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-multi02294nas a2200193 4500008004100000022001300041245005200054210005200106260001600158300001100174490000800185520173700193100001901930700001701949700001401966700001201980700002001992856008802012 2020 eng d a0360132300aTen questions on urban building energy modeling0 aTen questions on urban building energy modeling cJan-01-2020 a1065080 v1683 a

Buildings in cities consume up to 70% of all primary energy. To achieve cities’ energy and climate goals, it is necessary to reduce energy use and associated greenhouse gas emissions in buildings through energy conservation and efficiency improvements. Computational tools empowered with rich urban datasets can model performance of buildings at the urban scale to provide quantitative insights for stakeholders and inform their decision making on urban energy planning, as well as building energy retrofits at scale, to achieve efficiency, sustainability, and resilience of urban buildings.
Designing and operating urban buildings as a group (from a city block to a district to an entire city) rather than as single individuals requires simulation and optimization to account for interactions among buildings and between buildings and their surrounding urban environment, and for district energy systems serving multiple buildings with diverse thermal loads across space and time. When hundreds or more buildings are involved in typical urban building energy modeling (UBEM) to estimate annual energy demand, evaluate design or retrofit options, and quantify impacts of extreme weather events or climate change, it is crucial to integrate urban datasets and UBEM tools in a seamless automatic workflow with cloud or high-performance computing for users including urban planners, designers and researchers.
This paper presents ten questions that highlight significant UBEM research and applications. The proposed answers aim to stimulate discussion and provide insights into the current and future research on UBEM, and more importantly, to inspire new and important questions from young researchers in the field.

1 aHong, Tianzhen1 aChen, Yixing1 aLuo, Xuan1 aLuo, Na1 aLee, Sang, Hoon uhttps://simulationresearch.lbl.gov/publications/ten-questions-urban-building-energy01804nas a2200265 4500008004100000022001300041245010900054210006900163260001600232300001100248490000800259520098000267653001601247653002201263653002101285653002501306653002401331100001401355700001901369700001301388700001701401700001601418700001901434856008501453 2019 eng d a0360132300aCross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering0 aCrosssource sensing data fusion for building occupancy predictio cJan-09-2019 a1062800 v1623 a

Fusing various sensing data sources can significantly improve the accuracy and reliability of building occupancy detection. Fusing environmental sensors and wireless network signals are seldom studied for its computational and technical complexity. This study aims to propose an integrated adaptive lasso model that is able to extract critical data features for environmental and Wi-Fi probe dual sensing sources. Through rapid feature extraction and process simplification, the proposed method aims to improve the computational efficiency of occupancy detecting models. To validate the proposed model, an onsite experiment was conducted to examine two occupancy data resolutions, (real-time and four-level occupancy resolutions). The results suggested that, among all twelve features, eight features are most relevant. The mean absolute error of the real-time occupancy can be reduced to 2.18 and F1_accuracy is about 84.36% for the four-level occupancy.

10adata fusion10aFeature selection10aMachine learning10aoccupancy prediction10aPhysics-based model1 aWang, Wei1 aHong, Tianzhen1 aXu, Ning1 aXu, Xiaodong1 aChen, Jiayu1 aShan, Xiaofang uhttps://simulationresearch.lbl.gov/publications/cross-source-sensing-data-fusion02341nas a2200241 4500008004100000022001300041245007700054210006900131260001200200300001400212490000800226520145500234653002601689653001201715653001701727653001901744653003501763100001701798700001901815700001401834700001801848856023301866 2019 eng d a0378778800aDevelopment of city buildings dataset for urban building energy modeling0 aDevelopment of city buildings dataset for urban building energy  c11/2018 a252 - 2650 v1833 a

Urban building energy modeling (UBEM) is becoming a proven tool to support energy efficiency programs for buildings in cities. Development of a city-scale dataset of the existing building stock is a critical step of UBEM to automatically generate energy models of urban buildings and simulate their performance. This study introduces data needs, data standards, and data sources to develop city building datasets for UBEM. First, a literature review of data needs for UBEM was conducted. Then, the capabilities of the current data standards for city building datasets were reviewed. Moreover, the existing public data sources from several pioneer cites were studied to evaluate whether they are adequate to support UBEM. The results show that most cities have adequate public data to support UBEM; however, the data are represented in different formats without standardization, and there is a lack of common keys to make the data mapping easier. Finally, a case study is presented to integrate the diverse data sources from multiple city departments of San Francisco. The data mapping process is introduced and discussed. It is recommended to use the unique building identifiers as the common keys in the data sources to simplify the data mapping process. The integration methods and workflow are applied to other U.S. cities for developing the city-scale datasets of their existing building stock, including San Jose, Los Angeles, and Boston.

10aCity building dataset10aCityGML10aData mapping10aData standards10aUrban Building Energy Modeling1 aChen, Yixing1 aHong, Tianzhen1 aLuo, Xuan1 aHooper, Barry uhttps://linkinghub.elsevier.com/retrieve/pii/S0378778818316852https://api.elsevier.com/content/article/PII:S0378778818316852?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0378778818316852?httpAccept=text/plain02259nas a2200265 4500008004100000022001300041245013000054210006900184260001200253300001400265490000800279520141300287653002901700653002101729653002701750653001901777653003601796100001401832700001901846700001701865700001601882700001501898700001301913856006701926 2019 eng d a0306261900aForecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm0 aForecasting districtscale energy dynamics through integrating bu c04/2019 a217 - 2300 v2483 a

With the development of data-driven techniques, district-scale building energy prediction has attracted increasing attention in recent years for revealing energy use patterns and reduction potentials. However, data acquisition in large building groups is difficult and adjacent buildings also interact with each other. To reduce data cost and incorporate the inter-building impact with the data-driven building energy model, this study proposes a deep learning predictive approach that fuses the building network model with a long short-term memory learning model for district-scale building energy modeling. The building network was constructed based on correlations between the energy use intensity of buildings, which can significantly reduce the computational complexity of the deep learning models for energy dynamic prediction. Five typical building groups with energy use data from 2015 to 2018 on two institutional campuses were selected to perform the validation experiment with TensorFlow. Based on the prediction error assessments, the results suggest that for total building energy use intensity prediction, the proposed model can achieve a mean absolute percentage error of 6.66% and a root mean square error of 0.36 kWh/m2, compared to 12.05% and 0.63 kWh/m2 of the conventional artificial neural network model and to 11.06% and 0.89 kWh/m2 for the support vector regression model.

10aBuilding Energy Modeling10aBuilding network10aData-driven prediction10aDistrict-scale10aLong short-term memory networks1 aWang, Wei1 aHong, Tianzhen1 aXu, Xiaodong1 aChen, Jiayu1 aLiu, Ziang1 aXu, Ning uhttps://linkinghub.elsevier.com/retrieve/pii/S030626191930749402515nas a2200241 4500008004100000022001300041245010300054210006900157260001200226300001200238490000800250520174400258653003102002653002102033653003902054653002602093653002102119100001702140700001402157700001902171700001602190856006702206 2019 eng d a0378778800aIncorporating machine learning with building network analysis to predict multi-building energy use0 aIncorporating machine learning with building network analysis to c06/2019 a80 - 970 v1863 a

Predicting multi-building energy use at campus or city district scale has recently gained more attention; and more researchers have started to define reference buildings and study inter-impact between building groups. However, how to integrate the relationship to define reference buildings and predict multi-building energy use, using significantly less amount of building data and reducing complexity of prediction models, remains an open research question. To resolve this, this study proposed a novel method to predict multi-building energy use by integrating a social network analysis (SNA) with an Artificial Neural Network (ANN) technique. The SNA method was used to establish a building network (BN) by identifying reference buildings and determine correlations between reference buildings and non-reference buildings. The ANN technique was applied to learn correlations and historical building energy use, and then used to predict multi-building energy use. To validate the SNA-ANN method, 17 buildings in the Southeast University campus, located in Nanjing, China, were studied. These buildings have three years of actual monthly electricity use data and were grouped into four types: office, educational, laboratory, and residential. The results showed the integrated SNA-ANN method achieved average prediction accuracies of 90.67% for the office group, 90.79% for the educational group, 92.34% for the laboratory group, and 83.32% for the residential group. The results demonstrated the proposed SNA-ANN method achieved an accuracy of 90.28% for the predicted energy use for all building groups. Finally, this study provides insights into advancing the interdisciplinary research on multi-building energy use prediction.

