01921nas a2200265 4500008004100000022001300041245007100054210006600125260001200191300001600203490000800219520115000227653002401377653000901401653001401410653002501424653004301449653002101492100001501513700001201528700001901540700001501559700001401574856006701588 2019 eng d a0306261900aA novel approach for selecting typical hot-year (THY) weather data0 anovel approach for selecting typical hotyear THY weather data c03/2019 a1634 - 16480 v2423 a

The global climate change has resulted in not only warmer climate conditions but also more frequent extreme weather events, such as heat waves. However, the impact of heat waves on the indoor environment has been investigated in a limited manner. In this research, the indoor thermal environment is analyzed using a building performance simulation tool for a typical residential building in multiple cities in China, over a time period of 60 years using actual measured weather data, in order to gain a better understanding of the effect of heat wave events. The simulation results were used to analyze the indoor environment during hot summers. A new kind of weather data referred to as the typical hot year was defined and selected based on the simulated indoor environment during heat waves. The typical hot-year weather data can be used to simulate the indoor environment during extreme heat events and for the evaluation of effective technologies and strategies to mitigate against the impact of heat waves on the energy demand of buildings and human health. The limitations of the current study and future work are also discussed.

10aActual weather data10adest10aHeat wave10aMultiyear simulation10aResidential indoor thermal environment10aTypical hot year1 aGuo, Siyue1 aYan, Da1 aHong, Tianzhen1 aXiao, Chan1 aCui, Ying uhttps://linkinghub.elsevier.com/retrieve/pii/S030626191930465902762nas a2200241 4500008004100000022001300041245011200054210006900166260001200235300001400247490000800261520184900269653002602118653002102144653002402165653000902189653002502198653001602223100001702239700001202256700001902268856023302287 2018 eng d a0378778800aClustering and statistical analyses of air-conditioning intensity and use patterns in residential buildings0 aClustering and statistical analyses of airconditioning intensity c09/2018 a214 - 2270 v1743 a

Energy conservation in residential buildings has gained increased attention due to its large portion of global energy use and potential of energy savings. Occupant behavior has been recognized as a key factor influencing the energy use and load diversity in buildings, therefore more realistic and accurate air-conditioning (AC) operating schedules are imperative for load estimation in equipment design and operation optimization. With the development of sensor technology, it became easier to access an increasing amount of heating/cooling data from thermal energy metering systems in residential buildings, which provides another possible way to understand building energy usage and occupant behaviors. However, except for cooling energy consumption benchmarking, there currently lacks effective and easy approaches to analyze AC usage and provide actionable insights for occupants. To fill this gap, this study proposes clustering analysis to identify AC use patterns of residential buildings, and develops new key performance indicators (KPIs) and data analytics to explore the AC operation characteristics using the long-term metered cooling energy use data, which is of great importance for inhabitants to understand their thermal energy use and save energy cost through adjustment of their AC use behavior. We demonstrate the proposed approaches in a residential district comprising 300 apartments, located in Zhengzhou, China. Main outcomes include: Representative AC use patterns are developed for three room types of residential buildings in the cold climate zone of China, which can be used as more realistic AC schedules to improve accuracy of energy simulation; Distributions of KPIs on household cooling energy usage are established, which can be used for household AC use intensity benchmarking and performance diagnoses.

10aAC usage benchmarking10aAir-conditioning10aClustering analysis10aKPIs10aresidential building10aUse pattern1 aAn, Jingjing1 aYan, Da1 aHong, Tianzhen uhttps://linkinghub.elsevier.com/retrieve/pii/S0378778818307199https://api.elsevier.com/content/article/PII:S0378778818307199?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0378778818307199?httpAccept=text/plain01954nas a2200241 4500008004100000245009500041210006900136490000800205520119700213653002201410653001201432653002301444653002201467653002101489653002501510100001401535700001201549700001701561700001901578700001401597700001401611856008701625 2018 eng d00aComparative Study of Air-Conditioning Energy Use of Four Office Buildings in China and USA0 aComparative Study of AirConditioning Energy Use of Four Office B0 v1693 a

Energy use in buildings has great variability. In order to design and operate low energy buildings as well as to establish building energy codes and standards and effective energy policy, it is crucial to understand and quantify key factors influencing building energy performance. This study investigates air-conditioning (AC) energy use of four office buildings in four locations: Beijing, Taiwan, Hong Kong, and Berkeley. Building simulation was employed to quantify the influences of key factors, including climate, building envelope and occupant behavior. Through simulation of various combinations of the three influencing elements, it is found that climate can lead to AC cooling consumption differences by almost two times, while occupant behavior resulted in the greatest differences (of up to three times) in AC cooling consumption. The influence of occupant behavior on AC energy consumption is not homogeneous. Under similar climates, when the occupant behavior in the building differed, the optimized building envelope design also differed. Overall, the optimal building envelope should be determined according to the climate as well as the occupants who use the building.

