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Occupant-aware energy management: energy saving and comfort outcomes achievable through application of cooling setpoint adjustments

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Occupant-aware energy management: energy saving and comfort outcomes achievable through application of cooling setpoint adjustments

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Content Occupant-aware Energy Management: Energy Saving and Comfort Outcomes Achievable Through Application of Cooling Setpoint Adjustments By Leluo Zhang Presented to the FACULTY OF THE SCHOOL OF ARCHITECTURE UNIVERSITY OF SOUTHERN CALIFORNIA In partial fulfillment of the Requirements of degree MASTER OF BUILDING SCIENCE May 2017 ii COMMITTEE Kyle Konis, Ph.D, AIA Assistant Professor USC School of Architecture kkonis@usc.edu Marc Schiler, FASES, LC Professor USC School of Architecture marcs@usc.edu (213)740-4591 Douglas Noble, Ph.D, FAIA Associate Professor USC School of Architecture dnoble@usc.edu (213)740-4589 iii Abstract Past studies have shown that many buildings have overcooling problems during the cooling season with the cooling temperature setpoints that are widely used in industry practice nowadays (Mendell, 2009). It not only causes a certain level of discomfort for building occupants, but also results in excess cooling energy consumption. Adjustment of temperature setpoints has proven to be one of the most practical and cost-effective methods to reduce space cooling and heating energy consumption (Hoyt et al., 2014). However, because of the concern of the impact that may have on occupant thermal comfort, application of temperature setpoints adjustments have been limited to the unoccupied period. In addition, with the development of participatory sensing technologies, one promising application is to base setpoints on actual occupant preferences or acceptance levels rather than static models. Therefore, further research about energy savings achievable through temperature setpoint adjustment and its’ impacts on occupant comfort is needed in order to broaden the application of this strategy The previous research has shown that more than 20% of annual HVAC energy reduction could be achieved by applying temperature setpoint adjustment in sealed, mechanically conditioned commercial buildings in California. Instead of single HVAC system type and ventilation strategy, this thesis explored the application of cooling setpoint adjustment for multiple cooling and ventilation configurations. Variable Air Volume (VAV) system and Dedicated Outdoor Air System (DOAS) were used to represent conventional and low-energy HVAC system. In addition to mechanical ventilation, mixed-mode ventilation were involved. Totally 17 climates were selected based on ASHRAE climate zones to cover both extreme and mild climates globally. iv Two category of charts were generated for assisting designers or building operators to make decisions during initial design or building operational period. The first set of charts provides information about total Energy Use Intensity (EUI) of baseline models and energy saving reductions for each setpoint adjustment in seventeen climates. The second set of charts presents trade-offs between occupant comfort outcomes and energy reductions through cooling setpoints in each climate. These results indicated the potential of reducing cooling energy consumption, maintaining or even improving occupant comfort level, in commercial buildings through adjusting cooling setpoints. v Acknowledgement I would first like to express my sincere gratitude to my thesis chair Prof. Kyle Konis for his continuous support of this thesis. His guidance always steered me in the right direction, and helped me in all the time of research and writing of this thesis. I would also like to thank the rest of my thesis committee: Prof. Marc Schiler and Prof. Douglas Noble, for their valuable comments and hard questions. Finally, I would like to thank my fellow classmates in the Master of Building Science program, School of Architecture, University of Southern California, for providing me with support and encouragement throughout my years of study and through the process of researching and writing this thesis. vi Hypothesis Parametric thermal energy simulation can be used to quantify the potential energy savings from cooling setpoint adjustments in global climates and for conventional and low-energy cooling and ventilation strategies. vii Table of Contents Committee………………………………………………………………………………………..ii Abstract………………………………………………………………………………………… iii Acknowledgement……………………………………………………………………………….v Hypothesis…………………………………………………………………………………….....vi Chapter 1. Introduction…………….…………….......………………………………….....…...1 1. Introduction ........................................................................................................................... 1 1.1. Problem ......................................................................................................................................... 1 1.2. Terminologies ............................................................................................................................... 4 1.2.1. Cooling temperature setpoint ................................................................................................ 4 1.2.2. Predicted Mean Vote (PMV) model ..................................................................................... 5 1.2.3. Adaptive Thermal Comfort (ATC) ....................................................................................... 5 1.2.4. Occupant-aware energy management ................................................................................... 6 1.2.5. Variable Air Volume (VAV) system .................................................................................... 7 1.2.6. Dedicated Outdoor Air System (DOAS)............................................................................... 8 1.2.7. Sealed building with mechanical ventilation ........................................................................ 9 1.2.8. Mixed-mode ventilation ...................................................................................................... 10 1.2.9. Parametric simulations ........................................................................................................ 11 1.3. Objective ..................................................................................................................................... 11 Chapter 2. Background and Literature Review……………..…………………………….....14 2. Background and Literature Review .................................................................................. 14 2.1. Cooling setpoints range from PMV model ................................................................................. 14 2.2. Cooling setpoints range from Adaptive Thermal Comfort (ATC) model ................................... 15 2.3. Potential of extending cooling setpoint range ............................................................................. 18 2.4. Occupant-aware energy management and Occupant Mobile Gateway (OMG).......................... 19 2.5. Energy savings through temperature setpoint adjustments ......................................................... 22 2.6. Energy saving potential of application of natural ventilation ..................................................... 23 2.7. Effects of occupants’ window opening behaviors in simulations ............................................... 24 2.8. Selections of simulation tools ..................................................................................................... 25 2.9. Energy saving potential of various combinations of cooling and ventilation strategies ............. 28 Chapter 3. Methodology………………….…………………………..………………………...31 3. Methodology ......................................................................................................................... 31 3.1. Simulation Tool: IES VE ............................................................................................................ 32 3.2. Simulation Baselines ................................................................................................................... 33 3.3. Parametric simulations ................................................................................................................ 39 3.4. Expected results .......................................................................................................................... 42 viii 3.4.1. Baseline EUI of 17 climates ................................................................................................ 42 3.4.2. Total EUI saving percentages relative to baseline cooling setpoint (72°F) ........................ 44 3.4.3. Percentage of discomfort hours relative to total EUI savings ............................................. 46 Chapter 4. Results………...………………………………………………………………….....48 4. Results ................................................................................................................................... 48 4.1. Baseline Total EUI ...................................................................................................................... 48 4.2. Total EUI reductions per system configurations ......................................................................... 53 4.3. Percentage of discomfort hours and total EUI reductions .......................................................... 57 Chapter 5. Discussion……………...…………………………………………...………………77 5. Discussion ............................................................................................................................. 78 5.1. Climates and percentage of discomfort hours ............................................................................. 79 5.2. System type and percentage of discomfort hours ....................................................................... 81 5.3. Ventilation mode and percentage of discomfort hours ............................................................... 84 5.4. Potential of increasing cooling setpoint ...................................................................................... 87 5.4.1. Conventional configuration ................................................................................................. 88 5.4.2. Mixed-mode configuration ................................................................................................. 91 5.4.3. DOAS configuration ........................................................................................................... 93 5.4.4. DOAS + mixed-mode configuration ................................................................................... 95 Chapter 6. Conclusion and Future Work………...…………………………………………...98 6. Conclusion and Future Work ............................................................................................. 99 6.1. Conclusion .................................................................................................................................. 99 6.2. Future work ............................................................................................................................... 101 Bibliography…………………………………………………………………………………...101 ix List of Figures Figure 1. Observed indoor temperatures in 95 buildings relative to recommended thermal comfort guidelines (ASHRAE, 2005b) by season (Mendell, 2009). ...............................................3 Figure 2. VAV system diagram (Asbury et al., 2005). ....................................................................8 Figure 3. DOAS system diagram (Mumma and Jeong, 2005). ........................................................9 Figure 4. Concurrent mixed-mode ventilation operation (CBN, 2013). ........................................11 Figure 5. Physical building retrofit vs temperature setpoints adjustments (Hamilton 2011, Nest 2017). .............................................................................................................................................12 Figure 6 Acceptable operative temperature ranges for naturally conditioned spaces from ASHRAE 55 (ASHRAE, 2013) .....................................................................................................17 Figure 7. Design values for the indoor operative temperature for buildings without mechanical cooling systems as a function of the exponentially-weighted running mean of the outdoor temperature from EN-15251 (CEN/TC 251, 2014). ......................................................................18 Figure 8. View of the OMG user-interface following user assessment of thermal sensation (Konis, 2013). ................................................................................................................................20 Figure 9. Probabilistic model of thermal comfort generated by the OMG machine-learning module (Konis and Zhang, 2016). .................................................................................................21 Figure 10. Bullitt Center cooling and ventilation diagram (PAE Consulting Engineers, Inc, 2016). .............................................................................................................................................29 Figure 11. Methodology flowchart ................................................................................................32 Figure 12. Medium commercial building model and thermal zone. ..............................................34 Figure 13. Window control algorithm ...........................................................................................36 Figure 14. Macroflo opening type .................................................................................................37 Figure 15. Window control schedule and algorithm, set in Schedule Profile section ...................38 Figure 16. ApacheHVAC interface. ...............................................................................................39 Figure 17. Climates selected based on ASHRAE climate zones. ..................................................40 Figure 18. Parametric Batch Processor interface. ..........................................................................41 Figure 19. Energy consumption results showed in Vista Pro ........................................................42 Figure 20. Baseline EUI of four configurations of 17 climates. ....................................................43 Figure 21.Total EUI reduction percentages of conventional configuration with adjusted cooling temperature setpoints relative to baseline. .....................................................................................45 Figure 22. Percentage of discomfort hours relative to total EUI reductions-Phoenix climate. .....47 Figure 23. Baseline Total EUI of four system configurations of 17 climates. ..............................50 Figure 24. Total EUI reductions of conventional configuration. ...................................................54 x Figure 25. Total EUI reductions of mixed-mode configuration. ...................................................55 Figure 26. Total EUI reductions of DOAS configuration. .............................................................56 Figure 27. Total EUI reductions of DOAS + mixed-mode configuration. ....................................57 Figure 28.Percentage of discomfort hours relative to total EUI reductions-Miami climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). .............................................................................................................61 Figure 29. Percentage of discomfort hours relative to total EUI reductions-Riyadh climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). .............................................................................................................62 Figure 30. Percentage of discomfort hours relative to total EUI reductions-Houston climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). .............................................................................................................63 Figure 31. Percentage of discomfort hours relative to total EUI reductions-Phoenix climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). .............................................................................................................64 Figure 32. Percentage of discomfort hours relative to total EUI reductions-Memphis climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). .............................................................................................................65 Figure 33. Percentage of discomfort hours relative to total EUI reductions-Los Angeles climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). .............................................................................................................66 Figure 34. Percentage of discomfort hours relative to total EUI reductions-San Francisco climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). .............................................................................................................67 Figure 35. Percentage of discomfort hours relative to total EUI reductions-Baltimore climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). .............................................................................................................68 Figure 36. Percentage of discomfort hours relative to total EUI reductions-Albuquerque climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). .............................................................................................................69 Figure 37. Percentage of discomfort hours relative to total EUI reductions-Boston climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + xi mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). .............................................................................................................70 Figure 38. Percentage of discomfort hours relative to total EUI reductions-Chicago climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). .............................................................................................................71 Figure 39. Percentage of discomfort hours relative to total EUI reductions-Boise climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). .............................................................................................................72 Figure 40. Percentage of discomfort hours relative to total EUI reductions-Vancouver climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). .............................................................................................................73 Figure 41. Percentage of discomfort hours relative to total EUI reductions-Burlington climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). .............................................................................................................74 Figure 42. Percentage of discomfort hours relative to total EUI reductions-Helena climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). .............................................................................................................75 Figure 43. Percentage of discomfort hours relative to total EUI reductions-Duluth climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). .............................................................................................................76 Figure 44. Percentage of discomfort hours relative to total EUI reductions-Fairbanks climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). .............................................................................................................77 xii List of Tables Table 1. Baseline model configurations........................................................................................ 34 Table 2. Baseline Total EUI of four system configurations of 17 climates. ................................. 51 Table 3. Percentage of discomfort hours for conventional and DOAS configurations - Houston, Los Angeles, and Vancouver climates. ......................................................................................... 84 Table 4. Total EUI reductions and percentage of discomfort hours with cooling setpoint of 75°F - conventional configuration. .......................................................................................................... 89 Table 5.Total EUI reductions and percentage of discomfort hours with cooling setpoint of 75°F - mixed-mode configuration. ........................................................................................................... 91 Table 6.Total EUI reductions and percentage of discomfort hours with cooling setpoint of 75°F - DOAS configuration. .................................................................................................................... 93 Table 7. Total EUI reductions and percentage of discomfort hours with cooling setpoint of 75°F - DOAS + mixed-mode configuration. ............................................................................................ 96 1 Chapter 1. Introduction 1. Introduction 1.1. Problem In the industry practice, 72°F is commonly used as the cooling setpoint in commercial buildings. Temperature setpoint adjustment which deviates from industry practice is one of the most practical and cost-effective strategies for reducing space heating and cooling energy consumption (Hoyt et al., 2014), but its application is typically restricted to weekends and holiday periods, largely due to concern about how an adjustment may impact thermal comfort during the occupied hours. Field data from 95 office buildings in the U.S. Environmental Protection Agency’s Building Assessment Survey and Evaluation (BASE) Study showed that with the applications of temperature setpoints that are widely used in industry practices, buildings have an overcooling problem during the cooling season, and an overheating problem in the heating season (Mendell, 2009). Figure 1 summarizes descriptive data on thermal variables in the 95 buildings included in a previous study, and compared the observed temperature ranges to American Society of Heating, Refrigerating and Air-Conditioning Engineers Standard 55-Thermal Environmental Conditions for Human Occupancy 2 (ASHRAE 55) recommended comfort ranges by season. ASHRAE 55 is a standard provides the minimum requirements that needed to be achieved for acceptable indoor thermal environment. The solid lines in the figure represent the observed temperature ranges from surveyed buildings, and the dashed lines represent ASHRAE recommended temperature ranges. The median of mean building temperature observed in summer was 23.2°C and the range was from 21.6 to 24.8°C, while recommended comfort range for summer was from 23.2°C to 26.3°C, which was approximately 2°C higher than the observed range. Median and maximum indoor temperature observed in winter were around 0.5°C lower than those in summer, which evenly spanned the recommended range. Indeed, the data showed that increasing cooling setpoint and reducing heating setpoint could maintain occupant comfort in these 95 buildings (Mendell, 2009). Due to the setpoint changes, there existed a potential for energy savings in these 95 buildings. Thus, from the data shown and general observation, by adjusting cooling and heating setpoints to a higher and lower level respectively, there existed a possibility to achieve energy savings and still maintain occupant thermal comfort. However, Mendell’s research did not include quantified energy saving information or the associated impact on occupant thermal comfort. Consequently, this thesis will examine the relationship among setpoint, energy savings, and impact on occupant thermal comfort. 3 Figure 1. Observed indoor temperatures in 95 buildings relative to recommended thermal comfort guidelines (ASHRAE, 2005b) by season (Mendell, 2009). In addition, in practice, types of mechanical system and building ventilation strategies could have impacts on the effectiveness of setpoint adjustment in terms of energy saving and occupancy thermal comfort. Quantifying the energy saving and changes of occupant comfort of different types of systems is also essential to provide project team or building owners useful reference when making design or retrofit decisions. 4 1.2. Terminologies 1.2.1. Cooling temperature setpoint “Thermostatic temperature control systems consist of a thermostat plus some sort of heater and/or cooler. The thermostat is a thermally actuated switch that changes between the "on" and "off" state at a specific temperature, usually adjustable.” (Brengelmann, 2003). Temperature setpoints are the temperature at which the switch is activated. A Cooling temperature setpoint is the air temperature that the thermostat supposes to maintain during the cooling season, and a heating temperature setpoint is the thermostat minimum allowable temperature during the heating season. The temperature of a conditioned space is maintained in the range limited between cooling and heating setpoints to provide occupants a thermally comfortable indoor environment. For example, if the measured temperature is above the cooling setpoint, the cooler is turned on and the temperature falls. When the falling temperature passes the cooling set point, the thermostat switches the cooler off and the temperature rises passively. Eventually, it rises above the set point and the cycle repeats (Brengelmann, 2003). 5 1.2.2. Predicted Mean Vote (PMV) model P.O. Fanger developed the Predicted Mean Vote (PMV) model to define thermal comfort using heat balance equations and empirical studies. A combination of six parameters: air temperature, relative humidity, air speed, metabolic rate, and clothing insulation, is used to calculate Predicted Mean Vote (PMV). The PMV runs from cold (-3) to hot (+3), with a seven-point scale (Fanger, 1970). Zero stands for thermal neutrality, which is the ideal value, and the recommended range is suggested by ASHRAE Standard 55 as (-0.5<PMV<+0.5) (ASHRAE Standard 55, 2013). 1.2.3. Adaptive Thermal Comfort (ATC) Different from PMV method, the Adaptive Thermal Comfort (ATC) method follows the idea that occupants could adapt themselves to thermal environment in different times of a year to some extent. Therefore the assessment of occupant comfort using the ATC method is relative to the outdoor environment. Numerous researchers took surveys on building occupants worldwide about their thermal comfort while taking simultaneous environmental measurements (De Dear et al., 1988). Analyzed results showed that occupants of naturally ventilated buildings have a wider acceptable temperature range compared to occupants of sealed, air conditioned buildings, 6 which is because their preferences of temperature depends on outdoor conditions. Now the ATC method is suggested to assess occupant comfort in naturally ventilated space by ASHRAE Standard 55. 1.2.4. Occupant-aware energy management The term “occupant-aware energy management” in this thesis is a thermal management strategy to reduce energy consumption caused by overcooling. It is achieved by adjusting cooling temperature setpoints within thermal comfort setpoint ranges learned from building occupants’ feedback in previous studies. The previous research has shown that more than 20% of annual HVAC energy reduction could be achieved by applying occupant-aware energy management in sealed, mechanically conditioned commercial buildings in California. Energy saving achievable also showed differences regarding to various building vintage and climate zones across California (Konis and Zhang, 2016). The results from previous studies showed that occupant-aware energy management is an effective thermal management strategy to 7 minimize unnecessary HVAC energy use while maintaining thermally comfortable indoor environment. 1.2.5. Variable Air Volume (VAV) system A Variable Air Volume (VAV) was used to represent conventional HVAC system type in this thesis. A VAV system is a type of HVAC system that is commonly used in building nowadays. It supplies variable airflow at constant temperature (Airflow Exploration Center, 2014). Supply air fan speed varies according to space air temperature and setpoint temperature. A VAV terminal unit, called a VAV box, uses dampers to further control the airflow distributed to each thermal zone. The advantages of a VAV system include lower fan energy consumption and reducing compressor wear compared to constant air volume system (AAON, 2014). 8 Figure 2. VAV system diagram (Asbury et al., 2005). 1.2.6. Dedicated Outdoor Air System (DOAS) Dedicated Outdoor Air System (DOAS) was used to represent low-energy HVAC system in this thesis. DOAS system consists of two parallel systems, one is a dedicated system handling ventilation and part of space latent and sensible loads, another system is a parallel system that handles most latent and sensible loads of the space. There are many optional systems to accommodate for handling space loads such as VAV system and Fan Coil Unit (FCU), and the system used in the thesis is radiant ceiling panels. The radiant cooling ceiling panels remove most of the space cooling loads (only sensible loads) through radiation from their cool surfaces, and DOAS unit provides ventilation to the space as well as remove latent 9 loads and part of sensible loads. DOAS was proven to be an efficient HVAC system by previous studies. A study done in 2003 compared the energy performance of DOAS system coupled with radiant panels to a conventional VAV system for a 3,000 sf office space in an educational building in Pennsylvania. The results showed a 42% reduction of annual air-conditioning energy consumption (Jeong et al. 2003). Figure 3. DOAS system diagram (Mumma and Jeong, 2005). 1.2.7. Sealed building with mechanical ventilation Sealed building in this thesis stands for buildings without occupant-controllable openings on the building envelope, and no windows or openings access to outdoor is used for outdoor air ventilation in occupied spaces. Mechanical ventilation is the only ventilation method that is used in sealed buildings in the context. Mechanical ventilation uses fans to drive the flow of outside air into an occupied space (ASHRAE Handbook, 2005). It is usually integrated into the HVAC system. 