10aArtificial neural networks10aBuilding network10acold winter and hot summer climate10aEnergy use prediction10aMachine learning1 aXu, Xiaodong1 aWang, Wei1 aHong, Tianzhen1 aChen, Jiayu uhttps://linkinghub.elsevier.com/retrieve/pii/S037877881831976502424nas a2200241 4500008004100000022001300041245013600054210006900190260001200259300001200271490000800283520161400291653002301905653003401928653002301962653002701985100001402012700001902026700001202045700001902057700001602076856009002092 2019 eng d a0306261900aLinking energy-cyber-physical systems with occupancy prediction and interpretation through WiFi probe-based ensemble classification0 aLinking energycyberphysical systems with occupancy prediction an c02/2019 a55 - 690 v2363 a

With rapid advances in sensing and digital technologies, cyber-physical systems are regarded as the most prominent platforms to improve building design and management. Researchers investigated the possibility of integrating energy management system with cyber-physical systems as energy-cyber-physical systems to promote building energy management. However, minimizing energy consumption while fulfilling building functions for energy-cyber-physical systems is challenging due to the dynamics of building occupants. As occupant behavior is one major source of uncertainties for energy management, ignoring it often results in energy wastes caused by overheating and overcooling as well as discomfort due to insufficient thermal and ventilation services. To mitigate such uncertainties, this study proposed an occupancy linked energy-cyber-physical system that incorporates WiFi probe-based occupancy detection. The proposed framework utilized ensemble classification algorithms to extract three types of occupancy information. It creates a data interface to link energy management system and cyber-physical systems and allows automated occupancy detection and interpretation through assembling multiple weak classifiers for WiFi signals. A validation experiment in a large office room was conducted to examine the performance of the proposed occupancy linked energy-cyber-physical systems. The experiment and simulation results suggest that, with a proper classifier and occupancy type, the proposed model can potentially save about 26.4% of energy consumption from the cooling and ventilation demands.

10aBuilding occupancy10aEnergy-Cyber-Physical Systems10aensemble algorithm10aWi-Fi probe technology1 aWang, Wei1 aHong, Tianzhen1 aLi, Nan1 aWang, Ryan, Qi1 aChen, Jiayu uhttps://simulationresearch.lbl.gov/publications/linking-energy-cyber-physical-systems01921nas 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/S030626191930465902188nas a2200301 4500008004100000245011100041210006900152260001600221520124300237653001701480653002401497653002901521653002501550100001601575700002001591700001501611700001401626700001901640700001601659700001501675700002001690700001801710700001801728700002001746700002001766700001801786856008201804 2019 eng d00aPrototyping the BOPTEST Framework for Simulation-Based Testing of Advanced Control Strategies in Buildings0 aPrototyping the BOPTEST Framework for SimulationBased Testing of aRome, Italy3 a

Advanced control strategies are becoming increasingly necessary in buildings in order to meet and balance requirements for energy efficiency, demand flexibility, and occupant comfort. Additional development and widespread adoption of emerging control strategies, however, ultimately require low implementation costs to reduce payback period and verified performance to gain control vendor, building owner, and operator trust. This is difficult in an already first-cost driven and risk-averse industry. Recent innovations in building simulation can significantly aid in meeting these requirements and spurring innovation at early stages of development by evaluating performance, comparing state-of-the-art to new strategies, providing installation experience, and testing controller implementations. This paper presents the development of a simulation framework consisting of test cases and software platform for the testing of advanced control strategies (BOPTEST - Building Optimization Performance Test). The objectives and requirements of the framework, components of a test case, and proposed software platform architecture are described, and the framework is demonstrated with a prototype implementation and example test case.

10abenchmarking10abuilding simulation10aModel predictive control10asoftware development1 aBlum, David1 aJorissen, Filip1 aHuang, Sen1 aChen, Yan1 aArroyo, Javier1 aBenne, Kyle1 aLi, Yanfei1 aGavan, Valentin1 aRivalin, Lisa1 aHelsen, Lieve1 aVrabie, Draguna1 aWetter, Michael1 aSofos, Marina uhttps://simulationresearch.lbl.gov/publications/prototyping-boptest-framework02513nas a2200241 4500008004100000245010500041210006900146490000800215520166100223653003101884653003301915653003201948653002201980653002102002653002502023100001802048700002502066700002402091700002702115700001902142700002602161856008402187 2018 eng d00aHuman-building interaction at work: Findings from an interdisciplinary cross-country survey in Italy0 aHumanbuilding interaction at work Findings from an interdiscipli0 v1323 a

This study presents results from an interdisciplinary survey assessing contextual and behavioral factors driving occupants' interaction with building and systems in offices located across three different Mediterranean climates in Turin (Northern), Perugia (Central), and Rende (Southern) Italy. The survey instrument is grounded in an interdisciplinary framework that bridges the gap between building physics and social science environments on the energy- and comfort-related human-building interaction in the workspace. Outcomes of the survey questionnaire provide insights into four key learning objectives: (1) individual occupant's motivational drivers regarding interaction with shared building environmental controls (such as adjustable thermostats, operable windows, blinds and shades, and artificial lighting), (2) group dynamics such as perceived social norms, attitudes, and intention to share controls, (3) occupant perception of the ease of use and knowledge of how to operate control systems, and (4) occupant-perceived comfort, satisfaction, and productivity. This study attempts to identify climatic, cultural, and socio-demographic influencing factors, as well as to establish the validity of the survey instrument and robustness of outcomes for future studies. Also, the paper aims at illustrating why and how social science insights can bring innovative knowledge into the adoption of building technologies in shared contexts, thus enhancing perceived environmental satisfaction and effectiveness of personal indoor climate control in office settings and impacting office workers' productivity and reduced operational energy costs.

10aHuman-building interaction10aindoor environmental comfort10ainterdisciplinary framework10aoccupant behavior10aoffice buildings10aquestionnaire survey1 aD'Oca, Simona1 aPisello, Anna, Laura1 aDe Simone, Marilena1 aBarthelmes, Verena, M.1 aHong, Tianzhen1 aCorgnati, Stefano, P. uhttps://simulationresearch.lbl.gov/publications/human-building-interaction-work02692nas a2200193 4500008004100000245010800041210006900149490000800218520202000226653001202246653001502258653002102273653002802294653003502322653001802357100001702375700001902392856008702411 2018 eng d00aImpacts of Building Geometry Modeling Methods on the Simulation Results of Urban Building Energy Models0 aImpacts of Building Geometry Modeling Methods on the Simulation 0 v2153 a

Urban-scale building energy modeling (UBEM)—using building modeling to understand how a group of buildings will perform together—is attracting increasing attention in the energy modeling field. Unlike modeling a single building, which will use detailed information, UBEM generally uses existing building stock data consisting of high-level building information. This study evaluated the impacts of three zoning methods and the use of floor multipliers on the simulated energy use of 940 office and retail buildings in three climate zones using City Building Energy Saver. The first zoning method, OneZone, creates one thermal zone per floor using the target building’s footprint. The second zoning method, AutoZone, splits the building’s footprint into perimeter and core zones. A novel, pixel-based automatic zoning algorithm is developed for the AutoZone method. The third zoning method, Prototype, uses the U.S. Department of Energy’s reference building prototype shapes. Results show that simulated source energy use of buildings with the floor multiplier are marginally higher by up to 2.6% than those modeling each floor explicitly, which take two to three times longer to run. Compared with the AutoZone method, the OneZone method results in decreased thermal loads and less equipment capacities: 15.2% smaller fan capacity, 11.1% smaller cooling capacity, 11.0% smaller heating capacity, 16.9% less heating loads, and 7.5% less cooling loads. Source energy use differences range from -7.6% to 5.1%. When comparing the Prototype method with the AutoZone method, source energy use differences range from -12.1% to 19.0%, and larger ranges of differences are found for the thermal loads and equipment capacities. This study demonstrated that zoning methods have a significant impact on the simulated energy use of UBEM. One recommendation resulting from this study is to use the AutoZone method with floor multiplier to obtain accurate results while balancing the simulation run time for UBEM.

10aCityBES10aenergyplus10aFloor multiplier10aGeometry Representation10aUrban Building Energy Modeling10aZoning Method1 aChen, Yixing1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/impacts-building-geometry-modeling02433nas a2200193 4500008004100000245009600041210006900137490000800206520175100214653002201965653001501987653004302002653002702045653002802072100001402100700001602114700001902130856009002149 2018 eng d00aModeling occupancy distribution in large spaces with multi-feature classification algorithm0 aModeling occupancy distribution in large spaces with multifeatur0 v1373 a

Occupancy information enables robust and flexible control of heating, ventilation, and air-conditioning (HVAC) systems in buildings. In large spaces, multiple HVAC terminals are typically installed to provide cooperative services for different thermal zones, and the occupancy information determines the cooperation among terminals. However, a person count at room-level does not adequately optimize HVAC system operation due to the movement of occupants within the room that creates uneven load distribution. Without accurate knowledge of the occupants' spatial distribution, the uneven distribution of occupants often results in under-cooling/heating or over-cooling/heating in some thermal zones. Therefore, the lack of high-resolution occupancy distribution is often perceived as a bottleneck for future improvements to HVAC operation efficiency. To fill this gap, this study proposes a multi-feature k-Nearest-Neighbors (k-NN) classification algorithm to extract occupancy distribution through reliable, low-cost Bluetooth Low Energy (BLE) networks. An on-site experiment was conducted in a typical office of an institutional building to demonstrate the proposed methods, and the experiment outcomes of three case studies were examined to validate detection accuracy. One method based on City Block Distance (CBD) was used to measure the distance between detected occupancy distribution and ground truth and assess the results of occupancy distribution. The results show the accuracy when CBD = 1 is over 71.4% and the accuracy when CBD = 2 can reach up to 92.9%.