10aBuilding envelope10aclimate10aenergy consumption10aoccupant behavior10aoffice buildings10atechnological choice1 aZhou, Xin1 aYan, Da1 aAn, Jingjing1 aHong, Tianzhen1 aShi, Xing1 aJin, Xing uhttps://simulationresearch.lbl.gov/publications/comparative-study-air-conditioning02060nas a2200265 4500008004100000245011700041210006900158260001200227300001200239490000800251520123400259653002401493653002401517653001501541653002501556653001801581653001701599100001401616700001201630700001901642700001501661700001401676700001401690856009001704 2017 eng d00aComparison of typical year and multiyear building simulations using a 55-year actual weather data set from China0 aComparison of typical year and multiyear building simulations us c06/2017 a890-9040 v1953 a

Weather has significant impacts on the thermal environment and energy use in buildings. Thus, accurate weather data are crucial for building performance evaluations. Traditionally, typical year data inputs are used to represent long-term weather data. However, there is no guarantee that a single year represents the changing climate well. In this study, the long-term representation of a typical year was assessed by comparing it to a 55-year actual weather data set. To investigate the weather impact on building energy use, 559 simulation runs of a prototype office building were performed for 10 large cities covering all climate zones in China. The analysis results demonstrated that the weather data varied significantly from year to year. Hence, a typical year cannot reflect the variation range of weather fluctuations. Typical year simulations overestimated or underestimated the energy use and peak load in many cases. With the increase in computational power of personal computers, it is feasible and essential to adopt multiyear simulations for full assessments of long-term building performance, as this will improve decision-making by allowing for the full consideration of variations in building energy use.

10aActual weather data10abuilding simulation10aenergy use10aMultiyear simulation10aPeak load  10aTypical year1 aCui, Ying1 aYan, Da1 aHong, Tianzhen1 aXiao, Chan1 aLuo, Xuan1 aZhang, Qi uhttps://simulationresearch.lbl.gov/publications/comparison-typical-year-and-multiyear02536nas a2200253 4500008004100000245008200041210006900123490000800192520173700200653002501937653002001962653001501982653002101997653003102018653002202049100001202071700001902083700001502102700002202117700001802139700002202157700001902179856008402198 2017 eng d00aIEA EBC Annex 66: Definition and simulation of occupant behavior in buildings0 aIEA EBC Annex 66 Definition and simulation of occupant behavior 0 v1563 a

More than 30% of the total primary energy in the world is consumed in buildings. It is crucial to reduce building energy consumption in order to preserve energy resources and mitigate global climate change. Building performance simulations have been widely used for the estimation and optimization of building performance, providing reference values for the assessment of building energy consumption and the effects of energy-saving technologies. Among the various factors influencing building energy consumption, occupant behavior has drawn increasing attention. Occupant behavior includes occupant presence, movement, and interaction with building energy devices and systems. However, there are gaps in occupant behavior modeling as different energy modelers have employed varied data and tools to simulate occupant behavior, therefore producing different and incomparable results. Aiming to address these gaps, the International Energy Agency (IEA) Energy in Buildings and Community (EBC) Programme Annex 66 has established a scientific methodological framework for occupant behavior research, including data collection, behavior model representation, modeling and evaluation approaches, and the integration of behavior modeling tools with building performance simulation programs. Annex 66 also includes case studies and application guidelines to assist in building design, operation, and policymaking, using interdisciplinary approaches to reduce energy use in buildings and improve occupant comfort and productivity. This paper highlights the key research issues, methods, and outcomes pertaining to Annex 66, and offers perspectives on future research needs to integrate occupant behavior with the building life cycle.

10abuilding performance10aenergy modeling10aenergy use10aIEA EBC Annex 6610aInterdisciplinary approach10aoccupant behavior1 aYan, Da1 aHong, Tianzhen1 aDong, Bing1 aMahdavi, Ardeshir1 aD'Oca, Simona1 aGaetani, Isabella1 aFeng, Xiaohang uhttps://simulationresearch.lbl.gov/publications/iea-ebc-annex-66-definition-and02155nas a2200133 4500008004100000245009000041210006900131520167300200100001701873700001201890700001901902700001501921856008501936 2017 eng d00aA Novel Stochastic Modeling Method to Simulate Cooling Loads in Residential Districts0 aNovel Stochastic Modeling Method to Simulate Cooling Loads in Re3 a

