02335nas a2200301 4500008004100000022001300041245011500054210006900169260001600238300001100254490000700265520135700272100002001629700001901649700003201668700001801700700002101718700002701739700002401766700003301790700001301823700001801836700002701854700002201881700001801903700002401921856008801945 2020 eng d a2214629600aCulture, conformity, and carbon? A multi-country analysis of heating and cooling practices in office buildings0 aCulture conformity and carbon A multicountry analysis of heating cJan-03-2020 a1013440 v613 a

This study investigates human-building interaction in office spaces across multiple countries including Brazil, Italy, Poland, Switzerland, the United States, and Taiwan. We analyze social-psychological, contextual, and demographic factors to explain cross-country differences in adaptive thermal actions (i.e. cooling and heating behaviors) and conformity to the norms of sharing indoor environmental control features, an indicator of energy consumption. Specifically, personal adjustments such as putting on extra clothes are generally preferred over technological solutions such as adjusting thermostats in reaction to thermal discomfort. Social-psychological factors including attitudes, perceived behavioral control, injunctive norms, and perceived impact of indoor environmental quality on work productivity influence occupants’ intention to conform to the norms of sharing environmental control features. Lastly, accessibility to environmental control features, office type, gender, and age are also important factors. These findings demonstrate the roles of social-psychological and certain contextual factors in occupants’
interactions with building design as well as their behavior of sharing environmental control features, both of which significantly influence building energy consumption, and thus, broader decarbonization.

1 aChen, Chien-fei1 aHong, Tianzhen1 ade Rubens, Gerardo, Zarazua1 aYilmaz, Selin1 aBandurski, Karol1 aBélafi, Zsófia, Deme1 aDe Simone, Marilena1 aBavaresco, Mateus, Vinícius1 aWang, Yu1 aLiu, Pei-ling1 aBarthelmes, Verena, M.1 aAdams, Jacqueline1 aD'Oca, Simona1 aPrzybylski, Łukasz uhttps://simulationresearch.lbl.gov/publications/culture-conformity-and-carbon-multi01921nas 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/S030626191930465901789nas a2200205 4500008004100000245013500041210006900176260001600245300000900261490000700270520100200277653012101279100001701400700001801417700001401435700001301449700001901462700001101481856009101492 2019 eng d00aRevealing Urban Morphology and Outdoor Comfort through Genetic Algorithm-Driven Urban Block Design in Dry and Hot Regions of China0 aRevealing Urban Morphology and Outdoor Comfort through Genetic A cJan-07-2019 a36830 v113 a

In areas with a dry and hot climate, factors such as strong solar radiation, high temperature, low humidity, dazzling light, and dust storms can tremendously reduce people’s thermal comfort. Therefore, researchers are paying more attention to outdoor thermal comfort in urban environments as part of urban design. This study proposed an automatic workflow to optimize urban spatial forms with the aim of improvement of outdoor thermal comfort conditions, characterized by the universal thermal climate index (UTCI). A city with a dry and hot climate—Kashgar, China—is further selected as an actual case study of an urban block and Rhino & Grasshopper is the platform used to conduct simulation and optimization process with the genetic algorithm. Results showed that in summer, the proposed method can reduce the averaged UTCI from 31.17 to 27.43 °C, a decrease of about 3.74 °C, and reduce mean radiation temperature (MRT) from 43.94 to 41.29 °C, a decrease of about 2.65 °C.