10 1.2.8. Mixed-mode ventilation Mixed-mode ventilation refers to a hybrid approach to space conditioning that uses a combination of natural ventilation from operable windows (either manually or automatically controlled), and mechanical systems that include air distribution equipment and refrigeration equipment for cooling (CBE Berkeley, 2013). There are three types of mixed-mode ventilation operations include concurrent, change- over, and zoned operation. Concurrent mixed-mode operation allows operable windows and mechanical system operate at the same time in the same space (Figure 4). In change-over operation, mechanical conditioning system and operable windows not operate at the same time in the same space, whenever the mechanical system is on, and the operable window keeps close. In zoned operation, different zones within a building have different conditioning strategies, so that natural ventilation through windows and mechanical ventilation could exist at the same time, but in different space. Concurrent operation is used for mixed-mode ventilation in this thesis since it’s the most prevail operation in existing buildings, and “mixed-mode ventilation” is used in short for “mixed-mode ventilation with concurrent operation”. 11 Figure 4. Concurrent mixed-mode ventilation operation (CBN, 2013). 1.2.9. Parametric simulations Parametric simulation refers to a systematic method that simulates output by changing a single or multiple input parameters with a list of different values for each parameter. Using parametric simulation, a relationship between two or more inputs and outputs can be expressed or graphed. This thesis used parametric simulation to present the relationship between cooling setpoint vs. energy use intensity (EUI) reduction percentage and EUI reduction percentage vs. percentage of discomfort hours. 1.3. Objective The objective of this thesis is to quantify the potential of energy saving and comfort outcomes through the application of a series of cooling setpoint adjustments. Since building occupants mainly experience thermal discomfort in summer due to space overcooling as 12 indicated in previous research, this thesis is focusing on energy savings and occupant comfort in the cooling season. The relationship of the potential of energy savings and comfort outcomes from setpoint adjustments is explored by using three different models: “Adaptive Thermal Comfort (ATC)” model, “Predicted Mean Vote (PMV)” model from ASHRAE 55, and empirical models from previous studies which are generated by machine-learning techniques using occupant subjective data collected in commercial buildings. The results of this thesis are aimed at assisting in design the decision making process in the early design stage and in the building operation period of commercial buildings. The results can indicate the potential for energy savings through the application of thermal environment management strategies, also inform its advantages comparing to some relatively expensive and time consuming physical building retrofit methods (e.g. replacing glazing, adding additional building envelope insulation). Figure 5. Physical building retrofit vs temperature setpoints adjustments (Hamilton 2011, Nest 2017). 13 Besides sealed buildings equipped with conventional mechanical systems, buildings with other typical systems and ventilation strategies are involved in the thesis. A range of cooling setpoint adjustment scenarios are applied to commercial building models representing: 1) Sealed buildings with VAV system and mechanical ventilation 3) Buildings with VAV system and mixed-mode ventilation 4) Buildings with DOAS system 5) Buildings with DOAS system and mixed-mode ventilation Parametric building energy simulations are performed to quantify energy savings achievable through occupant-aware cooling setpoint adjustments to conventional cooling setpoint. Thermal comfort outcomes, which were generated by ASHRAE adaptive thermal comfort model and PMV model, are used as an additional metrics to differentiate various commercial building cases. 14 Chapter 2. Background and Literature Review 2. Background and Literature Review This chapter summarizes some completed past research that relates to this thesis and discusses the connections that research has with this thesis. The main themes addressed in this chapter include occupancy thermal comfort assessment, energy savings through setpoint adjustment, energy saving potentials through natural ventilation, and simulation tools. 2.1. Cooling setpoints range from PMV model According to previous research done by Hoyt et al., the commonly used HVAC thermostat temperature setpoint range for commercial buildings in practice is from 70°F to 72°F, in which 70°F is the heating setpoint, and 72°F is the cooling setpoint. When indoor temperature is within thermostat temperature range, neither cooling nor heating occurs. The comfort range defined by PMV method is from -0.5 to 0.5 (ASHRAE, 2013). However, this comfort range allows a temperature variation of 5.4°F, which is much wider than the thermostat setpoint range (2°F, from 70°F to 72°F) in typical practice (Hoyt et al., 2014). Based on ASHRAE 55 comfort range, it is possible to extend the thermostat 15 temperature range by extending the cooling setpoint temperature to a higher value, and the heating setpoint temperature to a lower value. Besides the setpoint temperature extension potential indicated by ASHRAE standards, some arguments claim that setpoint temperature range could be widened to assess occupancy comfort. As the PMV model is applied universally, and excludes unique circumstances of occupant, it is likely to lead to a narrower comfort temperature range than occupant may actually accept. Examination of the extensive ASHRAE RP-884 field study database has shown that indoor environments controlled to narrow temperature ranges do not result in higher occupant satisfaction than environments with wider temperature ranges (7°F to 10°F) (Arens et al., 2012). 2.2. Cooling setpoints range from Adaptive Thermal Comfort (ATC) model A study done by Nicol and Humphreys in 2009 suggested that adaptive standards should be use to assess occupancy comfort. Individual differences such as clothing and metabolic rate should be considered differently when assessing occupant comfort instead of using uniform factors in the PMV approach (Nicol and Humphreys, 2009). There are adaptive thermal comfort models in ASHRAE and European Standard (EN-15251) standards. The ASHRAE adaptive thermal comfort model is intended to be used only in naturally ventilated buildings with operable windows as a primary way to control indoor temperature 16 (ASHRAE, 2013), while the EN standard is applied in buildings not using mechanical ways to control indoor temperature (CEN/TC 251, 2014). Though the definitions are slightly different, they both show similar indoor thermally comfortable temperature trends which increase as outdoor running mean temperature increases (Figure 6 and Figure 7), and defines zones of 80% and 90% acceptability. The 80% acceptability limits are for typical applications, and it shall be used when other information is not available. The 90% acceptability limits may be used when a higher standard of thermal comfort is desired. The line which suggests the acceptable indoor temperature in adaptive thermal comfort model could be a guide to set acceptable indoor temperature. Such adaptive standards could benefit the design of low-carbon buildings in terms of less restriction on close controlled indoor thermal environment. Additionally it could eliminate the distinctions between naturally ventilated and mechanically conditioned buildings. (Nicol and Humphreys, 2009) 17 Figure 6 Acceptable operative temperature ranges for naturally conditioned spaces from ASHRAE 55 (ASHRAE, 2013) 18 Figure 7. Design values for the indoor operative temperature for buildings without mechanical cooling systems as a function of the exponentially-weighted running mean of the outdoor temperature from EN-15251 (CEN/TC 251, 2014). 2.3. Potential of extending cooling setpoint range A study investigated the relationship between “Building Related Symptoms” (BRS) and thermal metrics constructed from real-time measurements data from 95 office buildings in the U.S. Environmental Protection Agency’s Building Assessment Survey and Evaluation (BASE) Study (Mendell, 2009). BRS was defined if the following symptoms were experienced in a building at least one day per week during the last four weeks, and also improving when away from the building. The symptoms included: lower respiratory (at least one of wheeze, shortness of breath, or chest tightness); cough; upper respiratory (at 19 least one of stuffy or runny nose, sneezing, or sore or dry throat); dry, itching, or irritated eyes; fatigue or difficulty concentrating; headache; and dry, itching, or irritated skin. The results of the study showed that increasing cooling setpoint in summer and decreasing heating setpoint in winter were associated in a decrease of BRS mentioned above. In summer time, when the cooling setpoint was increased to ASHRAE recommended summer temperature range (23.2°C to 26.8°C), a reduction in both occupancy symptom and cooling energy consumption could be achieved. In winter time, lowering the heating setpoint within the ASHRAE recommended range (21.6°C to 24.8°C) could achieve a reduction in both occupancy symptom and cooling energy consumption, with no penalty in thermal comfort (Mendell, 2009). 2.4. Occupant-aware energy management and Occupant Mobile Gateway (OMG) In consideration of limitations in current commonly applied temperature setpoint practices, applying cooling temperature setpoint from occupant-aware temperature setpoint range is a better way to create acceptable thermal environment while saving energy. The “occupant- aware” temperature setpoint range is generated from a data-driven thermal comfort model at zone level using a software-based mobile sensing technology called Occupant Mobile Gateway (OMG). It enables users to report their thermal sensation and acceptability level in terms of indoor temperature and other indoor environment quality factors using smart phone based survey tool as shown in Figure 8 (Konis, 2013). 20 Figure 8. View of the OMG user-interface following user assessment of thermal sensation (Konis, 2013). The technology is able to pair occupants’ subjective assessments of thermal sensation with concurrent physical measurements of temperature and other indoor environment quality factors. The physical measurements data are collected through a physical sensor (Thermodo sensor (Robocat, 2014)) attached to the mobile device. A probabilistic model (Figure 9) is generated from subjective data obtained over a period of two weeks from 45 occupants working in four sealed, mechanical conditioned commercial office buildings located in Southern California. There were 1490 unique observations of thermal comfort been collected in total. The two curves represent 21 probability of occupants reporting “too cold” and “too warm” separately. Although the model is generated based on limited filed data and small group of occupants, it still shows a great potential of adjusting cooling and heating temperature setpoints from currently wide used setpoints. A discomfort level of 20% showing an acceptable temperature range from 68.2°F to 80.1°F (Konis and Zhang, 2016). Referring to the learned temperature range, cooling setpoint is possible to be adjusted up to approximately 80°F, while the commonly used cooling setpoint in the industry is 72°F. However, the probabilistic model did not include relative humidity, air speed, metabolic rate, and clothing insulation. Though compared to PMV model, this model considered unique circumstances, the exclusion of these factors indicates the limitation of this data-driven probabilistic model. Figure 9. Probabilistic model of thermal comfort generated by the OMG machine-learning module (Konis and Zhang, 2016). 22 2.5. Energy savings through temperature setpoint adjustments The potential of extent indoor temperature range indicated large potential of energy saving through setpoint adjustment. UC Berkeley Center of the Built Environment did research on energy saving through adjusting cooling and heating setpoint in VAV system. The results show that an average of 29% of cooling energy and 27% total HVAC energy savings are achieved by increasing cooling setpoint from 72°F to 77°F, and an average of 34% of terminal heating energy could be saved by reducing heating setpoint from 70°F to 68°F (Hoyt et al., 2014). The results trend was described in the paper as: “The benefit are cumulative, and small incremental changes to the setpoints result in proportional savings.”. Climates have effects on energy savings by increasing cooling setpoint or reducing heating setpoint. Hot climates benefited more from increasing cooling setpoint, while cold climates benefit more from reducing heating setpoint. Temperate climates showed greatest potential of energy savings through widen temperature (Hoyt et al., 2014). Hoyt also stated that types of mechanical system will have a large impact on energy savings resulting through adjusting temperature setpoints in practice. Konis and Zhang’s research (2016) showed similar results, while maintaining 80% occupancy satisfactions from the probabilistic thermal comfort model, cooling energy savings could be achieved from 19.9% to 68.3% depending on building vintage and 23 California climates in medium office building with cooling setpoint being extended from 75°F (DOE reference model cooling setpoint) to 80°F. Heating energy could be saved from 31.7% to 74.9% with heating setpoint being reduced from 70°F to 68°F. Pre-1980 building vintage showed greatest benefit resulting in setpoint adjustment in average, which suggested the great potential using this method in retrofitting cases. 2.6. Energy saving potential of application of natural ventilation Beside setpoint adjustment measures, application of natural ventilation could add on building energy savings. A report by the University of California, San Diego prepared for California Energy Commission (Linden et al., 2014) found out that application of natural ventilation and mixed-mode can provide buildings with both significant energy savings and occupant indoor thermal satisfaction. Energy savings through natural ventilation application when retrofitting offices buildings are roughly equal to a set of energy saving measures, such as reducing internal loads, adding façade insulation and adjusting setpoints, etc. Applying natural ventilation together with other energy saving measures could achieve even better energy performance. The research also presented that occupants in buildings with natural ventilation or mixed- mode ventilation showed satisfaction even when indoor temperature was 82°F, which is far beyond the comfortable temperature range ASHRAE suggested for mechanical conditioning and ventilation mode (Linden et al., 2014). One possible reason would be air 24 movement could reduce the “stuffiness” in warm conditions. It showed that occupants still gave satisfactory feedbacks when indoor temperature reached 82°F, and most discomforts reported are due to overcooling by mechanical system under mixed-mode mode. Another interesting point obtained from the Commercial End-use Survey data set is that between 7% to 9% of California buildings do not have mechanical ventilation or conditioning system at all (Linden et al., 2014). This finding indicated the possibility of applying free-running (operating mode only rely on natural ventilation) for certain period in a year in office buildings in similar temperate climates like California. Despite of comfort and energy saving benefits, involving natural ventilation as part of design or retrofit plan do have certain challenges and concerns. Outdoor air quality, climate suitability, acoustic performance, security and fire safety are all important factors to take account of when designing natural ventilation involved projects (Axley, 2001). For natural ventilation that primary rely on occupant controlled operable windows, occupants control behaviors also contribute to difficulties in design process. 