilation is applied to the space. Figure 6.10. For Stack Ventilation (M3), until 2080s, about half of occupied hours will be out of comfort zone in New York. In different location and for different type of natural ventilation, the comfort level is different. A general trend of the percentage of comfort hours can be seen from current condition to 2080s (Figure 6.11 to 6.13). 75 Figure 6.11. With the increased temperature in the future, the percentage of hours in the comfort zone will also increase until it reaches 100% Figure 6.12. The percentage of hours in the comfort zone will decrease significantly from current condition to 2080s. 0% 20% 40% 60% 80% 100% 120% Current 2020 2050 2080 Percentage of comfort hours P E R C E N TAG E O F H O U R S I N T H E C O M F O R T Z O N E ( LO S A N G E L E S ) Two openings on one side Cross Ventilation Stack Ventilation Medium Office 0% 10% 20% 30% 40% 50% 60% 70% Current 2020 2050 2080 Percentage of comfort hours P E R C E N TAG E O F H O U R S I N T H E C O M F O R T Z O N E ( M I A M I ) Two openings on one side Cross Ventilation Stack Ventilation Medium Office 76 Figure 6.13 As Miami, the percentage of hours in the comfort zone will also decrease largely when the climate is considered. Generally, the effectiveness of natural ventilation need to be further evaluated. It has a tendency to become worse for most of the places including New York and Miami. For some other places including Los Angeles, the natural ventilation will become even more helpful. 6.2 Mixed-mode ventilation When the outdoor temperature is higher than 78 or lower than 65, the natural ventilation will become detrimental since the hot or cold air will be introduced into the interior space. However, if the mixed- mode ventilation is used, the system can detect the unusual outdoor temperature and therefore turn on the system to mechanically ventilate the space, which will consume more energy but keep occupants to stay in the comfort zone. Again, the analysis period for the mixed-mode ventilation is also only in the summer which is June, July and August three months. For mixed-mode ventilation, it will consume energy since system will be operating when the outdoor temperature is not proper for natural ventilation. The darker the red is, the higher energy use it is. The darker the red is, more energy will be consumed in the HVAC system. (Table 6.2). Table 6.2 Mixed-mode ventilation, the darker the red is, the higher energy use. 0% 10% 20% 30% 40% 50% 60% 70% Current 2020 2050 2080 Percentage of comfort hours P E R C E N TAG E O F H O U R S I N T H E C O M F O R T Z O N E ( N E W YO R K ) Two openings on one side Cross Ventilation Stack Ventilation Medium Office Current 2020 2050 2080 Current 2020 2050 2080 Current 2020 2050 2080 M1 Los Angeles 78.70% 94.57% 99.89% 100.00% 1.55 1.80 5.42 9.73 1.97 1.90 5.43 9.73 M2 Los Angeles 92.17% 98.70% 100.00% 100.00% 1.27 1.69 5.42 9.73 1.37 1.71 5.42 9.73 M3 Los Angeles 93.15% 98.70% 100.00% 100.00% 2.35 3.31 10.82 19.47 2.52 3.35 10.82 19.47 Medium Office Los Angeles 99.35% 99.35% 98.48% 100.00% 0.69 1.17 3.62 5.28 0.70 1.18 3.68 5.28 M1 Miami 100.00% 100.00% 100.00% 100.00% 14.15 16.24 18.45 22.12 14.15 16.24 18.45 22.12 M2 Miami 100.00% 100.00% 100.00% 100.00% 14.15 16.24 18.45 22.12 14.15 16.24 18.45 22.12 M3 Miami 100.00% 100.00% 100.00% 100.00% 28.36 32.58 37.04 44.46 28.36 32.58 37.04 44.46 Medium Office Miami 100.00% 100.00% 100.00% 100.00% 5.97 6.27 6.54 6.82 5.97 6.27 6.54 6.82 M1 New York 99.78% 100.00% 100.00% 100.00% 6.33 7.81 10.07 13.86 6.35 7.81 10.07 13.86 M2 New York 99.78% 99.13% 99.67% 100.00% 6.07 7.68 10.02 13.85 6.08 7.74 10.05 13.85 M3 New York 99.67% 99.02% 99.57% 100.00% 12.65 15.62 20.18 27.83 12.69 15.78 20.27 27.83 Medium Office New York 100.00% 99.78% 99.89% 100.00% 3.20 3.92 4.56 5.45 3.20 3.93 4.56 5.45 Mixed Mode 77 When the system entirely relies on the mechanical ventilation, indoor air temperature can be kept at the set point, which in most case is about 75°F for cooling and 68°F for heating. Therefore, if the mechanical ventilation is used, the indoor temperature will mostly be in comfort zone if the system is properly sized. However, the energy use is for each system under mixed mode ventilation mode is different from city to city (Figure 6.14). Figure 6.14. The percentage of hours in the comfort zone will increase until it reaches 100% Mixed-mode ventilation is a strategy to keep people in comfort zone, and at same time the mixed-mode system uses less energy than mechanical ventilation. According to the results, mixed-mode ventilation can almost keep more than 95% of the total hours in comfort zone during the occupied time. For Los Angeles, only under current weather condition, there is only 78% of the total hours are in the comfort zone, which is even less than natural ventilated spaces in Los Angeles. For Miami, during the occupied time which is assuming from 8:00 am to 18:00 pm, the temperature is usually too high and the natural ventilation is not a proper strategy since it will introduce hot air into the space. Therefore, every hour during the occupied time will be mechanically ventilated. Therefore, 100% of the occupied will be in the comfort zone. In New York, no matter what kind of ventilation type is used and no matter which year it is, there will always be more than 99% comfortable hours. 