10adry and hot areas; outdoor thermal comfort; urban morphology; urban performance simulation; genetic algorithm-driven1 aXu, Xiaodong1 aYin, Chenhuan1 aWang, Wei1 aXu, Ning1 aHong, Tianzhen1 aLi, Qi uhttps://www.mdpi.com/2071-1050/11/13/3683https://www.mdpi.com/2071-1050/11/13/3683/pdf02762nas 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-conditioning02346nas a2200229 4500008004100000245009900041210006900140490000800209520158400217653003601801653001301837653002001850653001801870653001501888653003001903100002001933700001501953700001901968700002001987700002102007856008802028 2018 eng d00aA Novel Variable Refrigerant Flow (VRF) Heat Recovery System Model: Development and Validation0 aNovel Variable Refrigerant Flow VRF Heat Recovery System Model D0 v1683 a

As one of the latest emerging HVAC technologies, the Variable Refrigerant Flow (VRF) system with heat recovery (HR) configurations has obtained extensive attention from both the academia and industry. Compared with the conventional VRF systems with heat pump (HP) configurations, VRF-HR is capable of recovering heat from cooling zones to heating zones and providing simultaneous cooling and heating operations. This can further lead to substantial energy saving potential and more flexible zonal control. In this paper, a novel model is developed to simulate the energy performance of VRF-HR systems. It adheres to a more physics-based development with the ability to simulate the refrigerant loop performance and consider the dynamics of more operational parameters, which is essential for representing more advanced control logics. Another key feature of the model is the introduction of component-level curves for indoor units and outdoor units instead of overall performance curves for the entire system, and thus it requires much fewer user-specified performance curves as model inputs. The validation study shows good agreements between the simulated energy use from the new VRF-HR model and the laboratory measurement data across all operational modes at sub-hourly time steps. The model has been adopted in the official release of the EnergyPlus simulation program since Version 8.6, which enables more accurate and robust assessments of VRF-HR systems to support their applications in energy retrofit of existing buildings or design of zero-net-energy buildings.

10abuilding performance simulation10acontrols10aenergy modeling10aheat recovery10avalidation10aVariable refrigerant flow1 aZhang, Rongpeng1 aSun, Kaiyu1 aHong, Tianzhen1 aYura, Yoshinori1 aHinokuma, Ryohei uhttps://simulationresearch.lbl.gov/publications/novel-variable-refrigerant-flow-vrf02060nas 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-multiyear02000nas a2200205 4500008004100000245008800041210006900129490000800198520130700206653002701513653002001540653002201560653002201582653002701604653002001631100002101651700001901672700001701691856008601708 2017 eng d00aIEA EBC Annex 53: Total Energy Use in Buildings – Analysis and Evaluation Methods0 aIEA EBC Annex 53 Total Energy Use in Buildings Analysis and Eval0 v1523 a

One of the most significant barriers to achieving deep building energy efficiency is a lack of knowledge about the factors determining energy use. In fact, there is often a significant discrepancy between designed and real energy use in buildings, which is poorly understood but are believed to have more to do with the role of human behavior than building design. Building energy use is mainly influenced by six factors: climate, building envelope, building services and energy systems, building operation and maintenance, occupants’ activities and behavior, and indoor environmental quality. In the past, much research focused on the first three factors. However, the next three human-related factors can have an influence as significant as the first three. Annex 53 employed an interdisciplinary approach, integrating building science, architectural engineering, computer modeling and simulation, and social and behavioral science to develop and apply methods to analyze and evaluate the real energy use in buildings considering the six influencing factors. Outcomes from Annex 53 improved understanding and strengthen knowledge regarding the robust prediction of total energy use in buildings, enabling reliable quantitative assessment of energy-savings measures, policies, and techniques.

10aenergy data definition10aenergy modeling10aenergy monitoring10aoccupant behavior10aPerformance Evaluation10areal energy use1 aYoshino, Hiroshi1 aHong, Tianzhen1 aNord, Natasa uhttps://simulationresearch.lbl.gov/publications/iea-ebc-annex-53-total-energy-use02536nas 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-system02134nas a2200253 4500008003900000245009200039210006900131260001100200490000800211520133600219653002401555653002001579653001501599653001401614653002101628653003001649100001901679700001501698700002001713700002101733700002301754700002001777856008301797 2015 d00aDevelopment and validation of a new variable refrigerant flow systemmodel in EnergyPlus0 aDevelopment and validation of a new variable refrigerant flow sy c9/20150 v1173 a