District cooling systems are widely used in urban residential communities in China. Most district cooling systems are oversized;this leads to wasted investment and low operational efficiency and thus energy wastage. The accurate prediction of district cooling loads that supports rightsizing cooling plant equipment remains a challenge. This study developed a new stochastic modeling method that includes (1) six prototype house models representing a majority of apartments in the district, (2)occupant behavior models in residential buildings reflecting the temporal and spatial diversity and complexity based on a large-scale residential survey in China, and (3) a stochastic sampling process to represent all apartments and occupants in the district. The stochastic method was employed in a case study using the DeST simulation engine to simulate the cooling loads of a real residential district in Wuhan, China. The simulation results agree well with the actual measurement data based on five performance metrics representing the aggregated cooling loads, the peak cooling loads as well as the spatial load distribution,and the load profiles. Two currently used simulation methods were also employed to simulate the district cooling loads. The simulation results showed that oversimplified occupant behavior assumptions lead to significant overestimations of the peak cooling load and total district cooling loads. Future work will aim to simplify the workflow and data requirements of the stochastic method to enable its practical application as well as explore its application in predicting district heating loads and in commercial or mixed-use districts.

1 aAn, Jingjing1 aYan, Da1 aHong, Tianzhen1 aSun, Kaiyu uhttps://simulationresearch.lbl.gov/publications/novel-stochastic-modeling-method02293nas a2200241 4500008004100000245016600041210006900207520145100276653002201727653001801749653002101767653002301788653002501811653002201836653001501858100001401873700001201887700001701899700001901916700001401935700001501949856008701964 2017 eng d00aSpatial Distribution of Internal Heat Gains: A Probabilistic Representation and Evaluation of Its Influence on Cooling Equipment Sizing in Large Office Buildings0 aSpatial Distribution of Internal Heat Gains A Probabilistic Repr3 a

Internal heat gains from occupants, lighting, and plug loads are significant components of the space cooling load in an office building. Internal heat gains vary with time and space. The spatial diversity is significant, even for spaces with the same function in the same building. The stochastic nature of internal heat gains makes determining the peak cooling load to size air-conditioning systems a challenge. The traditional conservative practice of considering the largest internal heat gain among spaces and applying safety factors overestimates the space cooling load, which leads to oversized air-conditioning equipment and chiller plants. In this study, a field investigation of several large office buildings in China led to the development of a new probabilistic approach that represents the spatial diversity of the design internal heat gain of each tenant as a probability distribution function. In a large office building, a central chiller plant serves all air handling units (AHUs), with each AHU serving one or more floors of the building. Therefore, the spatial diversity should be considered differently when the peak cooling loads to size the AHUs and chillers are calculated. The proposed approach considers two different levels of internal heat gains to calculate the peak cooling loads and size the AHUs and chillers in order to avoid oversizing, improve the overall operating efficiency, and thus reduce energy use.

10aair handling unit10achiller plant10aequipment sizing10ainternal heat gain10aspatial distribution10aspatial diversity10astochastic1 aZhang, Qi1 aYan, Da1 aAn, Jingjing1 aHong, Tianzhen1 aTian, Wei1 aSun, Kaiyu uhttps://simulationresearch.lbl.gov/publications/spatial-distribution-internal-heat01971nas a2200193 4500008004100000245013400041210006900175520127100244653001201515653002001527653001701547653004101564653002201605100001401627700001201641700001901653700001601672856008901688 2017 eng d00aTemporal and spatial characteristics of the urban heat island in Beijing and the impact on building design and energy performance0 aTemporal and spatial characteristics of the urban heat island in3 a

With the increased urbanization in most countries worldwide, the urban heat island (UHI) effect, referring to the phenomenon that an urban area has higher ambient temperature than the surrounding rural area, has gained much attention in recent years. Given that Beijing is developing rapidly both in urban population and economically, the UHI effect can be significant. A long-term measured weather dataset from 1961 to 2014 for ten rural stations and seven urban stations in Beijing, was analyzed in this study, to understand the detailed temporal and spatial characteristics of the UHI in Beijing. The UHI effect in Beijing is significant, with an urban-to-rural temperature difference of up to 8℃ during the winter nighttime. Furthermore, the impacts of UHIs on building design and energy performance were also investigated. The UHI in Beijing led to an approximately 11% increase in cooling load and 16% decrease in heating load in the urban area compared with the rural area, whereas the urban heating peak load decreased 9% and the cooling peak load increased 7% because of the UHI effect. This study provides insights into the UHI in Beijing and recommendations to improve building design and decision-making while considering the urban microclimate.