6.3 Mechanical ventilation Mechanical ventilation will use most of energy, however, it will keep the occupants comfortable all the time when it is occupied. The analysis period is again during June, July and August three months, which is in summer. The mechanical ventilation can keep 100% of the occupied hours in the comfort zone, but it will consume more energy than the mixed-mode ventilation since the system is responsible for conditioning interior space all the time when needed (Fig. 6.3). 0% 20% 40% 60% 80% 100% Current 2020 2050 2080 P E R C E N T A G E O F H O U R S I N C O M F O R T Z O N E Two Openings on one side Cross Ventilation Stack Ventilation Medium Office 78 Figure 6.3: Comfort hours and energy consumption for mechanical ventilation It is clear that Miami will have the highest building energy use no matter which year it is, since the building is in a hot climate and more cooling energy use required from the HVAC system. If the building totally depends on the mechanical ventialtion, it will consume more energy than other ventilation types. 6.4 Summary of the ventilation type’s impact on building energy use For natural ventilation, it’s application depends on the location, in this case, it also depends on the future climate, since the changing temperature in the future will make ventilation not stable. For Los Angeles, natural ventilation will have a higher percentage of hours that are in the comfort zone. In addition, according to the ventilation type, it is also noticeable that Model 1, which is the ventilation type that has two openings on a same wall, which is M1 in the graph. There are two openings on one side can provide the highest percentage of the number of hours in the comfort zone. For all 4 models, the percentage of comfortable hours will be at 100 percent, for current year, the this percentage is about 80 to 90%, and it will go up until it reaches 100% by 2080s. (Figure 6.15 to 6.17). Figure 6.15 No matter what kind of ventilation type is used, it will finally reach 100% comfort hours until 2080s. Current 2020 2050 2080 Current 2020 2050 2080 Current 2020 2050 2080 M1 Los Angeles 100.00% 100.00% 100.00% 100.00% 5.65 7.87 10.11 12.82 5.65 7.87 10.11 12.82 M2 Los Angeles 100.00% 100.00% 100.00% 100.00% 5.72 7.84 10.11 13.17 5.72 7.84 10.11 13.17 M3 Los Angeles 100.00% 100.00% 100.00% 100.00% 6.88 8.84 10.74 13.31 6.88 8.84 10.74 13.31 Medium Office Los Angeles 100.00% 100.00% 100.00% 100.00% 8.52 9.77 11.13 12.97 8.52 9.77 11.13 12.97 M1 Miami 100.00% 100.00% 100.00% 100.00% 15.78 16.96 18.20 20.32 15.78 16.96 18.20 20.32 M2 Miami 100.00% 100.00% 100.00% 100.00% 17.28 17.28 20.90 23.71 17.28 17.28 20.90 23.71 M3 Miami 100.00% 100.00% 100.00% 100.00% 16.90 18.08 19.25 21.27 16.90 18.08 19.25 21.27 Medium Office Miami 100.00% 100.00% 100.00% 100.00% 16.90 18.08 19.25 21.27 16.90 18.08 19.25 21.27 M1 New York 100.00% 100.00% 100.00% 100.00% 8.82 10.44 12.69 15.89 8.82 10.44 12.69 15.89 M2 New York 100.00% 100.00% 100.00% 100.00% 9.14 11.18 13.83 17.54 9.14 11.18 13.83 17.54 M3 New York 100.00% 100.00% 100.00% 100.00% 11.67 13.35 15.99 19.47 11.67 13.35 15.99 19.47 Medium Office New York 100.00% 100.00% 100.00% 100.00% 11.98 13.05 14.76 16.96 11.98 13.05 14.76 16.96 Mechanical Ventilation 79 For Miami, since it is already in climate zone 1, which is hot during most time of a year, the temperature will be keeping increasing in the future and the natural ventilation will not be as effective as it is expected. However, if only one ventilation type can be selected, it should be either Model 1 (Two openings on the same wall) or Mode 2 (Cross ventilation). The medium office building will experience a very unsatisfied thermal condition in the future. Figure 6.16 In Miami, the percentage of comfort hours will decrease from current year For New York, it is a total different case. From current condition to 2020s, the percentage of comfortable hours is actually increasing, which means people will have more time under a comfortable indoor environment. However, until 2050s, this percentage deceases. The reason for this result is that under current condition, more people will feel uncomfortably cold in New York once natural ventilation is applied, once it gets warmer, fewer people will feet cold. However, if temperature keeps increasing until 2050, people will be experience more uncomfortable hours because of too hot. 80 Figure 6.17 New York will have energy use increase at first, but after 2020s, there will be an energy use decrease In general, both buildings in Los Angeles and Miami will have energy use increase in the future, and New York will experience energy use decrease. However, the energy consumption of mixed-mode ventilation must be higher than pure natural ventilation which does not consume any energy. The energy use is much lower than pure mechanically ventilated spaces (Figure 6.18). Figure 6.18 When the mixed-mode ventilation is