Variable refrigerant flow (VRF) systems vary the refrigerant flow to meet the dynamic zone thermalloads, leading to more efficient operations than other system types. This paper introduces a new modelthat simulates the energy performance of VRF systems in the heat pump (HP) operation mode. Com-pared with the current VRF-HP models implemented in EnergyPlus, the new VRF system model has morecomponent models based on physics and thus has significant innovations in: (1) enabling advanced con-trols, including variable evaporating and condensing temperatures in the indoor and outdoor units, andvariable fan speeds based on the temperature and zone load in the indoor units, (2) adding a detailedrefrigerant pipe heat loss calculation using refrigerant flow rate, operational conditions, pipe length, andpipe insulation materials, (3) improving accuracy of simulation especially in partial load conditions, and(4) improving the usability of the model by significantly reducing the number of user input performancecurves. The VRF-HP model is implemented in EnergyPlus and validated with measured data from fieldtests. Results show that the new VRF-HP model provides more accurate estimate of the VRF-HP systemperformance, which is key to determining code compliance credits as well as utilities incentive for VRFtechnologies.

10abuilding simulation10aenergy modeling10aenergyplus10aHeat pump10amodel validation10aVariable refrigerant flow1 aHong, Tianzhen1 aSun, Kaiyu1 aZhang, Rongpeng1 aHinokuma, Ryohei1 aKasahara, Shinichi1 aYura, Yoshinori uhttps://simulationresearch.lbl.gov/publications/development-and-validation-new02767nas 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-energy02234nas a2200313 4500008003900000245007900039210006900118260001200187300001200199490000700211520134300218653001401561653001501575653001801590653001501608653002401623653002901647653001501676653001301691100001701704700001901721700001301740700001401753700001301767700001501780700001501795700002201810856008801832 2014 d00aComparison of Building Energy Use Data Between the United States and China0 aComparison of Building Energy Use Data Between the United States c08/2014 a165-1750 v783 a

Buildings in the United States and China consumed 41% and 28% of the total primary energy in 2011, respectively. Good energy data are the cornerstone to understanding building energy performance and supporting research, design, operation, and policy making for low energy buildings. This paper presents initial outcomes from a joint research project under the U.S.–China Clean Energy Research Center for Building Energy Efficiency. The goal is to decode the driving forces behind the discrepancy of building energy use between the two countries; identify gaps and deficiencies of current building energy monitoring, data collection, and analysis; and create knowledge and tools to collect and analyze good building energy data to provide valuable and actionable information for key stakeholders. This paper first reviews and compares several popular existing building energy monitoring systems in both countries. Next a standard energy data model is presented. A detailed, measured building energy data comparison was conducted for a few office buildings in both countries. Finally issues of data collection, quality, sharing, and analysis methods are discussed. It was found that buildings in both countries performed very differently, had potential for deep energy retrofit, but that different efficiency measures should apply.

10abuildings10acomparison10adata analysis10adata model10aEnergy benchmarking10aenergy monitoring system10aenergy use10aretrofit1 aXia, Jianjun1 aHong, Tianzhen1 aShen, Qi1 aFeng, Wei1 aYang, Le1 aIm, Piljae1 aLu, Alison1 aBhandari, Mahabir uhttps://simulationresearch.lbl.gov/publications/comparison-building-energy-use-data03531nas a2200241 4500008003900000245007900039210006900118260002200187300001100209490000800220520280200228653001403030653001503044653002403059653001503083653003103098653001303129100001903142700001303161700001603174700001403190856008503204 2014 d00aData and Analytics to Inform Energy Retrofit of High Performance Buildings0 aData and Analytics to Inform Energy Retrofit of High Performance bElsevierc08/2014 a90-1060 v1263 a