10abeijing10abuilding design10aMicroclimate10aTemporal and spatial characteristics10aurban heat island1 aCui, Ying1 aYan, Da1 aHong, Tianzhen1 aMa, Jingjin uhttps://simulationresearch.lbl.gov/publications/temporal-and-spatial-characteristics01990nas a2200205 4500008004100000245007700041210006900118520131200187653002201499653002501521653002401546653001501570653002201585653002201607100001901629700001201648700001801660700002001678856008601698 2017 eng d00aTen Questions Concerning Occupant Behavior in Buildings: The Big Picture0 aTen Questions Concerning Occupant Behavior in Buildings The Big 3 a

Occupant behavior has significant impacts on building energy performance and occupant comfort. However, occupant behavior is not well understood and is often oversimplified in the building life cycle, due to its stochastic, diverse, complex, and interdisciplinary nature. The use of simplified methods or tools to quantify the impacts of occupant behavior in building performance simulations significantly contributes to performance gaps between simulated models and actual building energy consumption. Therefore, it is crucial to understand occupant behavior in a comprehensive way, integrating qualitative approaches and data- and model-driven quantitative approaches, and employing appropriate tools to guide the design and operation of low-energy residential and commercial buildings that integrate technological and human dimensions. This paper presents ten questions, highlighting some of the most important issues regarding concepts, applications, and methodologies in occupant behavior research. The proposed questions and answers aim to provide insights into occupant behavior for current and future researchers, designers, and policy makers, and most importantly, to inspire innovative research and applications to increase energy efficiency and reduce energy use in buildings.

 

10aBehavior Modeling10abuilding performance10abuilding simulation10aenergy use10ainterdisciplinary10aoccupant behavior1 aHong, Tianzhen1 aYan, Da1 aD'Oca, Simona1 aChen, Chien-Fei uhttps://simulationresearch.lbl.gov/publications/ten-questions-concerning-occupant02540nas a2200241 4500008004100000245008400041210006900125260001200194520178100206653001001987653002201997653002302019653002202042653002502064653002302089100001202112700001902124700001402143700001402157700001702171700001302188856009702201 2017 eng d00aA Thorough Assessment of China’s Standard for Energy Consumption of Buildings0 aThorough Assessment of China s Standard for Energy Consumption o c03/20173 a

China’s Design Standard for Energy Efficiency of Public Buildings (the Design Standard) is widely used in the design phase to regulate the energy efficiency of physical assets (envelope, lighting, HVAC) in buildings. However, the standard does not consider many important factors that influence the actual energy use in buildings, and this can lead to gaps between the design estimates and actual energy consumption. To achieve the national energy savings targets defined in the strategic 12th Five-Year Plan, China developed the first standard for energy consumption of buildings GB/T51161-2016 (the Consumption Standard). This study provides an overview of the Consumption Standard, identifies its strengths and weaknesses, and recommends future improvements. The analysis and discussion of the constraint value and the leading value, two key indicators of the energy use intensity, provide insight into the intent and effectiveness of the Consumption Standard. The results indicated that consistency between China’s Design Standard GB 50189-2015 and the Consumption Standard GB/T51161-2016 could be achieved if the Design Standard used the actual building operations and occupant behavior in calculating the energy use in Chinese buildings. The development of an outcome-based code in the U.S. was discussed in comparison with China’s Consumption Standard, and this revealed the strengths and challenges associated with implementing a new compliance method based on actual energy use in buildings in the U.S. Overall, this study provides important insights into the latest developments of actual consumption-based building energy standards, and this information should be valuable to building designers and energy policy makers in China and the U.S.

10aChina10acode and standard10aenergy consumption10aenergy efficiency10aEnergy Use Intensity10aoutcome-based code1 aYan, Da1 aHong, Tianzhen1 aLi, Cheng1 aZhang, Qi1 aAn, Jingjing1 aHu, shan uhttps://simulationresearch.lbl.gov/publications/thorough-assessment-china%E2%80%99s-standard01628nas a2200241 4500008003900000245009100039210006900130260001200199300001200211490000800223520083600231653002201067653003401089653003601123653001501159653002201174100001901196700002801215700001801243700001201261700002601273856008701299 2016 d00aAdvances in research and applications of energy-related occupant behavior in buildings0 aAdvances in research and applications of energyrelated occupant  c03/2016 a694-7020 v1163 a

Occupant behavior is one of the major factors influencing building energy consumption and contributing to uncertainty in building energy use prediction and simulation. Currently the understanding of occupant behavior is insufficient both in building design, operation and retrofit, leading to incorrect simplifications in modeling and analysis. This paper introduced the most recent advances and current obstacles in modeling occupant behavior and quantifying its impact on building energy use. The major themes include advancements in data collection techniques, analytical and modeling methods, and simulation applications which provide insights into behavior energy savings potential and impact. There has been growing research and applications in this field, but significant challenges and opportunities still lie ahead.