10aenergy efficiency10aHVAC loads10amulti-feature classification algorithm10aoccupancy distribution10aoccupancy-based control1 aWang, Wei1 aChen, Jiayu1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/modeling-occupancy-distribution-large02356nas a2200229 4500008004100000022001300041245012200054210006900176260001200245300001400257490000700271520146000278653001601738653002601754653002101780653002501801653001801826100001401844700001601858700001901874856023301893 2018 eng d a0926580500aOccupancy prediction through machine learning and data fusion of environmental sensing and Wi-Fi sensing in buildings0 aOccupancy prediction through machine learning and data fusion of c10/2018 a233 - 2430 v943 a

Occupancy information is crucial to building facility design, operation, and energy efficiency. Many studies propose the use of environmental sensors (such as carbon dioxide, air temperature, and relative humidity sensors) and radio-frequency sensors (Wi-Fi networks) to monitor, assess, and predict occupancy information for buildings. As many methods have been developed and a variety of sensory data sources are available, establishing a proper selection of model and data source is critical to the successful implementation of occupancy prediction systems. This study compared three popular machine learning algorithms, including k-nearest neighbors (kNN), support vector machine (SVM), and artificial neural network (ANN), combined with three data sources, including environmental data, Wi-Fi data, and fused data, to optimize the occupancy models' performance in various scenarios. Three error measurement metrics, the mean average error (MAE), mean average percentage error (MAPE), and root mean squared error (RMSE), have been employed to compare the models' accuracies. Examined with an on-site experiment, the results suggest that the ANN-based model with fused data has the best performance, while the SVM model is more suitable with Wi-Fi data. The results also indicate that, comparing with independent data sources, the fused data set does not necessarily improve model accuracy but shows a better robustness for occupancy prediction.

10adata fusion10aenvironmental sensing10aMachine learning10aoccupancy prediction10aWi-Fi sensing1 aWang, Wei1 aChen, Jiayu1 aHong, Tianzhen uhttps://linkinghub.elsevier.com/retrieve/pii/S0926580518302656https://api.elsevier.com/content/article/PII:S0926580518302656?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0926580518302656?httpAccept=text/plain02098nas a2200181 4500008004100000022001300041245012600054210006900180260001200249300001400261490000800275520133900283100001401622700001601636700001901652700001201671856023301683 2018 eng d a0360132300aOccupancy prediction through Markov based feedback recurrent neural network (M-FRNN) algorithm with WiFi probe technology0 aOccupancy prediction through Markov based feedback recurrent neu c06/2018 a160 - 1700 v1383 a

Accurate occupancy prediction can improve facility control and energy efficiency of buildings. In recent years, buildings' exiting WiFi infrastructures have been widely studied in the research of occupancy and energy conservation. However, using WiFi to assess occupancy is challenging due to that occupancy information is often characterized stochastically and varies with time and easily disturbed by building components. To overcome such limitations, this study utilizes WiFi probe technology to actively scan WiFi connection requests and responses between access points and network devices of building occupants. With captured signals, this study proposed a Markov based feedback recurrent neural network (M-FRNN) algorithm to model and predict the occupancy profiles. One on-site experiment was conducted to collect ground truth data using camera-based video analysis and the results were used to validate the M-FRNN occupancy prediction model over a 9-day measurement period. From the results, the M-FRNN based occupancy model using WiFi probes shows best accuracies can reach 80.9%, 89.6%, and 93.9% with a tolerance of 2, 3, and 4 occupants respectively. This study demonstrated that WiFi data coupled with stochastic machine learning system can provide a viable alternative to determine a building's occupancy profile.

1 aWang, Wei1 aChen, Jiayu1 aHong, Tianzhen1 aZhu, Na uhttps://linkinghub.elsevier.com/retrieve/pii/S0360132318302464https://api.elsevier.com/content/article/PII:S0360132318302464?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0360132318302464?httpAccept=text/plain01823nas a2200241 4500008004100000022001400041245009000055210006900145260001200214300001100226520103500237653001301272653002001285653001501305653002701320653000801347100001701355700001401372700001901386700001901405700002101424856013601445 2018 eng d a1940-149300aRepresentation and evolution of urban weather boundary conditions in downtown Chicago0 aRepresentation and evolution of urban weather boundary condition c11/2018 a1 - 143 a

This study presents a novel computing technique for data exchange and coupling between a high-resolution weather simulation model and a building energy model, with a goal of evaluating the impact of urban weather boundary conditions on energy performance of urban buildings. The Weather Research and Forecasting (WRF) model is initialized with the operational High-Resolution Rapid Refresh (HRRR) dataset to provide hourly weather conditions over the Chicago region. We utilize the building footprint, land use, and building stock datasets to generate building energy models using EnergyPlus. We mapped the building exterior surfaces to local air nodes to import simulated microclimate data and to export buildings' heat emissions to their local environment. Preliminary experiments for a test area in Chicago show that predicted building cooling energy use differs by about 4.7% for the selected date when compared with simulations using TMY weather data and without considering the urban microclimate boundary conditions.

10acoupling10aenergy modeling10aenergyplus10aUrban climate modeling10aWRF1 aJain, Rajeev1 aLuo, Xuan1 aSever, Gökhan1 aHong, Tianzhen1 aCatlett, Charlie uhttps://www.tandfonline.com/doi/full/10.1080/19401493.2018.1534275https://www.tandfonline.com/doi/pdf/10.1080/19401493.2018.153427502543nas a2200277 4500008004100000022001300041245018600054210006900240260001200309300001400321490000800335520142800343653002701771653002701798653001801825653003601843100001901879700002001898700002201918700002101940700002401961700001301985700001501998700001902013856023302032 2018 eng d a0306261900aTranslating climate change and heating system electrification impacts on building energy use to future greenhouse gas emissions and electric grid capacity requirements in California0 aTranslating climate change and heating system electrification im c09/2018 a522 - 5340 v2253 a

Climate change and increased electrification of space and water heating in buildings can significantly affect future electricity demand and hourly demand profiles, which has implications for electric grid greenhouse gas emissions and capacity requirements. We use EnergyPlus to quantify building energy demand under historical and under several climate change projections of 32 kinds of building prototypes in 16 different climate zones of California and imposed these impacts on a year 2050 electric grid configuration by simulation in the Holistic Grid Resource Integration and Deployment (HIGRID) model. We find that climate change only prompted modest increases in grid resource capacity and negligible difference in greenhouse gas emissions since the additional electric load generally occurred during times with available renewable generation. Heating electrification, however, prompted a 30–40% reduction in greenhouse gas emissions but required significant grid resource capacity increases, due to the higher magnitude of load increases and lack of readily available renewable generation during the times when electrified heating loads occurred. Overall, this study translates climate change and electrification impacts to system-wide endpoint impacts on future electric grid configurations and highlights the complexities associated with translating building-level impacts to electric system-wide impacts.

10aBuilding Energy Demand10aClimate Change Impacts10aelectric grid10aHeating Electrification Effects1 aTarroja, Brian1 aChiang, Felicia1 aAghaKouchak, Amir1 aSamuelsen, Scott1 aRaghavan, Shuba, V.1 aWei, Max1 aSun, Kaiyu1 aHong, Tianzhen uhttps://linkinghub.elsevier.com/retrieve/pii/S0306261918306962https://api.elsevier.com/content/article/PII:S0306261918306962?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0306261918306962?httpAccept=text/plain02102nas a2200121 4500008004100000245005000041210004600091520170800137100001701845700001901862700001401881856008501895 2017 eng d00aAn Agent-Based Stochastic Occupancy Simulator0 aAgentBased Stochastic Occupancy Simulator3 a

Occupancy has significant impacts on building performance. However, in current building performance simulation programs, occupancy inputs are static and lack diversity, contributing to discrepancies between the simulated and actual building performance. This paper presents an Occupancy Simulator that simulates the stochastic behavior of occupant presence and movement in buildings, capturing the spatial and temporal occupancy diversity. Each occupant and each space in the building are explicitly simulated as an agent with their profiles of stochastic behaviors. The occupancy behaviors are represented with three types of models: (1) the status transition events (e.g., first arrival in office) simulated with Reinhart’s LIGHTSWITCH-2002 model, (2) the random moving events (e.g., from one office to another) simulated with Wang’s homogeneous Markov chain model, and (3) the meeting events simulated with a new stochastic model. A hierarchical data model was developed for the Occupancy Simulator, which reduces the amount of data input by using the concepts of occupant types and space types. Finally, a case study of a small office building is presented to demonstrate the use of the Simulator to generate detailed annual sub-hourly occupant schedules for individual spaces and the whole building. The Simulator is a web application freely available to the public and capable of performing a detailed stochastic simulation of occupant presence and movement in buildings. Future work includes enhancements in the meeting event model, consideration of personal absent days, verification and validation of the simulated occupancy results, and expansion for use with residential buildings.