Buildings consume more than one-third of the world’s primary energy. Reducing energy use in buildings with energy efficient technologies is feasible and also driven by energy policies such as energy benchmarking, disclosure, rating, and labeling in both the developed and developing countries. Current energy retrofits focus on the existing building stocks, especially older buildings, but the growing number of new high performance buildings built around the world raises a question that how these buildings perform and whether there are retrofit opportunities to further reduce their energy use. This is a new and unique problem for the building industry. Traditional energy audit or analysis methods are inadequate to look deep into the energy use of the high performance buildings. This study aims to tackle this problem with a new holistic approach powered by building performance data and analytics. First, three types of measured data are introduced, including the time series energy use, building systems operating conditions, and indoor and outdoor environmental parameters. An energy data model based on the ISO Standard 12655 is used to represent the energy use in buildings in a three-level hierarchy. Secondly, a suite of analytics were proposed to analyze energy use and to identify retrofit measures for high performance buildings. The data-driven analytics are based on monitored data at short time intervals, and cover three levels of analysis – energy profiling, benchmarking and diagnostics. Thirdly, the analytics were applied to a high performance building in California to analyze its energy use and identify retrofit opportunities, including: (1) analyzing patterns of major energy end-use categories at various time scales, (2) benchmarking the whole building total energy use as well as major end-uses against its peers, (3) benchmarking the power usage effectiveness for the data center, which is the largest electricity consumer in this building, and (4) diagnosing HVAC equipment using detailed time-series operating data. Finally, a few energy efficiency measures were identified for retrofit, and their energy savings were estimated to be 20% of the whole-building electricity consumption. Based on the analyses, the building manager took a few steps to improve the operation of fans, chillers, and data centers, which will lead to actual energy savings. This study demonstrated that there are energy retrofit opportunities for high performance buildings and detailed measured building performance data and analytics can help identify and estimate energy savings and to inform the decision making during the retrofit process. Challenges of data collection and analytics were also discussed to shape best practice of retrofitting high performance buildings.

10aAnalytics10adata model10aEnergy benchmarking10aenergy use10aHigh performance buildings10aretrofit1 aHong, Tianzhen1 aYang, Le1 aHill, David1 aFeng, Wei uhttps://simulationresearch.lbl.gov/publications/data-and-analytics-inform-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-performance02607nas a2200169 4500008003900000245006200039210006000101520204900161100001902210700001802229700001902247700001702266700002302283700002002306700002102326856009002347 2014 d00aA New Model to Simulate Energy Performance of VRF Systems0 aNew Model to Simulate Energy Performance of VRF Systems3 a

This paper presents a new model to simulate energy performance of variable refrigerant flow (VRF) systems in heat pump operation mode (either cooling or heating is provided but not simultaneously). The main improvement of the new model is the introduction of the evaporating and condensing temperature in the indoor and outdoor unit capacity modifier functions. The independent variables in the capacity modifier functions of the existing VRF model in EnergyPlus are mainly room wet-bulb temperature and outdoor dry-bulb temperature in cooling mode and room dry-bulb temperature and outdoor wet-bulb temperature in heating mode. The new approach allows compliance with different specifications of each indoor unit so that the modeling accuracy is improved. The new VRF model was implemented in a custom version of EnergyPlus 7.2. This paper first describes the algorithm for the new VRF model, which is then used to simulate the energy performance of a VRF system in a Prototype House in California that complies with the requirements of Title 24 – the California Building Energy Efficiency Standards. The VRF system performance is then compared with three other types of HVAC systems: the Title 24-2005 Baseline system, the traditional High Efficiency system, and the EnergyStar Heat Pump system in three typical California climates: Sunnyvale, Pasadena and Fresno. Calculated energy savings from the VRF systems are significant. The HVAC site energy savings range from 51 to 85%, while the TDV (Time Dependent Valuation) energy savings range from 31 to 66% compared to the Title 24 Baseline Systems across the three climates. The largest energy savings are in Fresno climate followed by Sunnyvale and Pasadena. The paper discusses various characteristics of the VRF systems contributing to the energy savings. It should be noted that these savings are calculated using the Title 24 prototype House D under standard operating conditions. Actual performance of the VRF systems for real houses under real operating conditions will vary.