2.7. Effects of occupants’ window opening behaviors in simulations Occupants’ window control behaviors are influenced by many parameters. Window opening behaviors are more likely to be influenced by indoor condition, and window 25 closing behaviors are more likely to be affected by outdoor conditions (Haldi, 2009). The impact of outdoor temperature could be significant with a correlation value of 0.7, and indoor temperature is mostly affect occupants’ window controlling behaviors in non- heating seasons (Roetzel et al, 2010). The window control also related to seasons, occupants are more likely to open or close the window in summer than in winter. The orientation of a window influences the window open pattern as well due to solar radiation and prevailing wind direction (Zhang, 2012). However, the research focus only on open and close of windows, not including factors that contribute to amount of outdoor air flow in and out of a building such as opening angles. When using simulation as an approach to study energy savings and thermal comfort performance of buildings including natural ventilation and window controls, properly set up parameters of windows and occupants control behaviors are of great importance. It is even more complicated in mixed-mode simulations. Cooperation between window opening patterns and mechanical system operations also need to be defined in the simulation programs. 2.8. Selections of simulation tools According to a project done by UC Berkeley, 2015, there are several main factors when selecting simulation tools when simulating natural ventilated or mixed-mode buildings. The critical differences of wind data obtained from weather station and on site, the model’s 26 sensitivity, clear instruction and guidelines, capability of setting up window opening behaviors, and ways of communicating the results are the main factors that should be considered when selecting tools for simulations. In the research done by Hoyt et al. in 2014, and the research done by Konis and Zhang in 2016, EnergyPlus (U.S. DOE, 2015) was used as the main simulation tool. The previous research used DOE reference commercial building model (Deru et al., 2011) as the baseline, and the reference model was created in EnergyPlus. Therefore, using EnergyPlus as the simulation tool saved the time for setting up the baseline model for previous research. No natural ventilation or mixed-mode ventilation was included in these two research. EnergyPlus is capable of setting up climate, construction, system, occupancy schedules, building energy management and control system, and other occupant behavior related scheduling. It generated detailed report including envelope performance, energy consumption, and indoor environment. The parametric simulations were generated from parametric simulation manager software which was built for Energy Plus entitled jEPlus (Zhang 2009, Zhang 2016). This software enables users to assign a set of different parameters in EnergyPlus files to automate parametric simulations. However, the interface of EnergyPlus creates difficulties when setting up some complex system (such as DOAS and cooling panels) and window operation schedules controlling by outdoor and indoor conditions. Practitioners in the interviews done by Gandhi et al in 2015 also cited the lack of an accessible interface and extensive run times as the reasons they not choosing EnergyPlus when carrying out simulations involving natural ventilation or mixed-mode ventilation (Gandhi et al., 2015). 27 A detailed demonstration of simulation works about natural ventilation and mixed-mode ventilation using EnergyPlus, Integrated Environmental Solutions Virtual Environment (IES VE) (Integrated Environmental Solutions Limited, 2015) proved the capability of this tool of simulating natural ventilated or mixed-mode buildings (Moore, 2013). Besides general functions that are similar with other energy modeling tools, IES VE enables users to set up window opening types and opening schedules in details. The project assign window type, opening angle, window control algorithm, and window opening schedule in high level details, which largely benefit the simulation accuracy (Moore, 2013). The project also shows how mechanical system could be corporate with window opening behaviors in order to simulate a more realistic system operations. According to the interviews a project team carried out, practitioners reported positively about IES VE’s user interface, accuracy of airside calculations, capability of running parametric simulations, level of detail and options offered, and clear and transparent ways of creating documentations (Gandhi et al., 2015). Other tools used in natural ventilation or mixed-mode ventilation simulations reported by practitioners included CONTAM (NIST, 2012) and Transient System Simulation Tool (TRNSYS) (TRNSYS, 2017). CONTAM was stated as a good numeric tool. However, it focuses on air quality and ventilation analysis, and it is not able to model energy consumption. The ability of TRNSYS to accept custom code was cited as a specific benefit. The lack of ability of performing parametric runs and accessing input or results as text files for scripting purposes were cited as two main limitations of the software (Gandhi et al., 28 2015). Some DOE-2 engine based simulation tools, such as eQuest (Hirsch, 2009) and Design Builder (DesignBuilder Software Ltd, 2016), also have the ability to simulate natural ventilation through openings. However, eQuest is only capable of simulation single infiltration and natural ventilation with simple timed schedule (not generated by indoor/outdoor conditions) (Crawley et al, 2005). Design Builder could simulate natural ventilation or mixed-mode ventilation in similar detailed level as IES VE, but IES VE provides user more options to analysis and to organize simulation outputs, such as calculating the percentage of occupied hours that in a certain temperature range. 2.9. Energy saving potential of various combinations of cooling and ventilation strategies Indoor thermal environment control strategies and ventilation strategies used in some high performance buildings could be used as a reference when carrying out simulations and design or retrofit works. The Bullitt Center located in Seattle is an example of adopting natural ventilation as an energy saving strategy. It combined Dedicated Outdoor Air System and natural ventilation to control indoor thermal environment and provide ventilation to occupants. Radiant floor was applied in each floor to deal with sensible loads in the space. Natural ventilation from windows and DOAS are used together for ventilation, and at the same time it deals with latent load and part of sensible loads. While natural ventilation from windows is the primary way for ventilation, the DOAS ensures that there are enough amount of outdoor air is supplied to the space. This high-performance building 29 suggests a possibility that applying setpoint adjustment measure in mixed-mode ventilation mode may achieve an even larger energy savings than just applying one single strategy. Figure 10. Bullitt Center cooling and ventilation diagram (PAE Consulting Engineers, Inc, 2016). Previous research has given discussions and conclusions on cooling energy consumptions, occupancy comfort, and potentials of energy saving through temperature setpoints 30 adjustments. Some of these findings point out that system configurations may have certain influence on the energy savings due to setpoint adjustments. However, since there are no research studies quantifying how setpoint adjustment strategies will perform under different ventilation modes, in different type of systems, and in different climates, a more comprehensive research involving all these four parameters is worthwhile to be done. By quantifying differences of energy savings through setpoint adjustment caused by the above factors could be valuable. Especially, it is beneficial for architects and engineers to refer to at an early stage of design or building operation period. 31 Chapter 3. Methodology 3. Methodology Two objectives of the methodology are 1) quantifying energy savings of buildings with various cooling and ventilation strategies in various climates, and 2) understanding the impacts of each parameter and their combinations having on air-conditioning energy consumption and occupancy comfort. Therefore, a method that can carry out large amount of simulations and that has the capability to organize simulation results is beneficial for this study. Parametric simulations were selected to be the main method due to their convenience when multiple building configurations and wide range of climates were involved. As stated earlier, this thesis was focused on cooling side of energy savings since occupancy discomforts was more common during cooling season than heating season. There were a total of 612 simulations including 17 climates, 4 cooling and ventilation strategies and 9 cooling setpoint scenarios. Figure 11Figure 11 shows an overview of the methodology, and the two charts are example of intended results, which will be mentioned in later chapters. 32 Figure 11. Methodology flowchart 3.1. Simulation Tool: IES VE IES Virtual Environment (IES VE) was selected as the tool for simulations due to its capability of simulating relatively complex window control and HVAC systems, and its superiorities in presenting and organizing results. ApacheHvac section allowed users to simulate relatively complicated HVAC systems, such as DOAS system. The Macroflo 33 section was capable of simulating natural ventilation through windows, and it was able to set up window control algorithm per varies indoor and outdoor environmental parameters. IES VE provided users with a variety of outcomes. Values of indoor environmental parameters, occupant comfort indicators, energy consumptions, and weather data could be found in simulation results. Simple result analysis and data visualization were made possible in Vista Pro section. For example, it allowed users to obtain percentage of hours that falls into certain occupant comfort range, which could be done by applying range function for hourly PMV values. 3.2. Simulation Baselines Four baseline models were determined in this study, each of which represented a different cooling and ventilation regime. These baselines were based on to Department of Energy (DOE) post-1980 medium-size reference commercial building model (Deru et al., 2011), which represented the most common type of commercial buildings nowadays. Except for cooling and ventilation configuration and cooling setpoint settings, other settings such as construction, schedules, occupancy, and internal heat gains were in compliance with DOE reference models. 34 Figure 12. Medium commercial building model and thermal zone. Every baseline differs from each other mainly in whether they used outdoor air as alternative cooling approach, or involved natural ventilation. Table 1 summarized cooling and ventilation strategy for baseline models. The conventional cases represented the most commonly applied cooling and ventilation type in office buildings - VAV system with mechanical ventilations. Mixed-mode configuration added operable windows to conventional case, which was aimed to determine the impacts of natural ventilation could have on energy and comfort outcomes. DOAS baseline had a dedicated system for delivering outdoor air, and a parallel radiant panel system to remove most of the sensible loads. Outdoor air through the dedicated air system was used to fulfill ventilation requirements and deals with part of cooling loads in the space. The final baseline model included DOAS system and mixed-mode ventilation which represented the energy efficient air-conditioning practice. Table 1. Baseline model configurations System Type Ventilation Method 35 Conventional VAV Mechanical Mixed-mode VAV Mixed-mode DOAS DOAS + Radiant panels Mechanical DOAS + mixed- mode DOAS + Radiant panels Mixed-mode The mixed-mode model included operable windows to allow natural ventilation. Window opening type was set with maximum opening angle of 20°. Operable windows were set to be automatically controlled according to indoor and outdoor temperature for simulation purpose. An algorithm was assigned to window operation control as shown in Figure 13. Windows would open when the following three conditions met at the same time: 1) Indoor air temperature is greater than 72; 2) Outdoor dry-bulb temperature is greater than 68; 3) Indoor temperature is at least 2° F greater than outdoor dry-bulb temperature. Schedules, control algorithm, opening types, and dimensions could be set in Macroflo Opening Types window and Schedule Profiles. The formulas show in Figure 15 enabled realistic window opening process with a dead band of 2 °F for the outdoor and indoor temperature control situations. 36 Figure 13. Window control algorithm 37 Figure 14. Macroflo opening type 38 Figure 15. Window control schedule and algorithm, set in Schedule Profile section Mixed-mode ventilation with concurrent operation was set for simulation cases including mixed-mode ventilation, which means operable windows and mechanical systems were allowed to be operated at the same time in the same space. Mechanical system operated when natural ventilation through operable windows could not provide enough cooling or fresh air for the space. The coordination of natural ventilation through window opening and mechanical systems were realized using ApacheHVAC section in IES VE. The ApacheHvac was designed for detailed HVAC system settings. It also provided users a way to control the systems using different types of logic controllers. Different conditions 39 and parameters were assigned to logic controllers to realize the coordination of natural and mechanical ventilations. Figure 16. ApacheHVAC interface. 3.3. Parametric simulations As shown in Figure 17, seventeen climates were selected based on ASHRAE climate zones. The average annual temperature span in a wide range from 77°F to 27°F, 40 involving both extreme and mild climates globally. The climates were sorted from the warmest to the coldest climates. Figure 17. Climates selected based on ASHRAE climate zones. Cooling setpoint was adjusted from 72°F to 80°F with a step of 1°F. Cooling setback temperature was always 8°F higher than setpoint temperature. The baseline cooling setpoint was referred to industry practice. Meanwhile, the upper limit of setpoint range, 80°F, was referred to learned setpoint range from previous research. The research collected 1490 observations of thermal comfort from 45 occupants in 4 commercial buildings in southern California during a period of two weeks. Based on collected observations and simultaneously measured thermal data, a probabilistic model was generated showing the probability of discomfort relative to indoor temperature. The model suggested that a cooling setpoint of 80.1°F could be achieved while maintain 80% of occupant satisfaction (Konis and Zhang, 2016). 