1 aChen, Yixing1 aHong, Tianzhen1 aLuo, Xuan uhttps://simulationresearch.lbl.gov/publications/agent-based-stochastic-occupancy02592nas a2200193 4500008004100000245013700041210006900178520187800247653002902125653001202154653003302166653001502199653002202214653001602236100001702252700001902269700002202288856008802310 2017 eng d00aAutomatic Generation and Simulation of Urban Building Energy Models Based on City Datasets for City-Scale Building Retrofit Analysis0 aAutomatic Generation and Simulation of Urban Building Energy Mod3 a

Buildings in cities consume 30% to 70% of total primary energy, and improving building energy efficiency is one of the key strategies towards sustainable urbanization. Urban building energy models (UBEM) can support city managers to evaluate and prioritize energy conservation measures (ECMs) for investment and the design of incentive and rebate programs. This paper presents the retrofit analysis feature of City Building Energy Saver (CityBES) to automatically generate and simulate UBEM using EnergyPlus based on cities’ building datasets and user-selected ECMs. CityBES is a new open web-based tool to support city-scale building energy efficiency strategic plans and programs. The technical details of using CityBES for UBEM generation and simulation are introduced, including the workflow, key assumptions, and major databases. Also presented is a case study that analyzes the potential retrofit energy use and energy cost savings of five individual ECMs and two measure packages for 940 office and retail buildings in six city districts in northeast San Francisco, United States. The results show that: (1) all five measures together can save 23%-38% of site energy per building; (2) replacing lighting with light-emitting diode lamps and adding air economizers to existing heating, ventilation and air-conditioning (HVAC) systems are most cost-effective with an average payback of 2.0 and 4.3 years, respectively; and (3) it is not economical to upgrade HVAC systems or replace windows in San Franciso due to the city’s mild climate and minimal cooling and heating loads. The CityBES retrofit analysis feature does not require users to have deep knowledge of building systems or technologies for the generation and simulation of building energy models, which helps overcome major technical barriers for city managers and their consultants to adopt UBEM.

10aBuilding Energy Modeling10aCityBES10aEnergy conservation measures10aenergyplus10aRetrofit Analysis10aUrban Scale1 aChen, Yixing1 aHong, Tianzhen1 aPiette, Mary, Ann uhttps://simulationresearch.lbl.gov/publications/automatic-generation-and-simulation02060nas 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-multiyear02489nas a2200205 4500008004100000245008600041210006900127520180800196653001702004653002002021653002102041653001702062653001502079653003202094100001402126700001902140700001702159700002202176856008502198 2017 eng d00aElectric Load Shape Benchmarking for Small- and Medium-Sized Commercial Buildings0 aElectric Load Shape Benchmarking for Small and MediumSized Comme3 a

Small- and medium-sized commercial buildings owners and utility managers often look for opportunities for energy cost savings through energy efficiency and energy waste minimization. However, they currently lack easy access to low-cost tools that help interpret the massive amount of data needed to improve understanding of their energy use behaviors. Benchmarking is one of the techniques used in energy audits to identify which buildings are priorities for an energy analysis. Traditional energy performance indicators, such as the energy use intensity (annual energy per unit of floor area), consider only the total annual energy consumption, lacking consideration of the fluctuation of energy use behavior over time, which reveals the time of use information and represents distinct energy use behaviors during different time spans. To fill the gap, this study developed a general statistical method using 24-hour electric load shape benchmarking to compare a building or business/tenant space against peers. Specifically, the study developed new forms of benchmarking metrics and data analysis methods to infer the energy performance of a building based on its load shape. We first performed a data experiment with collected smart meter data using over 2,000 small- and medium-sized businesses in California. We then conducted a cluster analysis of the source data, and determined and interpreted the load shape features and parameters with peer group analysis. Finally, we implemented the load shape benchmarking feature in an open-access web-based toolkit (the Commercial Building Energy Saver) to provide straightforward and practical recommendations to users. The analysis techniques were generic and flexible for future datasets of other building types and in other utility territories.

10abenchmarking10aBuilding energy10acluster analysis10aload profile10aload shape10arepresentative load pattern1 aLuo, Xuan1 aHong, Tianzhen1 aChen, Yixing1 aPiette, Mary, Ann uhttps://simulationresearch.lbl.gov/publications/electric-load-shape-benchmarking02042nas a2200193 4500008004100000245014000041210006900181520132600250653002201576653003601598653001801634653001501652653002201667100001901689700001701708700001901725700001801744856008601762 2017 eng d00aOccupant behavior models: A critical review of implementation and representation approaches in building performance simulation programs0 aOccupant behavior models A critical review of implementation and3 a

Occupant behavior (OB) in buildings is a leading factor influencing energy use in buildings. Quantifying this influence requires the integration of OB models with building performance simulation (BPS). This study reviews approaches to representing and implementing OB models in today’s popular BPS programs, and discusses weaknesses and strengths of these approaches and key issues in integrating of OB models with BPS programs. Two key findings are: (1) a common data model is needed to standardize the representation of OB models, enabling their flexibility and exchange among BPS programs and user applications; the data model can be implemented using a standard syntax (e.g., in the form of XML schema), and (2) a modular software implementation of OB models, such as functional mock-up units for co-simulation, adopting the common data model, has advantages in providing a robust and interoperable integration with multiple BPS programs. Such common OB model representation and implementation approaches help standardize the input structures of OB models, enable collaborative development of a shared library of OB models, and allow for rapid and widespread integration of OB models with BPS programs to improve the simulation of occupant behavior and quantification of their impact on building performance.

10aBehavior Modeling10abuilding performance simulation10aco-simulation10adata model10aoccupant behavior1 aHong, Tianzhen1 aChen, Yixing1 aBelafi, Zsofia1 aD'Oca, Simona uhttps://simulationresearch.lbl.gov/publications/occupant-behavior-models-critical02401nas a2200229 4500008004100000245007200041210006900113260001200182490000800194520165900202653003301861653002201894653002501916653002201941653003501963653001701998100001402015700001902029700001702048700001902065856008702084 2017 eng d00aPerformance Evaluation of an Agent-based Occupancy Simulation Model0 aPerformance Evaluation of an Agentbased Occupancy Simulation Mod c04/20170 v1153 a

Occupancy is an important factor driving building performance. Static and homogeneous occupant schedules, commonly used in building performance simulation, contribute to issues such as performance gaps between simulated and measured energy use in buildings. Stochastic occupancy models have been recently developed and applied to better represent spatial and temporal diversity of occupants in buildings. However, there is very limited evaluation of the usability and accuracy of these models. This study used measured occupancy data from a real office building to evaluate the performance of an agent-based occupancy simulation model: the Occupancy Simulator. The occupancy patterns of various occupant types were first derived from the measured occupant schedule data using statistical analysis. Then the performance of the simulation model was evaluated and verified based on (1) whether the distribution of observed occupancy behavior patterns follows the theoretical ones included in the Occupancy Simulator, and (2) whether the simulator can reproduce a variety of occupancy patterns accurately. Results demonstrated the feasibility of applying the Occupancy Simulator to simulate a range of occupancy presence and movement behaviors for regular types of occupants in office buildings, and to generate stochastic occupant schedules at the room and individual occupant levels for building performance simulation. For future work, model validation is recommended, which includes collecting and using detailed interval occupancy data of all spaces in an office building to validate the simulated occupant schedules from the Occupancy Simulator.

10amodel performance evaluation10aoccupancy pattern10aOccupancy simulation10aoccupant behavior10aoccupant presence and movement10averification1 aLuo, Xuan1 aLam, Khee, Poh1 aChen, Yixing1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/performance-evaluation-agent-based02483nas a2200241 4500008004100000245008900041210006900130260001200199300001400211490000700225520173000232653002201962653002501984653002402009653001502033653002202048653001802070100001702088700001502105700001902120700001402139856008802153 2017 eng d00aSimulation and visualization of energy-related occupant behavior in office buildings0 aSimulation and visualization of energyrelated occupant behavior  c03/2017 a785–7980 v103 a

In current building performance simulation programs, occupant presence and interactions with building systems are over-simplified and less indicative of real world scenarios, contributing to the discrepancies between simulated and actual energy use in buildings. Simulation results are normally presented using various types of charts. However, using those charts, it is difficult to visualize and communicate the importance of occupants’ behavior to building energy performance. This study introduced a new approach to simulating and visualizing energy-related occupant behavior in office buildings. First, the Occupancy Simulator was used to simulate the occupant presence and movement and generate occupant schedules for each space as well as for each occupant. Then an occupant behavior functional mockup unit (obFMU) was used to model occupant behavior and analyze their impact on building energy use through co-simulation with EnergyPlus. Finally, an agent-based model built upon AnyLogic was applied to visualize the simulation results of the occupant movement and interactions with building systems, as well as the related energy performance. A case study using a small office building in Miami, FL was presented to demonstrate the process and application of the Occupancy Simulator, the obFMU and EnergyPlus, and the AnyLogic module in simulation and visualization of energy-related occupant behaviors in office buildings. The presented approach provides a new detailed and visual way for policy makers, architects, engineers and building operators to better understand occupant energy behavior and their impact on energy use in buildings, which can improve the design and operation of low energy buildings.