10aBehavior Modeling10aBuilding design and operation10abuilding performance simulation10aenergy use10aoccupant behavior1 aHong, Tianzhen1 aTaylor-Lange, Sarah, C.1 aD'Oca, Simona1 aYan, Da1 aCorgnati, Stefano, P. uhttps://simulationresearch.lbl.gov/publications/advances-research-and-applications02436nas a2200217 4500008004100000245013500041210006900176520163000245653002401875653002501899653002301924653002201947653003801969653004402007100001602051700001202067700001502079700001902094700001602113856008902129 2016 eng d00aA Comparative Study on Energy Performance of Variable Refrigerant Flow Systems and Variable Air Volume Systems in Office Buildings0 aComparative Study on Energy Performance of Variable Refrigerant 3 a

Variable air volume (VAV) systems and variable refrigerant flow (VRF) systems are popularly used in office buildings. This study investigated VAV and VRF systems in five typical office buildings in China, and compared their air conditioning energy use. Site survey and field measurements were conducted to collect data of building characteristics and operation. Measured cooling electricity use was collected from sub-metering in the five buildings. The sub-metering data, normalized by climate and operating hours, show that VRF systems consumed much less air conditioning energy by up to 70% than VAV systems. This is mainly due to the different operation modes of both system types leading to much fewer operating hours of the VRF systems. Building simulation was used to quantify the impact of operation modes of VRF and VAV systems on cooling loads using a prototype office building in China. Simulated results show the VRF operation mode leads to much less cooling loads than the VAV operation mode, by 42% in Hong Kong and 53% in Qingdao. The VRF systems operated in the part-time-part-space mode enabling occupants to turn on air-conditioning only when needed and when spaces were occupied, while the VAV systems operated in the full-time-full-space mode limiting occupants’ control of operation. The findings provide insights into VRF systems operation and controls as well as its energy performance, which can inform HVAC designers on system selection and building operators or facility managers on improving VRF system operations.    


 

 

10abuilding simulation10acomparative analysis10aenergy performance10afield measurement10aVariable Air Volume (VAV) Systems10aVariable Refrigerant Flow (VRF) Systems1 aYu, Xinqiao1 aYan, Da1 aSun, Kaiyu1 aHong, Tianzhen1 aZhu, Dandan uhttps://simulationresearch.lbl.gov/publications/comparative-study-energy-performance02384nas a2200253 4500008003900000245010000039210006900139260001200208300001200220490000700232520158600239653002401825653001501849653002201864653002201886653002101908653002501929653002401954100001401978700001201992700001902004700001702023856009002040 2015 d00aData Analysis and Stochastic Modeling of Lighting Energy Use in Large Office Buildings in China0 aData Analysis and Stochastic Modeling of Lighting Energy Use in  c01/2015 a275-2870 v863 a

Lighting consumes about 20% to 40% of the total electricity use in large office buildings in China. Commonly in building simulations, static time schedules for typical weekdays, weekends and holidays are assumed to represent the dynamics of lighting energy use in buildings. This approach does not address the stochastic nature of lighting energy use, which can be influenced by occupant behavior in buildings. This study analyzes the main characteristics of lighting energy use over various timescales, based on the statistical analysis of measured lighting energy use data from 15 large office buildings in Beijing and Hong Kong. It was found that in these large office buildings, the 24-hourly variation in lighting energy use was mainly driven by the schedules of the building occupants. Outdoor illuminance levels had little impact on lighting energy use due to the lack of automatic daylighting controls (an effective retrofit measure to reduce lighting energy use) and the relatively small perimeter area exposed to natural daylight. A stochastic lighting energy use model for large office buildings was further developed to represent diverse occupant activities, at six different time periods throughout a day, and also the annual distribution of lighting power across these periods. The model was verified using measured lighting energy use from the 15 buildings. The developed stochastic lighting model can generate more accurate lighting schedules for use in building energy simulations, improving the simulation accuracy of lighting energy use in real buildings.

10abuilding simulation10aenergy use10aLighting modeling10aoccupant behavior10aoffice buildings10aPoisson distribution10astochastic modeling1 aZhou, Xin1 aYan, Da1 aHong, Tianzhen1 aRen, Xiaoxin uhttps://simulationresearch.lbl.gov/publications/data-analysis-and-stochastic-modeling02285nas a2200241 4500008003900000245007400039210006900113260001200182300000900194490000700203520156400210653002301774653002401797653001501821653001601836653001801852653002201870653001801892100001701910700001201927700001901939856008501958 2015 d00aData Mining of Space Heating System Performance in Affordable Housing0 aData Mining of Space Heating System Performance in Affordable Ho c07/2015 a1-130 v893 a

The space heating in residential buildings accounts for a considerable amount of the primary energy use. Therefore, understanding the operation and performance of space heating systems becomes crucial in improving occupant comfort while reducing energy use. This study investigated the behavior of occupants adjusting their thermostat settings and heating system operations in a 62-unit affordable housing complex in Revere, Massachusetts, USA. The data mining methods, including clustering approach and decision trees, were used to ascertain occupant behavior patterns. Data tabulating ON/OFF space heating states was assessed, to provide a better understanding of the intermittent operation of space heating systems in terms of system cycling frequency and the duration of each operation. The decision tree was used to verify the link between room temperature settings, house and heating system characteristics and the heating energy use. The results suggest that the majority of apartments show fairly constant room temperature profiles with limited variations during a day or between weekday and weekend. Data clustering results revealed six typical patterns of room temperature profiles during the heating season. Space heating systems cycled more frequently than anticipated due to a tight range of room thermostat settings and potentially oversized heating capacities. The results from this study affirm data mining techniques are an effective method to analyze large datasets and extract hidden patterns to inform design and improve operations.