41 Parametric simulations were done with assistance of IES VE Parametric Batch Processor tool. The tool enables user to queue up simulations after assigned ApacheHVAC file and opening types to each of the simulation. It also allowed certain level of automatic parametric simulations by select variable in the drug down menu, but with limited variable types. The variable function cannot associated to ApcheHVAC currently. Thus, a large amount of time was spent on manually assigning systems to each simulation file. Figure 18. Parametric Batch Processor interface. Energy consumption results and comfort outputs were obtained from the parametric simulations. The results were able to show in an organized way in IES VE Vista Pro section. Occupants comfort level outputs were exported in hourly data, and energy consumptions data were exported after being organized in monthly total and annual total formats. Outputs from different setpoint scenarios in same system and ventilation configuration were organized in the same chart in convenience of comparison within same cooling and ventilation configuration. 42 Figure 19. Energy consumption results showed in Vista Pro 3.4. Expected results Intended output of the parametric simulations and results analysis would be several summary charts, which organized energy and comfort outcomes for better interpretation. Figure 20 to Error! Reference source not found. are examples of expected results. Results will be interpreted and discussed in details in the following chapter. 3.4.1. Baseline EUI of 17 climates Figure 20 summarized total EUI of baseline models of 17 climates. There were 4 cooling and ventilation configurations under each climate. A different color 43 represented each climate. Red represented the warmest climate, and purple for the coldest climate. Total EUI includes cooling and heating, fan, lighting and equipment energy consumption. This chart was intended to indicate different energy performances of four baselines involved in this thesis. Figure 20. Baseline EUI of four configurations of 17 climates. 44 3.4.2. Total EUI saving percentages relative to baseline cooling setpoint (72°F) Figure 21 summarized total EUI savings of eight cases with adjusted cooling setpoints relative to baseline model with a cooling setpoint of 72°F. The chart includes 17 climates, each of which was presented in a different color. X-axis indicates the cooling setpoint, and Y-axis for percentage of EUI reduction. Results of four cooling and ventilation configurations were summarized in separate charts. Figure 21 represents the conventional configuration, and all four charts for four configurations are shown in Chapter 4, from Figure 24 to Figure 27. 45 Figure 21.Total EUI reduction percentages of conventional configuration with adjusted cooling temperature setpoints relative to baseline. 46 3.4.3. Percentage of discomfort hours relative to total EUI savings Figure 22 presents the occupant comfort outputs relative to total EUI reductions in Phoenix. There are 17 percentage of discomfort hours charts, and each of them showed the results of on climate. Figure 22 is an example to show the intended output from the methodologies discussed in this chapter. Percentage of discomfort hours charts of all 17 climates (Figure 28 to Figure 44) are shown in Chapter 4. Every cooling and ventilation configuration was represented in a different color. The X-axis indicates the percentage of total EUI reduction, and the Y-axis for the percentage of discomfort hours. The ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases (orange and purple lines), and the PMV model was used for the other two cases (red and blue lines). Figure 21 shows EUI reduction according to each cooling setpoint adjustment from 72°F to 80°F with a step of 1°F. The percentage of discomfort hours was simulated also based on each cooling setpoint adjustment with the same range and increment. Since both EUI reduction and discomfort hours percentage were based on the same cooling setpoint range and increment, Figure 22 illustrates discomfort hours percentage at a given EUI reduction value. Therefore, each data point in Figure 22 represents the discomfort hours percentage at a given EUI reduction value with a corresponding cooling setpoint. This chart is helpful in studying the relations between occupant comfort and energy reduction when adjusting cooling temperature setpoints. 47 Figure 22. Percentage of discomfort hours relative to total EUI reductions-Phoenix climate. 48 Chapter 4. Results 4. Results This thesis is intended to assist in the design decision making process in early design stages and in building operation periods of commercial buildings. The results can indicate the potential of energy saving through the application of thermal environment management strategies, also inform its advantages comparing to some relatively expensive and time consuming physical building retrofit methods. Therefore, EUI reduction percentages were first examined against baseline cooling setpoints in each climate with different system and ventilation configurations. This result provides information on energy savings for each cooling setpoint increment from the baseline (72°F) to 80°F. Then, the percentage of discomfort hours was quantified against EUI reduction percentage with the same settings. This result shows trade-offs between energy saving and occupant discomfort. Ultimately, by examining each result, a balanced decision can be made to achieve energy saving and maintain occupant comfort. 4.1. Baseline Total EUI As stated in the previous chapter, Figure 23 summarizes the total EUI of four system and ventilation configurations (conventional, mixed-mode, DOAS, DOAS + mixed-mode) for 49 17 climates. Each system configuration differed from each other in terms of HVAC system type and ventilation mode. Total EUI included cooling, heating, fan, lighting and equipment energy consumption. Since each simulation case only differed with each other regarding cooling technologies and strategies, total EUI was used to reflect air- conditioning energy consumptions. Though only cooling setpoint adjustments were involved in this thesis, heating energy might be affected as well because of energy consumed by reheating. Climates are presented in different colors below where red represents the warmest climate and purple represents the coldest climate. 50 Figure 23. Baseline Total EUI of four system configurations of 17 climates. As comparing the same system configuration in different climates, buildings in cold climates usually consumed more energy than those in warm climates since they consumed large amounts of heating energy. Buildings in mild climates, such as Los Angeles and San Francisco, had less energy consumption when compared to buildings in more extreme climates. 51 As a more efficient air-conditioning system, a DOAS system consumes less energy than a VAV system. For instance, in Fairbanks climate, baseline conventional configuration had the total EUI of 84 kBtu/sf, and DOAS configuration had the total EUI of 72 kBtu/sf. With same HVAC system type, configuration with mixed-mode ventilation had lower EUI than configuration without mixed-mode ventilation. For example, in the Fairbanks climate, conventional configuration has the total EUI of 84 kBtu/sf, and mixed-mode configuration has that of 81 kBtu/sf. With same HVAC system, the differences between configuration with mixed-mode ventilation and without mixed-mode ventilation were more obvious in cold climates than in warm climates. However, mixed-mode configuration showed more energy consumption than conventional configuration in Houston and San Francisco climates (Table 2). Possible reasons for the increases in energy might be the trade-offs between heating and cooling energy consumptions. Since window opening behaviors were controlled by outdoor and indoor temperature in the simulations, and the opening schedule was set for all year long, windows might be opened during some days in spring and fall, or even in winter, whenever the opening temperature condition was reached. Therefore, mixed-mode ventilation might contribute to certain amount of cooling energy savings in Spring, Summer, and Fall seasons, but result in more heating energy consumptions at the same time. Table 2. Baseline Total EUI of four system configurations of 17 climates. VAV VAV +Mixed- mode DOA S DOAS+ Mixed- mode 52 Miami 30.17 30.00 24.54 24.11 Riyadh 36.80 36.69 27.72 27.00 Houston 34.40 34.36 28.46 27.53 Phoenix 35.42 35.11 27.73 26.79 Memphis 39.78 39.73 32.58 31.30 Los Angeles 27.34 27.33 22.22 21.33 San Francisco 31.84 31.96 24.66 23.59 Baltimore 45.08 44.96 37.18 35.43 Albuquerque 41.84 41.48 33.77 32.13 Boston 52.34 52.23 43.82 41.73 Chicago 50.29 50.09 41.56 39.85 Boise 49.97 48.65 38.45 36.04 Vancouver 44.27 41.60 34.76 32.93 Burlington 55.16 53.04 46.38 43.99 Helena 55.16 52.81 45.90 43.34 Duluth 65.37 63.10 55.98 52.82 Fairbanks 84.09 81.30 72.76 69.11 53 4.2. Total EUI reductions per system configurations Figure 24 to Figure 27 are four charts showing total EUI reductions of each cooling strategy through cooling setpoint adjustments. EUI reductions were calculated as a percentage of total EUI baseline (with cooling setpoint 72°F). Similar to Figure 23, climates in Figure 24 to Figure 27 are represented by different colors, red for the warmest climate, and purple for the coldest climate. For instance, in all four configurations, the Houston climate, a red-orange line, showed a concave down increasing curve in EUI reduction percentage value as cooling setpoint increased. In Figure 24, which represents EUI reduction of VAV system, as the cooling setpoint of Houston climate increased from 76 to 77 Degree Fahrenheit, the corresponding EUI reduction percentage increased from 12% to 14%, resulted in an additional 2% increase of EUI reduction, when comparing with total EUI baseline. For all configurations, data presented a diminishing marginal return pattern of EUI reductions against each cooling setpoint increment. EUI reduction percentages were larger for buildings in warm climates than those of in cold climates, due to cold climates were heating dominated. DOAS system showed less EUI reduction percentage comparing to VAV system. Therefore, the potential of energy saving in DOAS system was less than VAV system. Comparing cases with the same air-conditioning system, but with different ventilation strategy, mixed-mode ventilation resulted in larger percentage of EUI reductions, and the rate of EUI reductions were faster in VAV system than that of in DOAS system. 54 Figure 24. Total EUI reductions of conventional configuration. 55 Figure 25. Total EUI reductions of mixed-mode configuration. 56 Figure 26. Total EUI reductions of DOAS configuration. 57 Figure 27. Total EUI reductions of DOAS + mixed-mode configuration. 4.3. Percentage of discomfort hours and total EUI reductions Figure 28 to Figure 44 show how occupant comfort level in cooling season changes relative to total energy savings. Results from various climates were summarized in 58 separate charts. Four cooling and ventilation configurations are presented in different colors. Total EUI reductions of simulation cases, obtained from Figure 24 to Figure 27, are shown on the X-axis verses the percentage of discomfort hours on the Y-axis. Figure 24 to Figure 27 show EUI reduction according to each cooling setpoint adjustment from 72°F to 80°F with a step of 1°F. The percentage of discomfort hours was simulated also based on each cooling setpoint adjustment with the same range and increment. Therefore, percentages of discomfort hours are illustrated against EUI reduction values in Figure 28 to Figure 44. The percentage of discomfort hours was calculated as a percentage of occupied hours that were not in comfort zone. Comfort zone was defined using different comfort models for different cooling and ventilation configurations. For conventional and DOAS configurations, PMV model was used to assess the occupant comfort level, since these configurations do not include outdoor air for ventilation. Occupied hours with a PMV value excessed the range of -0.5 and 0.5 were defined as discomfort hours, which would be used when calculating the percentage of discomfort hours. ATC model was used to assess the comfort level for configurations with mixed-mode ventilation. There were three zones to define the comfort level of a space according to its’ operative temperature and outdoor running mean temperature. Occupied hours outside 90% acceptability zones were defined as discomfort hours. 59 In Figure 30, the Houston profile was listed as the third graph. Both conventional and DOAS configurations showed a trend similar to logistic growth curve. Percentage of discomfort hours experienced slow growth in the initial and last stage of increasing total EUI reduction, and relatively rapid growth in the middle stage of increasing total EUI reduction. As the cooling setpoint approached to the final increasing stage, there was a slowdown in the growth of discomfort. For instance, the percentage of discomfort hours of conventional configuration increased from 0.5% to 4% with the cooling setpoint increased from 72°F to 74°F, then there was a rapid growth from 4% to 92% with the cooling setpoint increased by 4 degrees from 74°F. Finally, it showed slow growth from 92% to 96% with the setpoint increased from 78°F to 80°F. On the other hand, mixed mode and DOAS with mixed mode showed a reversed relationship between percentage of discomfort hours and total EUI reductions as those of in conventional and DOAS configurations. Since ATC model was used to estimate comfort level, level of occupancy discomfort was largely influenced by outdoor temperature. For example, the percentage of discomfort hours showed a rapid drop from 80% to nearly 0% with the setpoint increased from 72°F to 76°F, then it showed a stable stage with the discomfort value stayed close to 0%. With the cooling setpoint being adjusted from 72°F to 80°F, comfort results for buildings with and without mixed-mode ventilation could show large differences depending on the climate. In hot climates like Houston, buildings with mixed-mode ventilation showed 60 decreasing percentage of discomfort hours with the increase of total EUI reductions; the buildings without mixed-mode ventilation showed increasing percentage of discomfort hours with the increase of total EUI reductions. However, in cold climate like Fairbanks, all four cooling and ventilation configurations showed similar trends, in which the percentage of discomfort hours increased as the total EUI reduction increased. In relatively mild climate like Memphis, conventional and DOAS configurations showed similar trends to those of in the hot and cold climates, the percentage of discomfort hours increased exponentially as the total EUI reduction increased; in mixed-mode and DOAS + mixed-mode configurations, the percentage of discomfort hours dropped first, then rose with the increase of EUI reductions. 61 Figure 28.Percentage of discomfort hours relative to total EUI reductions-Miami climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). 62 Figure 29. Percentage of discomfort hours relative to total EUI reductions-Riyadh climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). 63 Figure 30. Percentage of discomfort hours relative to total EUI reductions-Houston climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). 64 Figure 31. Percentage of discomfort hours relative to total EUI reductions-Phoenix climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). 65 Figure 32. Percentage of discomfort hours relative to total EUI reductions-Memphis climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). 