10aBehavior Modeling10abuilding performance10abuilding simulation10aenergyplus10aoccupant behavior10avisualization1 aChen, Yixing1 aLiang, Xin1 aHong, Tianzhen1 aLuo, Xuan uhttps://simulationresearch.lbl.gov/publications/simulation-and-visualization-energy03410nas a2200409 4500008004100000245008500041210006900126260001200195520213600207653000902343653002502352653002202377653002002399653001902419653002302438653003302461653003902494653003202533653001302565653002102578100002202599700001902621700002202640700002102662700002002683700001702703700002302720700002202743700002102765700002002786700002302806700001902829700001502848700002802863700002002891856008902911 2017 eng d00aSmall and Medium Building Efficiency Toolkit and Community Demonstration Program0 aSmall and Medium Building Efficiency Toolkit and Community Demon c03/20173 a

Small commercial buildings in the United States consume 47 percent of all primary energy consumed in the building sector. Retrofitting small and medium commercial buildings may pose a steep challenge for owners, as many lack the expertise and resources to identify and evaluate cost-effective energy retrofit strategies. To address this problem, this project developed the Commercial Building Energy Saver (CBES), an energy retrofit analysis toolkit that calculates the energy use of a building, identifies and evaluates retrofit measures based on energy savings, energy cost savings, and payback. The CBES Toolkit includes a web app for end users and the CBES Application Programming Interface for integrating CBES with other energy software tools. The toolkit provides a rich feature set, including the following:

  1. Energy Benchmarking providing an Energy Star score
  2. Load Shape Analysis to identify potential building operation improvements
  3. Preliminary Retrofit Analysis which uses a custom developed pre-simulated database
  4. Detailed Retrofit Analysis which utilizes real time EnergyPlus simulations

In a parallel effort the project team developed technologies to measure outdoor airflow rate; commercialization and use would avoid both excess energy use from over ventilation and poor indoor air quality resulting from under ventilation.

If CBES is adopted by California’s statewide small office and retail buildings, by 2030 the state can anticipate 1,587 gigawatt hours of electricity savings, 356 megawatts of non-coincident peak demand savings, 30.2 megatherms of natural gas savings, $227 million of energy-related cost savings, and reduction of emissions by 757,866 metric tons of carbon dioxide equivalent. In addition, consultant costs will be reduced in the retrofit analysis process.

CBES contributes to the energy savings retrofit field by enabling a straightforward and uncomplicated decision-making process for small and medium business owners and leveraging different levels of assessment to match user background, preference, and data availability.

10aCBES10acommercial buildings10aenergy efficiency10aenergy modeling10aenergy savings10aindoor air quality10aindoor environmental quality10aoutdoor air measurement technology10aoutdoor airflow intake rate10aretrofit10aventilation rate1 aPiette, Mary, Ann1 aHong, Tianzhen1 aFisk, William, J.1 aBourassa, Norman1 aChan, Wanyu, R.1 aChen, Yixing1 aCheung, H.Y., Iris1 aHotchi, Toshifumi1 aKloss, Margarita1 aLee, Sang, Hoon1 aPrice, Phillip, N.1 aSchetrit, Oren1 aSun, Kaiyu1 aTaylor-Lange, Sarah, C.1 aZhang, Rongpeng uhttps://simulationresearch.lbl.gov/publications/small-and-medium-building-efficiency02072nas a2200133 4500008004100000245014300041210006900184520152000253100001801773700002001791700001901811700001901830856008901849 2017 eng d00aSynthesizing building physics with social psychology: An interdisciplinary framework for context and occupant behavior in office buildings0 aSynthesizing building physics with social psychology An interdis3 a

This study introduces an interdisciplinary framework for investigating building-user interaction in office spaces. The framework is a synthesis of theories from building physics and social psychology including social cognitive theory, the theory of planned behavior, and the drivers-needs-actions-systems ontology for energy-related behaviors. The goal of the research framework is to investigate the effects of various behavioral adaptations and building controls (i.e., adjusting thermostats, operating windows, blinds and shades, and switching on/off artificial lights) to determine impacts on occupant comfort and energy-related operational costs in the office environment. This study attempts to expand state-of-the-art understanding of: (1) the environmental, personal, and behavioral drivers motivating occupants to interact with building control systems across four seasons, (2) how occupants’ intention to share controls is influenced by social-psychological variables such as attitudes, subjective norms, and perceived behavioral control in group negotiation dynamic, (3) the perceived ease of usage and knowledge of building technologies, and (4) perceived satisfaction and productivity. To ground the validation of the theoretical framework in diverse office settings and contexts at the international scale, an online survey was designed to collect cross-country responses from office occupants among 14 universities and research centers within the United States, Europe, China, and Australia.

1 aD'Oca, Simona1 aChen, Chien-Fen1 aHong, Tianzhen1 aBelafi, Zsofia uhttps://simulationresearch.lbl.gov/publications/synthesizing-building-physics-social01971nas 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-occupant01628nas 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-applications01736nas a2200121 4500008003900000245007500039210006900114520129700183100001701480700001401497700001901511856008401530 2016 d00aAn Agent-Based Occupancy Simulator for Building Performance Simulation0 aAgentBased Occupancy Simulator for Building Performance Simulati3 a

Traditionally, in building energy modeling (BEM) programs, occupancy inputs are deterministic and less indicative of real world scenarios, contributing to discrepancies between simulated and actual energy use in buildings. This paper presents an agent-based occupancy simulator, which models each occupant as an agent with specified movement events and statistics of space uses. To reduce the amount of data inputs, the simulator allows users to group occupants with similar behaviors as an occupant type, and spaces with similar function as a space type. It is a web-based application with friendly graphical user interface, cloud computing, and data storage. A case study is presented to demonstrate the usage of the occupancy simulator and its integration with EnergyPlus and obFMU. It first shows the required data inputs and the results from the occupancy simulator. Then, the generated occupant schedules are used in the EnergyPlus and obFMU simulation to evaluate the impacts of occupant behavior on building energy performance. The simulation results indicate that the occupancy simulator can capture the diversity of space’s occupancy behavior rather than the static weekly profiles, and can generate realistic occupancy schedules to support building performance simulation.

1 aChen, Yixing1 aLuo, Xuan1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/agent-based-occupancy-simulator02318nas a2200193 4500008003900000245006900039210006900108520165900177653001901836653003701855653001501892653002101907653002501928100001801953700002601971700002501997700001902022856008302041 2016 d00aIntroduction to an occupant behavior motivation survey framework0 aIntroduction to an occupant behavior motivation survey framework3 a

An increasing body of research is underlying the need to foster energy behaviors and interaction with technology as a way to achieve energy savings in office buildings. However, engaging office users into more “forgiving” comfort-adaptive behavior is not a trivial task, since neither consequences nor benefits for changing behavior have visible or tangible effects on them personally. Since the 70’s, survey studies in the field of building science have been used to gain better understanding of multidisciplinary drivers of occupant behavior with respect to comfort and energy requirements in buildings. Rather than focusing on individual behaviors – and influencing factors – purpose of this survey research is to provide quantitative descriptions on the collective and social motivations within the complexity of different social groups in working environment, under different geographical context, culture and norms. The resultant questionnaire survey emerges as a combination of traditional and adaptive comfort theories, merged with social science theory. The questionnaire explores to what extent the occupant energy-related behavior in working spaces is driven by a motivational sphere influenced by i) comfort requirements, ii) habits, iii) intentions and iv) actual control of building systems. The key elements of the proposed occupant behavior motivational framework are grounded on the Driver Need Action System framework for energy-related behaviors in buildings. Goal of the study is to construct an additional layer of standardized knowledge to enrich the state-of-the-art on energy-related behavior in office buildings.

10aDNAs framework10aenergy-related occupant behavior10amotivation10aoffice buildings10aquestionnaire survey1 aD'Oca, Simona1 aCorgnati, Stefano, P.1 aPisello, Anna, Laura1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/introduction-occupant-behavior02261nas a2200241 4500008003900000245011100039210006900150260001200219520144900231653002401680653003201704653002001736653001501756653003101771653001301802100002001815700001901835700002201854700001701876700001701893700002801910856008101938 2015 d00aAccelerating the energy retrofit of commercial buildings using a database of energy efficiency performance0 aAccelerating the energy retrofit of commercial buildings using a c07/20153 a

Small and medium-sized commercial buildings can be retrofitted to significantly reduce their energy use, however it is a huge challenge as owners usually lack of the expertise and resources to conduct detailed on-site energy audit to identify and evaluate cost-effective energy technologies. This study presents a DEEP (database of energy efficiency performance) that provides a direct resource for quick retrofit analysis of commercial buildings. DEEP, compiled from the results of about ten million EnergyPlus simulations, enables an easy screening of ECMs (energy conservation measures) and retrofit analysis. The simulations utilize prototype models representative of small and mid-size offices and retails in California climates. In the formulation of DEEP, large scale EnergyPlus simulations were conducted on high performance computing clusters to evaluate hundreds of individual and packaged ECMs covering envelope, lighting, heating, ventilation, air-conditioning, plug-loads, and service hot water. The architecture and simulation environment to create DEEP is flexible and can expand to cover additional building types, additional climates, and new ECMs. In this study DEEP is integrated into a web-based retrofit toolkit, the Commercial Building Energy Saver, which provides a platform for energy retrofit decision making by querying DEEP and unearthing recommended ECMs, their estimated energy savings and financial payback.