1 aHong, Tianzhen1 aPang, Xiufeng1 aSchetrit, Oren1 aWang, Liping1 aKasahara, Shinichi1 aYura, Yoshinori1 aHinokuma, Ryohei uhttps://simulationresearch.lbl.gov/publications/new-model-simulate-energy-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-programs05291nas a2200193 4500008003900000245004400039210004400083260001200127520474700139100001904886700001404905700001504919700001704934700001304951700001304964700001504977700002204992856008305014 2013 d00aBuilding Energy Monitoring and Analysis0 aBuilding Energy Monitoring and Analysis c06/20133 a

U.S. and China are the world's top two economics. Together they consumed one-third of the world's primary energy. It is an unprecedented opportunity and challenge for governments, researchers and industries in both countries to join together to address energy issues and global climate change. Such joint collaboration has huge potential in creating new jobs in energy technologies and services.

Buildings in the US and China consumed about 40% and 25% of the primary energy in both countries in 2010 respectively. Worldwide, the building sector is the largest contributor to the greenhouse gas emission. Better understanding and improving the energy performance of buildings is a critical step towards sustainable development and mitigation of global climate change.

This project aimed to develop a standard methodology for building energy data definition, collection, presentation, and analysis; apply the developed methods to a standardized energy monitoring platform, including hardware and software, to collect and analyze building energy use data; and compile offline statistical data and online real-time data in both countries for fully understanding the current status of building energy use. This helps decode the driving forces behind the discrepancy of building energy use between the two countries; identify gaps and deficiencies of current building energy monitoring, data collection, and analysis; and create knowledge and tools to collect and analyze good building energy data to provide valuable and actionable information for key stakeholders.

Key research findings were summarized as follows:

The research outputs from the project can help better understand energy performance of buildings, improve building operations to reduce energy waste and increase efficiency, identify retrofit opportunities for existing buildings, and provide guideline to improve the design of new buildings. The standardized energy monitoring and analysis platform as well as the collected real building data can also be used for other CERC projects that need building energy measurements, and be further linked to building energy benchmarking and rating/labeling systems.

1 aHong, Tianzhen1 aFeng, Wei1 aLu, Alison1 aXia, Jianjun1 aYang, Le1 aShen, Qi1 aIm, Piljae1 aBhandari, Mahabir uhttps://simulationresearch.lbl.gov/publications/building-energy-monitoring-and00439nas 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-energy02060nas a2200277 4500008003900000245009300039210006900132260001200201520117000213653001701383653001801400653001501418653003601433653002301469653001501492100001901507700002301526700001801549700003101567700001701598700001701615700002501632700001701657700002001674856008801694 2012 d00aMonitoring-based HVAC Commissioning of an Existing Office Building for Energy Efficiency0 aMonitoringbased HVAC Commissioning of an Existing Office Buildin c10/20123 a

The performance of Heating, Ventilation and Air Conditioning (HVAC) systems may fail to satisfy design expectations due to improper equipment installation, equipment degradation, sensor failures, or incorrect control sequences. Commissioning identifies and implements cost-effective operational and maintenance measures in buildings to bring them up to the design intent or optimum operation. An existing office building is used as a case study to demonstrate the process of commissioning. Building energy benchmarking tools are applied to evaluate the energy performance for screening opportunities at the whole building level. A large natural gas saving potential was indicated by the building benchmarking results. Faulty operations in the HVAC systems, such as improper operations of air-side economizers, simultaneous heating and cooling, and ineffective optimal start, were identified through trend data analyses and functional testing. The energy saving potential for each commissioning measure is quantified with a calibrated building simulation model. An actual energy saving of 10% was realized after the implementations of cost-effective measures.