10aaffordable housing10abuilding simulation10aclustering10adata mining10adecision tree10aoccupant behavior10aspace heating1 aRen, Xiaoxin1 aYan, Da1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/data-mining-space-heating-system02767nas a2200193 4500008003900000245008400039210006900123520210600192653002202298653002602320653002002346653003102366653002202397653002302419100001402442700001902456700001202475856008602487 2015 d00aAn Insight into Actual Energy Use and Its Drivers in High-Performance Buildings0 aInsight into Actual Energy Use and Its Drivers in HighPerformanc3 a

Using portfolio analysis and individual detailed case studies, we studied the energy performance and drivers of energy use in 51 high-performance office buildings in the U.S., Europe, China, and other parts of Asia. Portfolio analyses revealed that actual site energy use intensity (EUI) of the study buildings varied by a factor of as much as 11, indicating significant variation in real energy use in HPBs worldwide. Nearly half of the buildings did not meet the American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) Standard 90.1-2004 energy target, raising questions about whether a building’s certification as high performing accurately indicates that a building is energy efficient and suggesting that improvement in the design and operation of HPBs is needed to realize their energy-saving potential. We studied the influence of climate, building size, and building technologies on building energy performance and found that although all are important, none are decisive factors in building energy use. EUIs were widely scattered in all climate zones. There was a trend toward low energy use in small buildings, but the correlation was not absolute; some small HPBs exhibited high energy use, and some large HPBs exhibited low energy use. We were unable to identify a set of efficient technologies that correlated directly to low EUIs. In two case studies, we investigated the influence of occupant behavior as well as operation and maintenance on energy performance and found that both play significant roles in realizing energy savings. We conclude that no single factor determines the actual energy performance of HPBs, and adding multiple efficient technologies does not necessarily improve building energy performance; therefore, an integrated design approach that takes account of climate, technology, occupant behavior, and operations and maintenance practices should be implemented to maximize energy savings in HPBs. These findings are intended to help architects, engineers, operators, and policy makers improve the design and operation of HPBs.

10aactual energy use10abuilding technologies10adriving factors10ahigh-performance buildings10aintegrated design10aperformance rating1 aLi, Cheng1 aHong, Tianzhen1 aYan, Da uhttps://simulationresearch.lbl.gov/publications/insight-actual-energy-use-and-its01728nas a2200265 4500008003900000245010500039210006900144260001200213300001200225490000800237520089300245653002401138653002201162653002001184653001501204653002201219100001201241700002101253700001901274700001901293700001701312700002301329700002201352856008801374 2015 d00aOccupant Behavior Modeling for Building Performance Simulation: Current State and Future Challenges0 aOccupant Behavior Modeling for Building Performance Simulation C c11/2015 a264-2780 v1073 a

Occupant behavior is now widely recognized as a major contributing factor to uncertainty of building performance. While a surge of research on the topic has occurred over the past four decades, and particularly the past few years, there are many gaps in knowledge and limitations to current methodologies. This paper outlines the state-of-the-art research, current obstacles and future needs and directions for the following four-step iterative process: (1) occupant monitoring and data collection, (2) model development, (3) model evaluation, and (4) model implementation into building simulation tools. Major themes include the need for greater rigor in experimental methodologies; detailed, honest, and candid reporting of methods and results; and development of an efficient means to implement occupant behavior models and integrate them into building energy modeling programs.

10abuilding simulation10aenergy efficiency10aenergy modeling10aenergy use10aoccupant behavior1 aYan, Da1 aO'Brien, William1 aHong, Tianzhen1 aFeng, Xiaohang1 aGunay, Burak1 aTahmasebi, Farhang1 aMahdavi, Ardeshir uhttps://simulationresearch.lbl.gov/publications/occupant-behavior-modeling-building01903nas a2200229 4500008003900000245004100039210004100080260001200121300001200133490000700145520126600152653002401418653001801442653001401460653002201474653002001496653002401516100001901540700001201559700001901571856008301590 2015 d00aSimulation of Occupancy in Buildings0 aSimulation of Occupancy in Buildings c01/2015 a348-3590 v873 a

Occupants are involved in a variety of activities in buildings, which drive them to move among rooms, enter or leave a building. In this study, occupancy is defined at four levels and varies with time: (1) the number of occupants in a building, (2) occupancy status of a space, (3) the number of occupants in a space, and (4) the space location of an occupant. Occupancy has a great influence on internal loads and ventilation requirement, thus building energy consumption. Based on a comprehensive review and comparison of literature on occupancy modeling, three representative occupancy models, corresponding to the levels 2–4, are selected and implemented in a software module. Main contributions of our study include: (1) new methods to classify occupancy models, (2) the review and selection of various levels of occupancy models, and (3) new methods to integrate these model into a tool that can be used in different ways for different applications and by different audiences. The software can simulate more detailed occupancy in buildings to improve the simulation of energy use, and better evaluate building technologies in buildings. The occupancy of an office building is simulated as an example to demonstrate the use of the software module.