10abuilding simulation10aEnergy conservation measure10aenergy modeling10aenergyplus10aHigh Performance computing10aretrofit1 aLee, Sang, Hoon1 aHong, Tianzhen1 aPiette, Mary, Ann1 aSawaya, Geof1 aChen, Yixing1 aTaylor-Lange, Sarah, C. uhttps://simulationresearch.lbl.gov/publications/accelerating-energy-retrofit02814nas a2200349 4500008003900000245007400039210006900113260001100182490000800193520171300201653003701914653005101951653001402002653003202016653003202048653002602080653002202106653001502128653001502143653001302158653002002171653002402191100001902215700002202234700001702256700002002273700002802293700002002321700001502341700002302356856008502379 2015 d00aCommercial Building Energy Saver: An energy retrofit analysis toolkit0 aCommercial Building Energy Saver An energy retrofit analysis too c9/20150 v1593 a

Small commercial buildings in the United States consume 47% of the total primary energy of the buildings sector. Retrofitting small and medium commercial buildings poses a huge challenge for owners because they usually lack the expertise and resources to identify and evaluate cost-effective energy retrofit strategies. This paper presents the Commercial Building Energy Saver (CBES), an energy retrofit analysis toolkit, which calculates the energy use of a building, identifies and evaluates retrofit measures in terms of energy savings, energy cost savings and payback. The CBES Toolkit includes a web app (APP) for end users and the CBES Application Programming Interface (API) for integrating CBES with other energy software tools. The toolkit provides a rich set of features including: (1) Energy Benchmarking providing an Energy Star score, (2) Load Shape Analysis to identify potential building operation improvements, (3) Preliminary Retrofit Analysis which uses a custom developed pre-simulated database and, (4) Detailed Retrofit Analysis which utilizes real-time EnergyPlus simulations. CBES includes 100 configurable energy conservation measures (ECMs) that encompass IAQ, technical performance and cost data, for assessing 7 different prototype buildings in 16 climate zones in California and 6 vintages. A case study of a small office building demonstrates the use of the toolkit for retrofit analysis. The development of CBES provides a new contribution to the field by providing a straightforward and uncomplicated decision making process for small and medium business owners, leveraging different levels of assessment dependent upon user background, preference and data availability.

10aBuilding Technologies Department10aBuilding Technology and Urban Systems Division10abuildings10abuildings energy efficiency10aCommercial Building Systems10aconservation measures10aenergy efficiency10aenergy use10aenergyplus10aExternal10aRetrofit Energy10asimulation research1 aHong, Tianzhen1 aPiette, Mary, Ann1 aChen, Yixing1 aLee, Sang, Hoon1 aTaylor-Lange, Sarah, C.1 aZhang, Rongpeng1 aSun, Kaiyu1 aPrice, Phillip, N. uhttps://simulationresearch.lbl.gov/publications/commercial-building-energy-saver02785nas a2200145 4500008003900000245011200039210006900151520224000220100002002460700001902480700001702499700001702516700002202533856008402555 2015 d00aDEEP: A Database of Energy Efficiency Performance to Accelerate Energy Retrofitting of Commercial Buildings0 aDEEP A Database of Energy Efficiency Performance to Accelerate E3 a

The paper presents a method and process to establish a database of energy efficiency performance (DEEP) to enable quick and accurate assessment of energy retrofit of commercial buildings. DEEP was compiled from results of about 35 million EnergyPlus simulations. DEEP provides energy savings for screening and evaluation of retrofit measures targeting the small and medium-sized office and retail buildings in California. The prototype building models are developed for a comprehensive assessment of building energy performance based on DOE commercial reference buildings and the California DEER prototype buildings. The prototype buildings represent seven building types across six vintages of constructions and 16 California climate zones. DEEP uses these prototypes to evaluate energy performance of about 100 energy conservation measures covering envelope, lighting, heating, ventilation, air-conditioning, plug-loads, and domestic hot water. DEEP consists the energy simulation results for individual retrofit measures as well as packages of measures to consider interactive effects between multiple measures. The large scale EnergyPlus simulations are being conducted on the super computers at the National Energy Research Scientific Computing Center of Lawrence Berkeley National Laboratory. The pre-simulation database is a part of an on-going project to develop a web-based retrofit toolkit for small and medium-sized commercial buildings in California, which provides real-time energy retrofit feedback by querying DEEP with recommended measures, estimated energy savings and financial payback period based on users’ decision criteria of maximizing energy savings, energy cost savings, carbon reduction, or payback of investment. The pre-simulated database and associated comprehensive measure analysis enhances the ability to performance assessments of retrofits to reduce energy use for small and medium buildings and business owners who typically do not have resources to conduct costly building energy audit. DEEP will be migrated into the DEnCity - DOE’s Energy City, which integrates large-scale energy data for multi-purpose, open, and dynamic database leveraging diverse source of existing simulation data.

1 aLee, Sang, Hoon1 aHong, Tianzhen1 aSawaya, Geof1 aChen, Yixing1 aPiette, Mary, Ann uhttps://simulationresearch.lbl.gov/publications/deep-database-energy-efficiency02052nas a2200217 4500008003900000245008100039210006900120260005100189520129700240100001601537700002401553700002001577700002501597700001801622700001901640700002101659700002201680700002101702700002701723856008401750 2015 d00aGreen, Clean, & Mean: Pushing the Energy Envelope in Tech Industry Buildings0 aGreen Clean Mean Pushing the Energy Envelope in Tech Industry Bu bLawrence Berkeley National Laboratoryc05/20153 a

When it comes to innovation in energy and building performance, one can expect leading-edge activity from the technology sector. As front-line innovators in design, materials science, and information management, developing and operating high-performance buildings is a natural extension of their core business.

The energy choices made by technology companies have broad importance given their influence on society at large as well as the extent of their own energy footprint. Microsoft, for example, has approximately 250 facilities around the world (30 million square feet of floor area), with significant aggregate energy use of approximately 4 million kilowatt-hours per day.

There is a degree of existing documentation of efforts to design, build, and operate facilities in the technology sector. However, the material is fragmented and typically looks only at a single company, or discrete projects within a company.Yet, there is no single resource for corporate planners and decision makers that takes stock of the opportunities and documents sector-specific case studies in a structured manner. This report seeks to fill that gap, doing so through a combination of generalized technology assessments (“Key Strategies”) and case studies (“Flagship Projects”).

1 aMills, Evan1 aGranderson, Jessica1 aChan, Wanyu, R.1 aDiamond, Richard, C.1 aHaves, Philip1 aNordman, Bruce1 aMathew, Paul, A.1 aPiette, Mary, Ann1 aRobinson, Gerald1 aSelkowitz, Stephen, E. uhttps://simulationresearch.lbl.gov/publications/green-clean-mean-pushing-energy02313nas a2200265 4500008003900000245014200039210006900181260001200250300001200262490000700274520142200281653003201703653002401735653002001759653001001779653002201789653001501811100001901826700001801845700002801863700002701891700001701918700002601935856008601961 2015 d00aAn Ontology to Represent Energy-Related Occupant Behavior in Buildings. Part II: Implementation of the DNAS framework using an XML schema0 aOntology to Represent EnergyRelated Occupant Behavior in Buildin c08/2015 a196-2050 v943 a

Energy-related occupant behavior in buildings is difficult to define and quantify, yet critical to our understanding of total building energy consumption. Part I of this two-part paper introduced the DNAS (Drivers, Needs, Actions and Systems) framework, to standardize the description of energy-related occupant behavior in buildings. Part II of this paper implements the DNAS framework into an XML (eXtensible Markup Language) schema, titled ‘occupant behavior XML’ (obXML). The obXML schema is used for the practical implementation of the DNAS framework into building simulation tools. The topology of the DNAS framework implemented in the obXML schema has a main root element OccupantBehavior, linking three main elements representing Buildings, Occupants and Behaviors. Using the schema structure, the actions of turning on an air conditioner and closing blinds provide two examples of how the schema standardizes these actions using XML. The obXML schema has inherent flexibility to represent numerous, diverse and complex types of occupant behaviors in buildings, and it can also be expanded to encompass new types of behaviors. The implementation of the DNAS framework into the obXML schema will facilitate the development of occupant information modeling (OIM) by providing interoperability between occupant behavior models and building energy modeling programs.

10abuilding energy consumption10abuilding simulation10aenergy modeling10aobXML10aoccupant behavior10aXML schema1 aHong, Tianzhen1 aD'Oca, Simona1 aTaylor-Lange, Sarah, C.1 aTurner, William, J. N.1 aChen, Yixing1 aCorgnati, Stefano, P. uhttps://simulationresearch.lbl.gov/publications/ontology-represent-energy-related01865nas a2200145 4500008004100000245014400041210006900185260001200254490000700266520130200273100002101575700001801596700002101614856008401635 2014 eng d00aThe Role of International Partnerships in Delivering Low- Energy Building Design: A Case Study of the Singapore Scientific Planning Process0 aRole of International Partnerships in Delivering Low Energy Buil c05/20140 v143 a

This paper explores the role of international partnerships to facilitate low-energy building
design, construction, and operations. We briefly discuss multiple collaboration models
and the levels of impact they support. We present a case study of one collaborative
partnership model, the Scientific Planning Support (SPS) team. Staff from the Lawrence
Berkeley National Laboratory, the Austrian Institute of Technology, and Nanyang
Technological University formed the SPS team to provide design assistance and process
support during the design phase of a low-energy building project. Specifically, the SPS
team worked on the Clean Tech Two project, a tenanted laboratory and office building
that seeks Green Mark Platinum, the highest green building certification in Singapore.
The SPS team hosted design charrettes, helped to develop design alternatives, and
provided suggestions on the design process in support of this aggressive energy target.
This paper describes these efforts and discusses how teams like the SPS team and other partnership schemes can be leveraged to achieve high performance, low-energy buildingsat an international scale.