66 Figure 33. Percentage of discomfort hours relative to total EUI reductions-Los Angeles climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). 67 Figure 34. Percentage of discomfort hours relative to total EUI reductions-San Francisco climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). 68 Figure 35. Percentage of discomfort hours relative to total EUI reductions-Baltimore climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). 69 Figure 36. Percentage of discomfort hours relative to total EUI reductions-Albuquerque climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). 70 Figure 37. Percentage of discomfort hours relative to total EUI reductions-Boston climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). 71 Figure 38. Percentage of discomfort hours relative to total EUI reductions-Chicago climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). 72 Figure 39. Percentage of discomfort hours relative to total EUI reductions-Boise climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). 73 Figure 40. Percentage of discomfort hours relative to total EUI reductions-Vancouver climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). 74 Figure 41. Percentage of discomfort hours relative to total EUI reductions-Burlington climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). 75 Figure 42. Percentage of discomfort hours relative to total EUI reductions-Helena climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). 76 Figure 43. Percentage of discomfort hours relative to total EUI reductions-Duluth climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). 77 Figure 44. Percentage of discomfort hours relative to total EUI reductions- Fairbanks climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). 78 Chapter 5. Discussion 5. Discussion This chapter analyzes and discusses the results showed in previous chapter in order to make valuable conclusions. The discussion focuses on the effects of cooling setpoint adjustment on energy performance and occupant comfort level predicted by various comfort models, and the differences of applying cooling setpoint adjustments on different cooling and ventilation configurations in various climates. Figure 24 to Figure 27 show that applying cooling temperature setpoint could achieve energy reduction, and the level of reduction was related to the type of cooling system, ventilation modes and climates. Though the amount of energy reduction varied for any given setpoint adjustment, every simulated case showed that more energy reduction was achieved as the cooling setpoint was being increased. For example, referring to Figure 30. Percentage of discomfort hours relative to total EUI reductions-Houston climate (the ATC model was used to determine thermal dissatisfaction for the mixed mode and DOAS + mixed mode cases - orange and purple lines, and the PMV model was used for the other two cases - red and blue lines). for conventional configuration, when the cooling setpoint was adjusted from baseline (72°F) to 73°F, 3.5% total EUI reduction was achieved. When the cooling setpoint was adjusted from 77°F to 78°F, still with 1°F increase in cooling setpoint, only 1.2% additional total energy reduction was achieved. The increase of energy reductions was more substantial 79 when the adjusted setpoint was close to baseline, which indicated that adjusting cooling setpoint by small amount could receive relatively obvious energy saving effects. In this case, understating how adjusting setpoint influenced the occupant comfort is important to discuss the application potential of setpoint adjustment. In addition, from design perspective point of view, 20% of occupant discomfort (the threshold used in ASHRAE-55) was considered as the breakeven point for energy saving trade off with occupant thermal comfort in this thesis. More than 20% of discomfort defeated the purpose of running a system. In this case, energy was still consumed, and more than enough occupants were not satisfied. For specific design needs, another occupant discomfort level could be chosen. Figure 28 to Figure 44 provide illustrations the relations regarding energy saving and occupant comfort level as a guideline. 5.1. Climates and percentage of discomfort hours As shown from Figure 28 to Figure 44, climate was the main factor that contributed to the difference of numeric value of percentage of discomfort hours for same configuration. For mixed-mode and DOAS + mixed-mode configurations, as ATC model was used to assess their occupant comfort, the difference of their percentage of discomfort hours was due to the difference of outdoor temperature. The ATC model takes occupant's adaptability to outdoor environment into account. The acceptable indoor temperature range for building occupant defined by ATC model is relevant to the outdoor 80 temperature. For example, the outdoor temperature in a hot climate is higher than that in a cold climate, so the extrema of acceptable indoor temperature range for the hot climate is higher than the extrema for the cold climate. Therefore, same cooling setpoint contributed to different percentage of discomfort hours for configurations involving mixed-mode ventilation. For instance, at the cooling setpoint of 80°F, the percentage of discomfort hours for mixed-mode configuration in Miami climate was 0.4%, and that in Fairbanks was 79.0%, which is because Miami had an average temperature of 93°F in summer, and the average temperature in summer for Fairbanks was 73°F. For conventional and DOAS configurations, in hot climates, the maximum value of percentage of discomfort hours was larger than that in cold climates in general. For example, referred to Figure 24 and Figure 30, in conventional configuration, the maximum value of percentage of discomfort hours was 96.5% at the setpoint of 80°F in Houston climate, and the maximum of that in Fairbanks was 76.7%, also at setpoint of 80°F. A possible reason contributed to the difference is that the HVAC system operation time and building occupied time were set to be the same in all simulations. The HVAC system took a certain amount of time to bring the indoor temperature to the cooling setpoint, and that amount of time was different in each climate. The difference of the amount of time being taken was because the building envelope and outdoor temperature varied by climate. Since the percentage of discomfort hours was calculated as the percentage of hours that the temperature was not in comfort zone out of total occupied hours, how much time the HVAC system took to bring indoor temperature to the cooling setpoint influenced the percentage of discomfort hours. Among all climates, there were 81 two climates that showed different trends than other climates. In Los Angeles and San Francisco climates, the percentage of discomfort hours showed relative low values for all setpoint cases involved in the simulations. The highest value of percentage of discomfort hours was 43.1% for conventional configuration in Los Angles, and the highest value was even lower in San Francisco, which was 26.6%. The results showed that in these two climates, the cooling setpoint could be adjusted in a wider range compared to other climates, which indicated that adjusting cooling setpoint should be more practicable in these two climates. 5.2. System type and percentage of discomfort hours Comparing percentage of discomfort hours of conventional and DOAS configuration in the same climate, they both showed increase as the cooling setpoint increased. However, the increasing amount are not the same Table 3 summarizes the percentage of discomfort hours for conventional and DOAS configurations. Three climates were selected to represent hot, mild, and cold climate respectively. In hot climates, these two configurations showed similar percentage of discomfort hours when the cooling setpoint was in the two end ranges, from 72°F to 74°F and from 77°F to 80°F. However, the DOAS configuration showed much lower percentages of discomfort hours than conventional configuration when the cooling setpoint was at 75°F and 76°F compare to other ranges. For example, according to Houston climate in Figure 30 and 82 Table 3, the largest difference between these two configurations appeared when the cooling setpoint was at 76°F. DOAS configuration had a percentage of discomfort hours of 37.2%, which was 19.5% lower than that of conventional configuration, 56.7%. However, in the two end cooling setpoint ranges, the percentage of discomfort hours of both configurations were similar. The largest difference between these two configurations were only 3% when the cooling setpoint was at 77°F, with percentage of discomfort hours of conventional was 81% and that of DOAS configuration was 84%. The comparison results of conventional and DOAS configuration in cold climates were slightly different than those in hot climates. DOAS and conventional configuration still showed similar percentage of discomfort hours in certain range, from 72°F to 75°F and from 78°F to 80°F. Referring to Vancouver climate in Figure 40 and Table 3, DOAS configuration showed much better occupant thermal performance than conventional configuration when cooling setpoint was at 76°F and 77°F than other ranges. The largest difference of percentage of discomfort hours between these two configurations was 20.7% with the cooling setpoint of 77°F. The percentage of discomfort hours of conventional configuration was 34.6%, and that of DOAS configuration was 13.9%. In mild climates, these two configurations showed similar percentage of discomfort hours with the cooling setpoint was in the range from 72°F to 80°F. For instance, according to Los Angeles climate in Figure 33 and Table 3, the largest difference of percentage of discomfort hours between these two configurations was 4.3% with the cooling setpoint of 83 78°F. The percentage of discomfort hours of conventional configuration was 28.8%, and that of DOAS configuration was 33.1%. The results indicated that, in extreme climates (both hot and cold climate), DOAS system configuration could result in considerably lower percentage of discomfort hours than conventional configuration with the cooling setpoint being adjusted in a certain range when compare with the other ones. The range was approximately from 75°F to 77°F, and the two limits of this range could be slightly different depending on climates. Though within the certain range, DOAS configuration has better performance than conventional configuration in terms of occupant comfort level, there is no obvious differences, more than 5%, between the thermal comfort performance of these two configurations with the cooling setpoint in the range from 72°F to 74°F (or 75°F, depending on climates) and from 77°F (or 78°F, depending on climates) to 80°F in extreme climates. In mild climates, different types of HVAC system do not contribute to obvious differences in percentage of discomfort hours, with the cooling setpoint being adjusted in the range from 72°F to 80°F. 84 Table 3. Percentage of discomfort hours for conventional and DOAS configurations - Houston, Los Angeles, and Vancouver climates. 5.3. Ventilation mode and percentage of discomfort hours For buildings supplied with mixed-mode and DOAS + mixed-mode configurations, the ATC model was used to assess the occupant comfort, therefore the probability of occupant discomfort largely depended on outdoor temperature as discussed in 79. In a warm climate like Houston, the percentage of discomfort hours decreased as the setpoint increased. The percentage of discomfort hours of mixed-mode configuration baseline was 80%, and it reached nearly 0% when the setpoint was increased to 76°F. Similarly, the percentage of discomfort hours of DOAS + mixed-mode configuration baseline was 95%, and it showed a value close to 0% when the cooling setpoint was increased to 77°F. The average summer outdoor temperature in Houston was 85°F, the comfort indoor temperature range defined by the ATC model was from 76°F to 85°F. (CBE Thermal Conventional DOAS Conventional DOAS Conventional DOAS 72 0.5 0.1 0.3 0.2 1.6 1 73 1.1 0.3 0.2 0.2 1.3 0.8 74 3.1 1 0.2 0.2 1.2 0.6 75 17.6 6.7 0.2 0.1 3.6 0.5 76 56.7 37.2 0.3 0.1 15.7 0.5 77 81 84 10 12.6 34.6 13.9 78 92.4 92.4 28.8 33.1 54.2 51.6 79 95.9 94.6 43.1 38.9 64.5 61 80 96.5 95.2 43.1 42.7 69.3 64.2 Cooling Setpoint Houston (hot climate) Los Angeles (mild climate) Vancouver (cold climate) Percentage of Discomfort Hours (%) 85 Comfort Tool, 2013). Though the range is an estimated one using the average outdoor temperature, it could indicate that the baseline setpoint temperature, 72°F, is out of the range for most occupied time in summer, and by increasing cooling setpoint to 76°F, the building was actually been brought into the comfort zone. In addition, the percentage of discomfort hours showed a continuous drop for the setpoint cases that were involved in this thesis (from 72°F to 80°F). The trend indicated that in a hot climate, buildings with mixed-mode ventilation could benefit from increasing cooling setpoint from 72°F to 80°F. Total EUI could be reduced with a large improvement in the occupant comfort level. In a cool climate like Fairbanks, the percentage of discomfort hours for configurations involving mixed-mode showed reverse trend compared to that in a hot climate. The percentage of discomfort hours for mixed-mode and DOAS + mixed-mode configurations increased as setpoint temperature increasing. The percentage of discomfort hours for mixed-mode configuration was 3.55% at the cooling setpoint of 72°F, and it increased to 78.96% when the setpoint was adjusted to 80°F. For DOAS + mixed-mode configuration, the percentage of discomfort hours increased from 1.99% to 78.87% as the setpoint raised from 72°F to 80°F. Since the average summer temperature in Fairbanks was 63°F, which was 22°F lower than that in Houston, the acceptable temperature range defined by the ATC model was from 69°F to 77°F (CBE Thermal Comfort Tool, 2013). From the average summer outdoor temperature and corresponding comfort indoor temperature range, it could be predicted that the baseline setpoint, 72°F was in the acceptable range for most occupied time in summer, and the indoor temperature was approaching the 86 upper limit of the range as the cooling setpoint increased. Therefore, different from the situation in a hot climate, applying cooling setpoint adjustment was bring the building out from the comfort zone. The results indicated that a certain level of occupant comfort would be scarified when achieving energy savings through increasing cooling setpoint under there two configurations. The percentage of discomfort hours stayed under 20%, which was an acceptable level of occupant discomfort, until the cooling setpoint was increased beyond 76°F. At the setpoint temperature of 76°F, mixed-mode configuration achieved 3.12% total EUI reductions, and DOAS + mixed-mode configuration achieved 0.51% total EUI reductions. Since over 10% of occupant comfort have been scarified to achieve small amount of energy reductions, applying cooling setpoint adjustment as an energy saving strategy might not be beneficial for buildings including mixed-mode ventilation in cold climates. In mild climates, such like Albuquerque, the percentage of discomfort hours for configurations including mixed-mode ventilation first showed a drop, then it showed an increase after reached the minimum value - 0.3%. Referring to Figure 