applied, even though it is consuming a certain amount of energy, but it is a lot less than the mechanical ventialtion energy use 0 5 10 15 20 25 Current 2020 2050 2080 HVAC Energy use (MBtu) H V A C E N E R G Y U S E F O R E A C H T I M E S L I C E S Two Openings on one side Cross Ventilation Stack Ventilation Medium Office 81 Chapter 7. Future Work and Conclusion Other building energy efficient design strategies could be tested since most of them is tightly connected with the environment. It is necessary to select more strategies, like building orientation, to study its performance in the future. In addition, the future climate zone map is a big work for this study, but only a case study was carried in this cases since it is time-consuming to develop the future climate zone map for the entire U.S. Therefore, there is a lot of future work that can be carried out to continue this study. Basically, the analysis demonstrated that:  The current climate zone map does not apply to the future if the climate change is considered, it is necessary to predict future climate zone map  Climate change is different from place to place, the significance of temperature is also different in each location, which will finally cause a difference significance of the buidling energy use change. Genearlly, in south U.S, the buildings will have energy use increase and in north U.S, buildings will have energy use decrease.The selection of energy efficient strategies and variables should not be just follow the current year. For example, the depth of overhang for the medium office building in Los Angeles should be 8 ft rather than 5 ft if the life cycle energy use is considered, which should always be the case.  Different ventilation type will have different thermal comfort condition. The ventilation type that will provide highest percentage of hours in the comfort zone may have detrimental effect in the future. However, for other places, certain ventilation type will actually provide eveh higher percentage of comfort hours. 7.1 Test of more variables for energy consumption and thermal comfort The scope of this study only focuses on passive heating and natural ventilation, the parameters includes SHGC, WWR, overhang and different ventilation types. There are more strategies that are related with climate change can be studied. The following list strategies that can help to reduce building energy use and improve occupants’ thermal comfort:  Application of economizer  Building orientation  Cooling and heating set point  Surface to volume ratio  Insulation All the components listed above are common strategies designers will use to achieve low energy consumption of a building. However, those strategies are all also significantly related with climate. Once the climate changes, the effectiveness of certain strategy need to be re-evaluated. In addition, more there are other design strategies that could be tested to determine those that make the most sense for different climate zones. 82 7.2 Using different weather file to analyze the building future performance As described in previous chapters, the weather file selected for all the analysis is based on HadCM3 climate change model. Other climate models can be used. In addition, only temperature changes were used form the HadCM3 model. More sophisticated models could include changes in humidity, precipitation, wind velocity, etc. that would effect a building’s perfomration. However, the selection of weather file is not going to change the entire methodology. The workflow will still be the same in creating a new weather file. A better understanding of the effect from different climate change scenario could be acquired by trying multiple theories. 7.3 7.3 Providing strategies for future climate zones As described in chapter 4, the climate change will finally cause the shift of climate zones. Based on the analysis, it has been see that most of the California counties will shift from one climate zone to another, which has a higher CDD and lower HDD. Therefore, the strategies applied to each climate zone might be changed. A strategy that can keep the building high performance throughout its life cycle would be the most desirable strategy. In order to develop strategies for future climate zones, the first step would be develop the future climate zone map. Since three time slices are provided, which are 2020s, 2050s and 2080s, three different future climate zone map are expected to be created based on the calculation of the weather data. Once the future climate zone map is developed, it is possible to re-evaluate the strategies and provide better recommendations. For example, the best SHGC value for Boston analysed in this study is different from the one suggested from ASHRAE 90.1 if the climate change is considered. Other strategies would also have the same issue if the climate zone will shift in the future. 