1 aParrish, Kristen1 aSingh, Reshma1 aChien, Szu-Cheng uhttps://simulationresearch.lbl.gov/publications/role-international-partnerships03302nas a2200229 4500008003900000245012400039210006900163260005100232300001200283490000800295520248200303653003102785653002402816653001502840653002802855653003202883653001702915100001902932700002002951700001802971856008302989 2013 d00aA Fresh Look at Weather Impact on Peak Electricity Demand and Energy Use of Buildings Using 30-Year Actual Weather Data0 aFresh Look at Weather Impact on Peak Electricity Demand and Ener bLawrence Berkeley National Laboratoryc11/2013 a333-3500 v1113 a

Buildings consume more than one third of the world’s total primary energy. Weather plays a unique and significant role as it directly affects the thermal loads and thus energy performance of buildings. The traditional simulated energy performance using Typical Meteorological Year (TMY) weather data represents the building performance for a typical year, but not necessarily the average or typical long-term performance as buildings with different energy systems and designs respond differently to weather changes. Furthermore, the single-year TMY simulations do not provide a range of results that capture yearly variations due to changing weather, which is important for building energy management, and for performing risk assessments of energy efficiency investments. This paper employs large-scale building simulation (a total of 3162 runs) to study the weather impact on peak electricity demand and energy use with the 30-year (1980–2009) Actual Meteorological Year (AMY) weather data for three types of office buildings at two design efficiency levels, across all 17 ASHRAE climate zones. The simulated results using the AMY data are compared to those from the TMY3 data to determine and analyze the differences. Besides further demonstration, as done by other studies, that actual weather has a significant impact on both the peak electricity demand and energy use of buildings, the main findings from the current study include: (1) annual weather variation has a greater impact on the peak electricity demand than it does on energy use in buildings; (2) the simulated energy use using the TMY3 weather data is not necessarily representative of the average energy use over a long period, and the TMY3 results can be significantly higher or lower than those from the AMY data; (3) the weather impact is greater for buildings in colder climates than warmer climates; (4) the weather impact on the medium-sized office building was the greatest, followed by the large office and then the small office; and (5) simulated energy savings and peak demand reduction by energy conservation measures using the TMY3 weather data can be significantly underestimated or overestimated. It is crucial to run multi-decade simulations with AMY weather data to fully assess the impact of weather on the long-term performance of buildings, and to evaluate the energy savings potential of energy conservation measures for new and existing buildings from a life cycle perspective.

10aActual meteorological year10abuilding simulation10aenergy use10aPeak electricity demand10aTypical meteorological year10aweather data1 aHong, Tianzhen1 aChang, Wen-Kuei1 aLin, Hung-Wen uhttps://simulationresearch.lbl.gov/publications/fresh-look-weather-impact-peak02408nas a2200229 4500008003900000022001400039245011700053210006900170260001200239300001200251490000600263520165000269653002401919653002001943653002201963653002201985653002102007653002502028100002002053700001902073856008602092 2013 d a1996-874400aStatistical Analysis and Modeling of Occupancy Patterns in Open-Plan Offices using Measured Lighting-Switch Data0 aStatistical Analysis and Modeling of Occupancy Patterns in OpenP c03/2013 a23–320 v63 a

Occupancy profile is one of the driving factors behind discrepancies between the measured and simulated energy consumption of buildings. The frequencies of occupants leaving their offices and the corresponding durations of absences have significant impact on energy use and the operational controls of buildings. This study used statistical methods to analyze the occupancy status, based on measured lighting-switch data in five-minute intervals, for a total of 200 open-plan (cubicle) offices. Five typical occupancy patterns were identified based on the average daily 24-hour profiles of the presence of occupants in their cubicles. These statistical patterns were represented by a one-square curve, a one-valley curve, a two-valley curve, a variable curve, and a flat curve. The key parameters that define the occupancy model are the average occupancy profile together with probability distributions of absence duration, and the number of times an occupant is absent from the cubicle. The statistical results also reveal that the number of absence occurrences decreases as total daily presence hours decrease, and the duration of absence from the cubicle decreases as the frequency of absence increases. The developed occupancy model captures the stochastic nature of occupants moving in and out of cubicles, and can be used to generate a more realistic occupancy schedule. This is crucial for improving the evaluation of the energy saving potential of occupancy based technologies and controls using building simulations. Finally, to demonstrate the use of the occupancy model, weekday occupant schedules were generated and discussed.

10abuilding simulation10aoccupancy model10aoccupancy pattern10aoccupant schedule10aoffice buildings10astatistical analysis1 aChang, Wen-Kuei1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/statistical-analysis-and-modeling00430nas a2200133 4500008004100000245005000041210005000091300001200141490000600153100001600159700001700175700001800192856008600210 2012 eng d00aReduction of numerical viscosity in FFD model0 aReduction of numerical viscosity in FFD model a234-2470 v61 aZuo, Wangda1 aJin, Mingang1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/reduction-numerical-viscosity-ffd00498nas a2200133 4500008004100000245008700041210006900128260001600197300001400213100001700227700001600244700001800260856008600278 2012 eng d00aValidation of three dimensional fast fluid dynamics for indoor airflow simulations0 aValidation of three dimensional fast fluid dynamics for indoor a aBoulder, CO a1055-10621 aJin, Mingang1 aZuo, Wangda1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/validation-three-dimensional-fast00559nas a2200157 4500008004100000245009200041210006900133260001200202300001400214490000700228100002100235700001800256700001800274700002100292856008800313 2011 eng d00aMulti-Criteria Optimisation using Past, Real Time and Predictive Performance Benchmarks0 aMultiCriteria Optimisation using Past Real Time and Predictive P c04/2011 a1258-12650 v191 aTorrens, Ignacio1 aKeane, Marcus1 aCosta, Andrea1 aO'Donnell, James uhttps://simulationresearch.lbl.gov/publications/multi-criteria-optimisation-using-000550nas a2200133 4500008004100000245011300041210006900154260003100223100001800254700001800272700002100290700001800311856008700329 2011 eng d00aSystematic Development of an Operational BIM Utilising Simulation and Performance Data in Building Operation0 aSystematic Development of an Operational BIM Utilising Simulatio aSydney, Australiac11/20111 aCorry, Edward1 aKeane, Marcus1 aO'Donnell, James1 aCosta, Andrea uhttps://simulationresearch.lbl.gov/publications/systematic-development-operational01525nas a2200169 4500008004100000245011000041210006900151260002700220520091900247653000801166653000801174653002201182653002801204100001601232700001801248856008901266 2011 eng d00aValidation of a Fast-Fluid-Dynamics Model for Predicting Distribution of Particles with Low Stokes Number0 aValidation of a FastFluidDynamics Model for Predicting Distribut aAustin, Texasc06/20113 aTo design a healthy indoor environment, it is important to study airborne particle distribution indoors. As an intermediate model between multizone models and computational fluid dynamics (CFD), a fast fluid dynamics (FFD) model can be used to provide temporal and spatial information of particle dispersion in real time. This study evaluated the accuracy of the FFD for predicting transportation of particles with low Stokes number in a duct and in a room with mixed convection. The evaluation was to compare the numerical results calculated by the FFD with the corresponding experimental data and the results obtained by the CFD. The comparison showed that the FFD could capture major pattern of particle dispersion, which is missed in models with well-mixed assumptions. Although the FFD was less accurate than the CFD partially due to its simplification in numeric schemes, it was 53 times faster than the CFD.10acfd10affd10alow stokes number10aparticle transportation1 aZuo, Wangda1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/validation-fast-fluid-dynamics-model00396nas a2200133 4500008004100000245002700041210002700068100001800095700001800113700002100131700002000152700001500172856007500187 2010 eng d00aBuildWise Final Report0 aBuildWise Final Report1 aKeane, Marcus1 aCosta, Andrea1 aO'Donnell, James1 aMenzel, Karsten1 aDirk, Alan uhttps://simulationresearch.lbl.gov/publications/buildwise-final-report02003nas a2200193 4500008004100000022001800041245011000059210006900169260004100238520131100279100002201590700001801612700002001630700002001650700002401670700001501694700001801709856008201727 2010 eng d a0-918249-60-000aDevelopment and Testing of Model Predictive Control for a Campus Chilled Water Plant with Thermal Storage0 aDevelopment and Testing of Model Predictive Control for a Campus aAsilomar, California, USAbOmnipress3 a

A Model Predictive Control (MPC) implementation was developed for a university campus chilled water plant. The plant includes three water-cooled chillers and a two million gallon chilled water storage tank. The tank is charged during the night to minimize on-peak electricity consumption and take advantage of the lower ambient wet bulb temperature. A detailed model of the chilled water plant and simplified models of the campus buildings were developed using the equation-based modeling language Modelica. Steady state models of the chillers, cooling towers and pumps were developed, based on manufacturers' performance data, and calibrated using measured data collected and archived by the control system. A dynamic model of the chilled water storage tank was also developed and calibrated. A semi-empirical model was developed to predict the temperature and flow rate of the chilled water returning to the plant from the buildings. These models were then combined and simplified for use in a MPC algorithm that determines the optimal chiller start and stop times and set-points for the condenser water temperature and the chilled water supply temperature. The paper describes the development and testing of the MPC implementation and discusses lessons learned and next steps in further research.