36, for the mixed- mode configuration, the value of discomfort dropped from 43.9% to 0.3% when the setpoint was adjusted from 72°F to 76°F, then it increased from 0.3% to 9.35% when the setpoint increased from 76°F to 80°F. For DOAS + mixed-mode configuration, the percentage of discomfort hours started at 66.7% with baseline setpoint, and it reached the minimum value of 0.35% at the setpoint of 77°F. An increase appeared after 77°F, and the value of discomfort increased to 5.11% when the setpoint increased to 80°F. The average summer out temperature was 75°F in Albuquerque, and the corresponding ATC 87 acceptable temperature range was 72°F to 82°F (CBE Thermal Comfort Tool, 2013). As indicated by both estimated range and the results shown in Figure 36, all adjusted cooling setpoints were in the acceptable indoor temperature range for most of the occupied time in summer. In addition to that, Figure 36 shows that the percentage of discomfort hours could reach a minimum value at a certain setpoint temperature, but since extreme climates have different outdoor temperatures and corresponding acceptable indoor temperature ranges than mild climates, the minimum percentage of discomfort hours appears outside the adjusted cooling temperature setpoint range (72°F-80°F). In cold climates, the minimum percentage of discomfort hours is likely to appear at a setpoint below 72°F, and in hot climate, it is likely to appear at a setpoint higher than 80°F. Therefore, in mild climates, there existed a recommended cooling setpoint temperature that could maximize occupant comfort in buildings with mixed-mode ventilation. By increasing the cooling setpoint to the recommended value, both energy saving and improvement of occupant comfort could be achieved. Though the percentage of discomfort hours increased after the recommended cooling setpoint, it usually stayed under 20% with the cooling setpoint being adjusted up to 80°F. 5.4. Potential of increasing cooling setpoint Though the percentage of discomfort hours and total EUI reductions showed differently for different configurations in different climates, all simulated cases had the potential of achieving energy saving through cooling setpoint adjustment while maintaining 88 acceptable occupant comfort level (percentage of discomfort hours lower than 20%). Table 4 to Table 7 summarizes the total EUI reduction and percentage of discomfort hours outcomes with the adjusted cooling setpoint of 75°F. Percentage of discomfort hours shown in Table 4 to Table 7 was calculated in the same way as that in Figure 28 to Figure 44. The ATC model was used for mixed-mode and DOAS + mixed-mode configurations, and the PMV model was used for conventional and DAS configurations. Results of different configurations were shown in separate tables. Cooling setpoint of 75°F is used in the DOE reference commercial building model, which represents a standard application of cooling setpoint based on ASHRAE 55. Since building operators and designers are likely to have concerns of ASHRAE standard when applying cooling setpoint adjustment, 75°F was selected here to show how the energy reduction and occupant comfort were influenced if the cooling setpoint was increased from industry application (72°F) to standard application (75°F). The climates that were highlighted in red had the percentage of discomfort hours more than 20%. According to Table 4 to Table 7, most climates had an acceptable level of occupant comfort when the cooling setpoint was adjusted to 75°F. 5.4.1. Conventional configuration As shown in Table 4, conventional configurations showed 62.2% of discomfort in Miami climate and 56.7% in Phoenix climate when the cooling setpoint was set to 75°F. Even when the cooling setpoint was set to 73°F, Conventional configuration 89 showed 42.3% percentage of discomfort hours in Miami climate and 22.5% in Phoenix climate. Only with the baseline setpoint, 72°F, the occupant comfort level was acceptable in both climates. Above results indicated that adjusting cooling setpoint was not an appropriate strategy to achieve energy saving for building with conventional configurations in these two climates. However, for other climates, it is possible to increase cooling setpoint at least 3°F from the baseline to achieve energy reduction as well as maintaining acceptable occupant comfort level. By increasing the cooling setpoint by 3°F, Riyadh has the highest total EUI reduction among all climates, which was 10.58% with a discomfort level of 19%. Table 4. Total EUI reductions and percentage of discomfort hours with cooling setpoint of 75°F -conventional configuration. Total EUI Reduction Percentage of discomfort hours (%) (%) Miami 12.79 62.20 Riyadh 10.58 19.00 Houston 8.47 17.60 Phoenix 9.96 56.70 Memphis 7.01 24.80 Los Angeles 9.54 0.20 San Francisco 8.71 1.80 90 Baltimore 5.54 7.90 Albuquerque 6.49 10.20 Boston 4.59 7.50 Chicago 5.15 8.60 Boise 5.24 10.60 Vancouver 5.79 3.60 Burlington 4.28 4.50 Helena 4.60 9.90 Duluth 3.80 6.40 Fairbanks 3.26 9.90 91 5.4.2. Mixed-mode configuration Per Table 5, mixed-mode configurations showed 28.66% of discomfort in Riyadh climate and 21.39% of discomfort in Phoenix climate. As the cooling setpoint been set from 72°F to 75°F, percentage of discomfort hours was kept higher than 20% in both Riyadh and Phoenix. However, when the cooling setpoint was adjusted to 76°F, mixed-mode configuration showed 10.82% of discomfort in Riyadh climate and 7.79% of discomfort in Phoenix climate. Furthermore, the percentage of discomfort hours became lower when the cooling setpoint kept increasing. As discussed earlier, in hot climate, percentage of discomfort hours kept reducing as the cooling setpoint being increased for mixed-mode configuration. For example, in Houston climate, when the cooling setpoint was increased to 75°F, 8.58% total EUI was reduced while the percentage of discomfort hours was 6.06%. If the cooling setpoint was increased further to 80°F, 15.7% of total EUI was reduced while the percentage of discomfort hours dropped to 0.43%. Therefore, cooling setpoint could be further increased in some hot climates to achieve more energy savings. Table 5.Total EUI reductions and percentage of discomfort hours with cooling setpoint of 75°F - mixed-mode configuration. Total EUI Reduction Percentage of 92 discomfort hours (%) (%) Miami 14.25 0.20 Riyadh 10.73 28.66 Houston 8.58 6.06 Phoenix 9.96 21.39 Memphis 7.27 5.80 Los Angeles 10.45 6.15 San Francisco 9.45 4.33 Baltimore 5.79 2.42 Albuquerque 6.71 6.23 Boston 4.76 2.42 Chicago 5.50 2.16 Boise 4.87 4.16 Vancouver 5.96 4.24 Burlington 4.49 2.94 93 Helena 4.76 2.16 Duluth 3.94 7.19 Fairbanks 3.12 18.96 5.4.3. DOAS configuration Per Table 6, DOAS configurations showed 35.0% of discomfort in Miami climate and 32.67% of discomfort in Riyadh climate with a cooling setpoint of 75°F. When the cooling setpoint was increased from the baseline to 74°F instead of to 75°F, DOAS configuration showed 5.0% of discomfort in Miami climate. However, the cooling setpoint was only able to increase to 73°F for maintaining at an acceptable discomfort level in Riyadh climate. For other climates, when cooling setpoint was increased by 3°F from the baseline, it was possible to achieve energy reduction as well as maintaining acceptable occupant comfort level. Though the level of discomfort was high in Miami and Riyadh climates as shown in the table, there was still a potential to achieve energy saving by rise the cooling setpoint, but not exceeding 75°F. Table 6.Total EUI reductions and percentage of discomfort hours with cooling setpoint of 75°F - DOAS configuration. Total EUI Reduction Percentage of 94 discomfort hours (%) (%) Miami 7.88 35.00 Riyadh 9.08 32.67 Houston 2.70 6.70 Phoenix 3.31 17.40 Memphis 2.01 13.40 Los Angeles 4.86 0.10 San Francisco 3.89 0.40 Baltimore 1.44 1.80 Albuquerque 1.79 0.20 Boston 1.14 2.40 Chicago 1.20 1.20 Boise 1.58 0.30 Vancouver 1.56 0.50 Burlington 1.02 0.20 95 Helena 0.99 2.40 Duluth 0.83 0.50 Fairbanks 0.48 0.10 5.4.4. DOAS + mixed-mode configuration There are four climates in Table 7 showed a discomfort level higher than 20%. In Riyadh, the percentage of discomfort hours showed values below 20% when the cooling setpoint was raised from 77°F to 80°F.In Phoenix, it showed values below 20% only when the cooling setpoint was increased beyond 78°F. In Houston and Memphis, the percentage of discomfort hours showed values below 20% when the cooling setpoint was increased from 76°F to 80°F. For building with DOAS + mixed-mode configuration in the four climates above, cooling setpoint needed to be adjusted beyond 75°F to achieve acceptable occupant comfort level., In addition, more total EUI reduction were achieved compared to the values shown in table XXX. Similar with mixed-mode configuration, in most hot climates, cooling setpoint could be increased further than 75°F to achieve more energy reduction and improvement of occupant comfort level. 96 Table 7. Total EUI reductions and percentage of discomfort hours with cooling setpoint of 75°F -DOAS + mixed-mode configuration. Total EUI Reduction Percentage of discomfort hours (%) (%) Miami 7.77 0.31 Riyadh 8.74 28.66 Houston 2.75 50.04 Phoenix 3.46 73.85 Memphis 2.05 43.90 Los Angeles 5.02 2.86 San Francisco 3.99 2.51 Baltimore 1.56 16.54 Albuquerque 1.97 16.54 Boston 1.24 8.48 Chicago 1.27 12.29 97 Boise 1.73 11.26 Vancouver 1.71 3.98 Burlington 1.13 4.68 Helena 1.09 3.03 Duluth 0.93 7.97 Fairbanks 0.51 14.63 Results from Table 4 to Table 7 indicated that there is large potential to apply cooling setpoint adjustment as an energy saving strategy in most combinations of system and ventilation configurations and climates. Except for conventional configuration in Miami and Phoenix climates, all other simulated cases allowed increasing cooling setpoint by around 3°F while maintaining acceptable occupant thermal comfort level. The largest total EUI reduction occurred in Miami climate with mixed-mode configuration at a cooling setpoint of 75°F. In some climates, most likely in hot climates, it was beneficial for buildings with mixed-mode or DOAS + mixed-mode configurations to increase cooling setpoint further than 75°F. For instance, buildings with mixed-mode configuration in Phoenix achieved total EUI reduction of 15.89% at 78°F cooling setpoint, and the percentage of discomfort hours was 0.09%. Different from previous studies, multiple parameters were involved in this thesis. HVAC system type, ventilation strategy and climates all contributed to the differences in energy and comfort 98 results. Though previous studies provided some valuable conclusions about expansion of temperature range (Hoyt and Zhang, 2015) and setpoint adjustment (Konis and Zhang, 2016), their results were either limited to a single system type or mild climates. According to results discussed above, extreme climates had very different energy and occupant thermal comfort performance than mild climates. The type of system contributed to the differences in the amount of total energy reduction in both mild and extreme climates. However, it only led to differences greater than 5% in occupant comfort level in extreme climates, and with the cooling setpoint between 75°F and 77°F (specific range may differ depending on climates). While occupant comfort levels were similar between the two systems with cooling setpoint between 72°F and 80°F in mild climate, and in two end ranges (from 72°F to 74°F and from 78°F to 80°F, specific range may differ depending on climates) in extreme climates. Ventilation strategy, which was not included in previous studies, was proven to be an important factor to consider when quantifying the energy saving achievable and corresponding occupant thermal comfort level in both mild and extreme climates. 99 Chapter 6. Conclusion and Future Work 6. Conclusion and Future Work 6.1. Conclusion The thesis used parametric simulations to quantify the energy saving achievable and occupant thermal comfort outcomes through cooling setpoint adjustment. The Department of Energy (DOE) reference commercial building model (Deru et al., 2011) was used to represent existing middle size commercial buildings. Seventeen climates, included both extreme and mild climates, were simulated. In addition, four cooling and ventilation configurations were included to study the influence of HVAC system type and ventilation modes. Three types of charts were the intended output of the thesis, which are: 1) total EUI of baselines chart; 2) total EUI reduction relative to cooling setpoint charts; 3) percentage of discomfort hours relative to total EUI reduction charts. The first two types of charts were intended to provide readers with information about the energy performance of the baseline, and the energy savings achievable at each cooling setpoint adjustment in each simulation. The percentage of discomfort hours charts helped readers in understating the 100 relations between occupant comfort and energy savings through cooling setpoint adjustment. The study has proven that simulations could quantify energy saving achievable and occupant thermal comfort outcomes through cooling setpoint adjustment. The results of the thesis could provide a guideline for designers and building operators in early design stage or in building retrofit process. By knowing the climate that a building is located in, the type of HVAC system, and the ventilation mode, the reference charts can help designers and building operators to assess the potential of energy reductions possible from cooling setpoint adjustment while maintaining or improving occupant thermal comfort. In addition, the charts could assist in decision makings by showing the relations between occupant comfort and energy savings. Compared to the study done by Hoyt et al in 2014, and the study done by Konis and Zhang in 2016, this thesis provided a more comprehensive data set. Simulation data, using a single type of cooling and ventilation configuration, could not indicate the energy and comfort outputs of buildings with a different type configuration. In addition, simulation results from mild climates could not be used to predict the outcomes of buildings in more extreme climates. Therefore, simulation results, involving a wide range of climates and various cooling and ventilation configurations, are valuable as a guideline for design and building renovation. 101 6.2. Future work In future research work, simulated cooling setpoint adjustment range could be differentiate by climates. The adjustment range in this thesis was set up referring to learned occupant comfort temperature range from a previous study (Konis and Zhang, 2016). Since the previous study only involved California climates, the cooling setpoint adjustment range might only be applicable in mild climate like Los Angeles and San Francisco. A similar occupant comfort temperature model could be generated from occupant feedback in various climates, to define the most appropriate simulated cooling setpoint adjustment range for different climates. This thesis only included simulations of cooling setpoint adjustment, because the overcooling problem in summer is more obvious than an overheating problem in winter, according to relevant research (Mendell, 2009). However, simulation of heating setpoint adjustment is worthwhile, as cooling energy occupies a significant part of the energy consumption, especially in cold climates. 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Abstract (if available)