10abuilding simulation10aco-simulation10aoccupancy10aoccupant behavior10asoftware module10astochastic modeling1 aFeng, Xiaohang1 aYan, Da1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/simulation-occupancy-buildings02025nas a2200229 4500008003900000245008400039210006900123260001200192300001200204490000700216520132300223653002001546653002901566653001001595653002201605653001301627653002101640100001901661700001401680700001201694856008901706 2015 d00aUpdates to the China Design Standard for Energy Efficiency in Public Buildings0 aUpdates to the China Design Standard for Energy Efficiency in Pu c12/2015 a187-1980 v873 a

The China Design Standard for Energy Efficiency in public buildings (GB 50189) debuted in 2005 when China completed the 10th Five-Year Plan. GB 50189-2005 played a crucial role in regulating the energy efficiency in Chinese commercial buildings. The standard was recently updated in 2014 to increase energy savings targets by 30% compared with the 2005 standard. This paper reviews the major changes to the standard, including expansion of energy efficiency coverage and more stringent efficiency requirements. The paper also discusses the interrelationship of the design standard with China's other building energy standards. Furthermore, comparisons are made with ASHRAE Standard 90.1-2013 to provide contrasting differences in efficiency requirements. Finally recommendations are provided to guide the future standard revision, focusing on three areas: (1) increasing efficiency requirements of building envelope and HVAC systems, (2) adding a whole-building performance compliance pathway and implementing a ruleset based automatic code baseline model generation in an effort to reduce the discrepancies of baseline models created by different tools and users, and (3) adding inspection and commissioning requirements to ensure building equipment and systems are installed correctly and operate as designed.

10abuilding design10abuilding energy standard10aChina10aenergy efficiency10aGB 5018910aPublic buildings1 aHong, Tianzhen1 aLi, Cheng1 aYan, Da uhttps://simulationresearch.lbl.gov/publications/updates-china-design-standard-energy00559nas a2200181 4500008003900000245005300039210005300092100001900145700001400164700002500178700001200203700001400215700001400229700001500243700001500258700001700273856008700290 2014 d00aIntegrated Design for High Performance Buildings0 aIntegrated Design for High Performance Buildings1 aHong, Tianzhen1 aLi, Cheng1 aDiamond, Richard, C.1 aYan, Da1 aZhang, Qi1 aZhou, Xin1 aGuo, Siyue1 aSun, Kaiyu1 aWang, Jingyi uhttps://simulationresearch.lbl.gov/publications/integrated-design-high-performance01503nas a2200181 4500008003900000245009000039210006900129520087000198653003801068653002101106653002701127653002001154100001401174700001201188700001901200700001601219856008601235 2013 d00aBuilding energy modeling programs comparison Research on HVAC systems simulation part0 aBuilding energy modeling programs comparison Research on HVAC sy3 a

Building energy simulation programs are effective tools for the evaluation of building energy saving and optimization of design. The fact that large discrepancies exist in simulated results when different BEMPs are used to model the same building has caused wide concern. Urgent research is needed to identify the main elements that contribute towards the simulation results. This technical report summarizes methodologies, processes, and the main assumptions of three building energy modeling programs (BEMPs) for HVAC calculations: EnergyPlus, DeST, and DOE-2.1E, and test cases are designed to analyze the calculation process in detail. This will help users to get a better understanding of BEMPs and the research methodology of building simulation. This will also help build a foundation for building energy code development and energy labeling programs.

10aBuilding energy modeling programs10acomparison tests10aHVAC system simulation10atheory analysis1 aZhou, Xin1 aYan, Da1 aHong, Tianzhen1 aZhu, Dandan uhttps://simulationresearch.lbl.gov/publications/building-energy-modeling-programs00439nas a2200121 4500008003900000245006600039210006500105260001200170100001400182700001900196700001200215856009000227 2013 d00aComparison of Building Energy Modeling Programs: HVAC Systems0 aComparison of Building Energy Modeling Programs HVAC Systems c08/20131 aZhou, Xin1 aHong, Tianzhen1 aYan, Da uhttps://simulationresearch.lbl.gov/publications/comparison-building-energy-modeling-002003nas a2200217 4500008003900000245008000039210006900119520131400188653002401502653001501526653001301541653001301554653002201567653002101589653002501610100001401635700001201649700001701661700001901678856008801697 2013 d00aData Analysis and Modeling of Lighting Energy Use in Large Office Buildings0 aData Analysis and Modeling of Lighting Energy Use in Large Offic3 a