7.4 Analyzing building life cycle cost considering climate change The research so far still focuses on building energy use. However, realistically, the energy life-cycle cost of energy of a project is also important to the designers, engineers, and client since the team is always trying to lower the budget as much as possible. It is possible to take the climate change into account when calculating building life cycle cost. Once annual energy use for each year is calculated, the energy price can be taken to further calculate the building life cycle cost. One important thing is that the energy price would not be the same during a building’s life cycle. Electricity price and gas price would always being changed by different kind of factors. Therefore, it would be more complicated to calculate building life cycle cost. More scenario should be considered regarding to the energy price changes. It would be a challenge to analyse building life cycle cost when climate change is considered. 7.5 Conclusion A prediction has been done for the counties in California. Among all the counties in California, there are totally 29 counties will experience cliamte zone shift in 2050s, since most counties in California are 83 currentl in climate zone 4B, they will shift to climate zone 3B until 2050s. This methodology has proved that it is possible to find out the climate zone for each county in the future and the entire U.S can also be analyzed. Finally, the deliverables for this study would be a future climate zone map for 2020s, 2050s and 2080s. Overall, the buidlings in south part of the U.S will experience an energy use increase, since the higher future temperature will add more cooling load to the building. On the other hand, buildings in the centural and north U.S will experience an building energy usedecrease due to a lower heating load in the winter caused by the increasing temperature in the future. For the building future energy use, it has been demonstrated from this study that the selection of energy efficient startegies are very sensitive to the climate change, especially for the passive strategeis. From the study, both the selection of Solar Heat Gain Coefficient (SHGC) and overhang would be different once the climate change is considered. The SHGC value suggested from the code should be modified based on the future weather condition, and the best overhang that will have the lowest energy use should be re- evaluated. A better way to evaluate the performance of a energy efficient strategy should not be only for current year; a life cycle energy use should be more appropriate to demonstrate the effectiveness of the strategies, since the building energy use is diferent from year to year in the future when climate keeps changing. Once the climate change and life cycle energy use is considered, a better energy efficient strategy can be found and evaluated. For the thermal comfort, the application of ventilation type has been analyzed. For certain places, the performance of ventilation will get better in the future, for example, the application of natural ventilation in Los Angeles will provide 100% comfort hours by 2080s. However, for some places where is already very warm, the use of natural ventilation is not a good choice if the global warming is considered. For the city like Miami, it is not recommended to use natural ventilation since less than 10% of the occupied hours will be in the comfort zone in the future. The mixed mode ventilation can also keep occupants at a high comfort level, but it will also be affected by the cliamte since it parcially depends on the outdoor environment. When the cliamte changes in the future, the energy used in mixed-mode ventilation will increase due to a higher outdoor temperature, and the the change of comfort level depends on different places. 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Abstract (if available)

Abstract Buildings account for up to 40% of the total energy use and 72% of the total electricity consumption and have a strong negative impact on climate change in the world (SBCI 2009). The change in climate will also affect how buildings perform

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University of Southern California Dissertations and Theses

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Asset Metadata

Creator Chen, Yiyu (author)

Core Title Environmental adaptive design: building performance analysis considering change

School School of Architecture

Degree Master of Building Science

Degree Program Building Science

Publication Date 04/16/2015

Defense Date 03/23/2015

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

Tag building performance,climate change,Energy,life cycle analysis,OAI-PMH Harvest

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Kensek, Karen M. (committee chair), Choi, Joon-Ho (committee member), Collins, Greg (committee member), Schiler, Marc (committee member)

Creator Email chenyiyu2013@hotmail.com,yiyuchen@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-550825

Unique identifier UC11297685

Identifier etd-ChenYiyu-3315.pdf (filename),usctheses-c3-550825 (legacy record id)

Legacy Identifier etd-ChenYiyu-3315.pdf

Dmrecord 550825

Document Type Thesis

Format application/pdf (imt)

Rights Chen, Yiyu

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

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