Abstract Past studies have shown that many buildings have overcooling problems during the cooling season with the cooling temperature setpoints that are widely used in industry practice nowadays (Mendell, 2009). It not only causes a certain level of discomfort for building occupants, but also results in excess cooling energy consumption. Adjustment of temperature setpoints has proven to be one of the most practical and cost-effective methods to reduce space cooling and heating energy consumption (Hoyt et al., 2014). However, because of the concern of the impact that may have on occupant thermal comfort, application of temperature setpoints adjustments have been limited to the unoccupied period. In addition, with the development of participatory sensing technologies, one promising application is to base setpoints on actual occupant preferences or acceptance levels rather than static models. Therefore, further research about energy savings achievable through temperature setpoint adjustment and its’ impacts on occupant comfort is needed in order to broaden the application of this strategy. ❧ The previous research has shown that more than 20% of annual HVAC energy reduction could be achieved by applying temperature setpoint adjustment in sealed, mechanically conditioned commercial buildings in California. Instead of single HVAC system type and ventilation strategy, this thesis explored the application of cooling setpoint adjustment for multiple cooling and ventilation configurations. Variable Air Volume (VAV) system and Dedicated Outdoor Air System (DOAS) were used to represent conventional and low-energy HVAC system. In addition to mechanical ventilation, mixed-mode ventilation were involved. Totally 17 climates were selected based on ASHRAE climate zones to cover both extreme and mild climates globally. ❧ Two category of charts were generated for assisting designers or building operators to make decisions during initial design or building operational period. The first set of charts provides information about total Energy Use Intensity (EUI) of baseline models and energy saving reductions for each setpoint adjustment in seventeen climates. The second set of charts presents trade-offs between occupant comfort outcomes and energy reductions through cooling setpoints in each climate. These results indicated the potential of reducing cooling energy consumption, maintaining or even improving occupant comfort level, in commercial buildings through adjusting cooling setpoints.

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Asset Metadata

Creator Zhang, Leluo (author)

Core Title Occupant-aware energy management: energy saving and comfort outcomes achievable through application of cooling setpoint adjustments

School School of Architecture

Degree Master of Building Science

Degree Program Building Science

Degree Conferral Date 2017-05

Publication Date 05/03/2017

Defense Date 03/22/2017

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

Tag cooling set-point,energy saving,OAI-PMH Harvest,occupant comfort,occupant-aware

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Konis, Kyle (committee chair), Noble, Douglas (committee member), Schiler, Marc (committee member)

Creator Email leluo.zhang@gmail.com,leluozha@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC11258606

Unique identifier UC11258606

Identifier etd-ZhangLeluo-5312.pdf (filename)

Legacy Identifier etd-ZhangLeluo-5312

Dmrecord 370611

Document Type Thesis

Format theses (aat)

Rights Zhang, Leluo

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

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