Lighting consumes about 20 to 40% of total electricity use in large office buildings in the U.S. and China. In order to develop better lighting simulation models it is crucial to understand the characteristics of lighting energy use. This paper analyzes the main characteristics of lighting energy use over various time scales, based on the statistical analysis of measured lighting energy use of 17 large office buildings in Beijing and Hong Kong. It was found that the daily 24-hour variations of lighting energy use were mainly driven by the schedule of the building occupants. Outdoor illumination levels have little impact on lighting energy use in large office buildings due to the lack of automatic daylighting controls and relatively small perimeter areas. A stochastic lighting energy use model was developed based on different occupant activities during six time periods throughout a day, and the annual distribution of lighting power across those periods. The model was verified using measured lighting energy use of one selected building. This study demonstrates how statistical analysis and stochastic modeling can be applied to lighting energy use. The developed lighting model can be adopted by building energy modeling programs to improve the simulation accuracy of lighting energy use.

10abuilding simulation10aenergy use10alighting10amodeling10aoccupant behavior10aoffice buildings10aPoisson distribution1 aZhou, Xin1 aYan, Da1 aRen, Xiaoxin1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/data-analysis-and-modeling-lighting02500nas a2200253 4500008003900000022003900039245010600078210006900184260003900253300001200292490000600304520167200310653003701982653002702019653001502046653000902061653001302070653001502083100001602098700001902114700001202133700001702145856008402162 2013 d aPrint: 1996-3599; Online 1996-874400aA Detailed Loads Comparison of Three Building Energy Modeling Programs: EnergyPlus, DeST and DOE-2.1E0 aDetailed Loads Comparison of Three Building Energy Modeling Prog bTsinghua University Pressc09/2013 a323-3350 v63 a

Building energy simulation is widely used to help design energy efficient building envelopes and HVAC systems, develop and demonstrate compliance of building energy codes, and implement building energy rating programs. However, large discrepancies exist between simulation results from different building energy modeling programs (BEMPs). This leads many users and stakeholders to lack confidence in the results from BEMPs and building simulation methods. This paper compared the building thermal load modeling capabilities and simulation results of three BEMPs: EnergyPlus, DeST and DOE-2.1E. Test cases, based upon the ASHRAE Standard 140 tests, were designed to isolate and evaluate the key influencing factors responsible for the discrepancies in results between EnergyPlus and DeST. This included the load algorithms and some of the default input parameters. It was concluded that there is little difference between the results from EnergyPlus and DeST if the input values are the same or equivalent despite there being many discrepancies between the heat balance algorithms. DOE-2.1E can produce large errors for cases when adjacent zones have very different conditions, or if a zone is conditioned part-time while adjacent zones are unconditioned. This was due to the lack of a strict zonal heat balance routine in DOE-2.1E, and the steady state handling of heat flow through interior walls and partitions. This comparison study did not produce another test suite, but rather a methodology to design tests that can be used to identify and isolate key influencing factors that drive the building thermal loads, and a process with which to carry them out.

10abuilding energy modeling program10abuilding thermal loads10acomparison10adest10aDOE-2.1E10aenergyplus1 aZhu, Dandan1 aHong, Tianzhen1 aYan, Da1 aWang, Chuang uhttps://simulationresearch.lbl.gov/publications/detailed-loads-comparison-three00784nas a2200241 4500008004100000245006200041210006200103260003200165653004300197653003700240653002400277653001500301653000900316653001000325653001500335653003000350653000900380100001600389700001900405700001200424700001700436856008900453 2012 eng d00aComparative research in building energy modeling programs0 aComparative research in building energy modeling programs aChina (in Chinese)c06/201110aadvanced building software: energyplus10abuilding energy modeling program10abuilding simulation10acomparison10adest10adoe-210aenergyplus10asimulation research group10atest1 aZhu, Dandan1 aHong, Tianzhen1 aYan, Da1 aWang, Chuang uhttps://simulationresearch.lbl.gov/publications/comparative-research-building-energy00666nas a2200229 4500008004100000245004000041210003800081260001800119653002400137653001500161653000900176653002000185653001500205653002400220653003000244653001500274100001600289700001700305700001200322700001900334856008300353 2011 eng d00aA Comparison of DeST and EnergyPlus0 aComparison of DeST and EnergyPlus aBeijingc201110abuilding simulation10acomparison10adest10aenergy modeling10aenergyplus10asimulation research10asimulation research group10atest cases1 aZhu, Dandan1 aWang, Chuang1 aYan, Da1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/comparison-dest-and-energyplus