10abenchmarking10acommissioning10aenergyplus10afault detection and diagnostics10afunctional testing10atrend data1 aEarni, Shankar1 aWoodworth, Spencer1 aPang, Xiufeng1 aHernandez-Maldonado, Jorge1 aYin, Rongxin1 aWang, Liping1 aGreenberg, Steve, E.1 aFiegel, John1 aRubalcava, Alma uhttps://simulationresearch.lbl.gov/publications/monitoring-based-hvac-commissioning00666nas 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-energyplus00473nas a2200121 4500008003900000245008000039210006900119260001200188100001900200700002700219700002100246856008400267 2009 d00aAssessment of Energy Impact of Window Technologies for Commercial Buildings0 aAssessment of Energy Impact of Window Technologies for Commercia c10/20091 aHong, Tianzhen1 aSelkowitz, Stephen, E.1 aYazdanian, Mehry uhttps://simulationresearch.lbl.gov/publications/assessment-energy-impact-window00674nas a2200205 4500008004100000245008400041210006900125260000900194490001500203653001600218653001000234653002300244653001500267653001500282100001900297700002100316700002100337700002100358856008900379 2008 eng d00aComparisons of HVAC Simulations between EnergyPlus and DOE-2.2 for data centers0 aComparisons of HVAC Simulations between EnergyPlus and DOE22 for c20090 v115 Part 110adata center10adoe-210aenergy performance10aenergyplus10asimulation1 aHong, Tianzhen1 aSartor, Dale, A.1 aMathew, Paul, A.1 aYazdanian, Mehry uhttps://simulationresearch.lbl.gov/publications/comparisons-hvac-simulations-between02284nas a2200145 4500008004100000245009700041210006900138260002400207520175000231100001301981700001901994700001702013700001902030856008902049 2006 eng d00aMeasured energy performance of a US-China demonstration energy-efficient commercial building0 aMeasured energy performance of a USChina demonstration energyeff aDallas, TXc01/20073 a

In July 1998, the U.S. Department of Energy (USDOE) and China's Ministry of Science of Technology (MOST) signed a Statement of Work (SOW) to collaborate on the design and construction of an energyefficient demonstration office building and design center to be located in Beijing. The proposed 13,000 m2 (140,000 ft2) nine-story office building would use U.S. energy-efficient materials, space-conditioning systems, controls, and design principles that were judged to be widely replicable throughout China. The SOW stated that China would contribute the land and provide for the costs of the base building, while the U.S. would be responsible for the additional (or marginal) costs associated with the package of energy efficiency andrenewable energy improvements to the building. The project was finished and the building occupied in 2004.

Using DOE-2 to analyze the energy performance of the as-built building, the building obtained 44 out of 69 possible points according to the Leadership in Energy and Environmental Design (LEED) rating, including the full maximum of 10 points in the energy performance section. The building achieved a LEED Gold rating, the first such LEED-rated office building in China, and is 60% more efficient than ASHRAE 90.1-1999. The utility data from the first year's operation match well the analysis results, providing that adjustments are made for unexpected changes in occupancy and operations. Compared with similarly equipped office buildings in Beijing, this demonstration building uses 60% less energy per floor area. However, compared to conventional office buildings with less equipment and window air-conditioners, the building uses slightly more energy per floor area.

1 aXu, Peng1 aHuang, Yu, Joe1 aJin, Ruidong1 aYang, Guoxiong uhttps://simulationresearch.lbl.gov/publications/measured-energy-performance-us-china01573nas a2200217 4500008004100000245009500041210006900136260001200205300001600217490000700233520084100240653001901081653002101100653004601121100002601167700001501193700002001208700002401228700002201252856008101274 2004 eng d00aBrownian Dynamics Simulation to Determine the Effective Thermal Conductivity of Nanofluids0 aBrownian Dynamics Simulation to Determine the Effective Thermal  c06/2004 a6492–64940 v953 a

A nanofluid is a fluid containing suspended solid particles, with sizes on the order of nanometers. Normally, nanofluids have higher thermal conductivities than their base fluids. Therefore, it is of interest to predict the effective thermal conductivity of such a nanofluid under different conditions, especially since only limited experimental data are available. We have developed a technique to compute the effective thermal conductivity of a nanofluid using Brownian dynamics simulation, which has the advantage of being computationally less expensive than molecular dynamics, and have coupled that with the equilibrium Green-Kubo method. By comparing the results of our calculation with the available experimental data, we show that our technique predicts the thermal conductivity of nanofluids to a good level of accuracy.

10acomplex fluids10aDisperse systems10aThermal conduction in nonmetallic liquids1 aBhattacharya, Prajesh1 aSaha, S.K.1 aYadav, Ajay, K.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S. uhttps://simulationresearch.lbl.gov/publications/brownian-dynamics-simulation00461nas a2200133 4500008004100000245005300041210005200094260001200146100001600158700002400174700002000198700002600218856008300244 2004 eng d00aNumerical Tools For Particle- Fluid Interactions0 aNumerical Tools For Particle Fluid Interactions c02/20041 aCalhoun, R.1 aPhelan, Patrick, E.1 aYadav, Ajay, K.1 aBhattacharya, Prajesh uhttps://simulationresearch.lbl.gov/publications/numerical-tools-particle-fluid00435nas a2200133 4500008004100000245005500041210005500096300001000151490000700161100001600168700001700184700001600201856008400217 2004 eng d00aUpdating traditional CRM system by terminal server0 aUpdating traditional CRM system by terminal server a94-950 v271 aZuo, Wangda1 aYang, Tianyi1 aZou, Wenyan uhttps://simulationresearch.lbl.gov/publications/updating-traditional-crm-system00573nas a2200145 4500008004100000245010100041210006900142260002700211100002600238700001500264700002000279700002400299700002200323856008200345 2003 eng d00aDetermining the Effective Thermal Conductivity of a Nanofluid Using Brownian Dynamics Simulation0 aDetermining the Effective Thermal Conductivity of a Nanofluid Us aLas Vegas, NVc07/20031 aBhattacharya, Prajesh1 aSaha, S.K.1 aYadav, Ajay, K.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S. uhttps://simulationresearch.lbl.gov/publications/determining-effective-thermal01863nas a2200193 4500008004100000024001500041245007000056210006900126260002800195520122600223100001801449700001901467700001901486700002201505700001601527700001801543700001801561856009001579 2000 eng d aLBNL-4845600aBetter IAQ Through Integrating Design Tools For The HVAC Industry0 aBetter IAQ Through Integrating Design Tools For The HVAC Industr aEspoo, Finlandc08/20003 a

There is currently no effective combination of interoperable design tools to cover all critical aspects of the HVAC design process. Existing design tools are separately available, but require expertise and operating time that is beyond the scope of a normal design project. For example, energy analysis and computational fluid dynamics (CFD) tools are not used in practical design, leading to poor indoor air quality and/or unnecessary energy consumption in buildings.

A prototype integrated software tool for demonstration, process mapping and proof-of-concept purposes was developed under a new international, Finland/USA jointly funded development project BildIT. Individual design tools were simplified and adapted to specific applications and also integrated so that they can be used in a timely and effective manner by the designer. The core of the prototype linked together an architectural CAD system, a 3D space model, a CFD program and a building energy simulation program and it utilises real product data from manufacturer's software. Also the complex building design, construction, maintenance and retrofit processes were mapped to get a template for the structure of the integrated design tool.

1 aLaine, Tuomas1 aKosonen, Risto1 aHagström, Kim1 aMustakallio, Panu1 aYin, De-Wei1 aHaves, Philip1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/better-iaq-through-integrating-design