1 aCoffey, Brian, E.1 aHaves, Philip1 aWetter, Michael1 aHencey, Brandon1 aBorrelli, Francesco1 aMa, Yudong1 aBengea, Sorin uhttps://simulationresearch.lbl.gov/publications/development-and-testing-model00514nas a2200133 4500008004100000245009100041210006900132260002000201100001800221700001800239700002100257700001800278856008400296 2010 eng d00aEnergy Monitoring Systems value, issues and recommendations based on five case studies0 aEnergy Monitoring Systems value issues and recommendations based aAntalya, Turkey1 aRaferty, Paul1 aKeane, Marcus1 aO'Donnell, James1 aCosta, Andrea uhttps://simulationresearch.lbl.gov/publications/energy-monitoring-systems-value00492nas a2200121 4500008004100000245011800041210006900159300001200228490000700240100001600247700001800263856008900281 2010 eng d00aFast and informative flow simulation in a building by using fast fluid dynamics model on graphics processing unit0 aFast and informative flow simulation in a building by using fast a747-7570 v451 aZuo, Wangda1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/fast-and-informative-flow-simulation00394nas a2200109 4500008004100000245005200041210005200093260002100145100001600166700001800182856008400200 2010 eng d00aFast simulation of smoke transport in buildings0 aFast simulation of smoke transport in buildings aBeograd, Serbian1 aZuo, Wangda1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/fast-simulation-smoke-transport00475nas a2200133 4500008004100000245007200041210006900113260001700182300001000199100001600209700001600225700001800241856008200259 2010 eng d00aImpact of time-splitting schemes on the accuracy of FFD simulations0 aImpact of timesplitting schemes on the accuracy of FFD simulatio aSyracuse, NY a55-601 aHu, Jianjun1 aZuo, Wangda1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/impact-time-splitting-schemes00464nas a2200133 4500008004100000245007000041210006900111300000900180490000700189100001600196700001600212700001800228856008400246 2010 eng d00aImprovements on FFD modeling by using different numerical schemes0 aImprovements on FFD modeling by using different numerical scheme a1-160 v581 aZuo, Wangda1 aHu, Jianjun1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/improvements-ffd-modeling-using00460nas a2200121 4500008004100000245008000041210006900121260001700190300001200207100001600219700001800235856008500253 2010 eng d00aImprovements on the fast fluid dynamics model for indoor airflow simulation0 aImprovements on the fast fluid dynamics model for indoor airflow aNew York, NY a539-5461 aZuo, Wangda1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/improvements-fast-fluid-dynamics01213nas a2200169 4500008004100000245008300041210006900124260003800193520061000231100001500841700002400856700002000880700002200900700001800922700001800940856008500958 2010 eng d00aModel Predictive Control of Thermal Energy Storage in Building Cooling Systems0 aModel Predictive Control of Thermal Energy Storage in Building C aBaltimore, Maryland, USAc06/20103 aA model-based predictive control (MPC) is designed for optimal thermal energy storage in building cooling systems. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. Typically the chillers are operated at night to recharge the storage tank in order to meet the building demands on the following day. In this paper, we build on our previous work, improve the building load model, and present experimental results. The experiments show that MPC can achieve reductionin the central plant electricity cost and improvement of its efficiency.1 aMa, Yudong1 aBorrelli, Francesco1 aHencey, Brandon1 aCoffey, Brian, E.1 aBengea, Sorin1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/model-predictive-control-thermal00423nas a2200121 4500008004100000245006300041210006300104300001200167490000700179100001600186700001800202856008100220 2010 eng d00aSimulations of air distribution in buildings by FFD on GPU0 aSimulations of air distribution in buildings by FFD on GPU a783-7960 v161 aZuo, Wangda1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/simulations-air-distribution00423nas a2200109 4500008004100000245006700041210006700108260001700175100001600192700001800208856008700226 2009 eng d00aFast parallelized flow simulations on graphic processing units0 aFast parallelized flow simulations on graphic processing units aBusan, Korea1 aZuo, Wangda1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/fast-parallelized-flow-simulations00409nas a2200121 4500008004100000245004600041210004600087260001800133300001200151100001600163700001800179856009000197 2009 eng d00aHigh performance computing for indoor air0 aHigh performance computing for indoor air aGlasgow, U.K. a244-2491 aZuo, Wangda1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/high-performance-computing-indoor-air02419nas a2200157 4500008004100000245007800041210006900119260001200188300001200200490000800212520190100220100001902121700001902140700001802159856008402177 2009 eng d00aImproving Control and Operation of a Single Duct VAV System through CCLEP0 aImproving Control and Operation of a Single Duct VAV System thro c07/2009 a760-7680 v1153 a

With the energy crisis of the early 1970s came the realization that buildings could be made much more efficient without sacrificing comfort. Over the last 30 years, use of variable air volume systems has become common practice. Many variable air volume (VAV) systems with pneumatic controls were installed in the 1980s and are still in use. However, these systems often have outdated control strategies and deficient mechanical systems are deficient, which may cause occupant discomfort and excess energy consumption.

An ASHRAE committee proposed building commissioning in 1988 to ensure that system performance met design specifications. Continuous Commissioning (CC[R]) technology was developed and implemented in 1992. CC is an ongoing process to resolve operating problems, improve comfort, optimize energy use and identify retrofits for existing commercial and institutional buildings and central plant facilities [1-5]. Since 1999, the Energy Systems Laboratory (ESL) at the University of Nebraska has conducted extensive research to implement optimal system control during the design phase and finalize the optimal setpoints after system installation. ESL researchers have developed and implemented the Continuous Commissioning Leading Energy Project (CCLEP) process with federal and industry support. The CCLEP process has two stages: the contracting stage and the implementation stage. During the contracting stage, a comprehensive technical evaluation is performed. The CCLEP implementation stage involves planning, retrofit and trouble shooting, and optimization and follow-up. The CCLEP process, procedures and seven case study results are presented in [6].

This paper presents information on the case study facility, existing and improved control sequences, and building performance improvement and energy consumption measures before and after CCLEP implementation

1 aCho, Young-Hum1 aLiu, Mingsheng1 aPang, Xiufeng uhttps://simulationresearch.lbl.gov/publications/improving-control-and-operation01151nas a2200145 4500008004100000245008900041210006900130260002200199520065200221100001800873700001800891700001800909700002100927856005700948 2009 eng d00aKey Factors - Methodology for Enhancement and Support of Building Energy Performance0 aKey Factors Methodology for Enhancement and Support of Building  aGlasgow, Scotland3 a

This paper presents the Key Factors methodology that supports energy managers in determining the optimal building operation strategy in relation to both energy consumption and thermal comfort. The methodology is supported by the utilisation of calibrated building energy simulation models that match measured data gathered by an extensive measurement framework. The paper outlines the proposed methodology defining the underpinning concepts and illustrating the performance metrics required to capture the effect of different building operation strategies. A brief case study is discussed to demonstrate the application of the methodology.

1 aCosta, Andrea1 aKeane, Marcus1 aRaferty, Paul1 aO'Donnell, James uhttp://zuse.ucc.ie/iruse/papersNew/AndreaGlasgow.pdf00500nas a2200121 4500008004100000245010400041210006900145100002100214700001800235700002100253700001800274856008600292 2009 eng d00aMulti-criteria optimisation using past, historical, real time and predictive performance benchmarks0 aMulticriteria optimisation using past historical real time and p1 aTorrens, Ignacio1 aKeane, Marcus1 aO'Donnell, James1 aCosta, Andrea uhttps://simulationresearch.lbl.gov/publications/multi-criteria-optimisation-using00439nas a2200121 4500008004100000245007400041210006900115300001000184490000700194100001600201700001800217856008200235 2009 eng d00aReal time or faster-than-real-time simulation of airflow in buildings0 aReal time or fasterthanrealtime simulation of airflow in buildin a33-440 v191 aZuo, Wangda1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/real-time-or-faster-real-time02217nas a2200217 4500008004100000020002200041245006700063210006400130250000700194260006500201490000700266520151600273653001301789100002101802700002301823700001801846700002001864700002301884700002001907856007201927 2009 eng d a978-91-7393-513-500aStandardization of thermo-fluid modeling in Modelica.Fluid 1.00 aStandardization of thermofluid modeling in ModelicaFluid 10 a13 aComo, ItalybLinköping University Electronic Pressc09/20090 v433 a

This article discusses the Modelica.Fluid library that has been included in the Modelica Standard Library 3.1. Modelica.Fluid provides interfaces and basic components for the device-oriented modeling of one dimensional thermo-fluid flow in networks containing vessels; pipes; fluid machines; valves and fittings.

A unique feature of Modelica.Fluid is that the component equations and the media models as well as pressure loss and heat transfer correlations are decoupled from each other. All components are implemented such that they can be used for media from the Modelica.Media library. This means that an incompressible or compressible medium; a single or a multiple substance medium with one or more phases might be used with one and the same model as long as the modeling assumptions made hold. Furthermore;

trace substances are supported. Modeling assumptions can be configured globally in an outer System object. This covers in particular the initialization; uni- or bi-directional flow; and dynamic or steady-state formulation of mass; energy; and momentum balance. All assumptions can be locally refined for every component.

While Modelica.Fluid contains a reasonable set of component models; the goal of the library is not to provide a comprehensive set of models; but rather to provide interfaces and best practices for the treatment of issues such as connector design and implementation of energy; mass and momentum balances. Applications from various domains are presented.

10amodelica1 aFranke, Rüdiger1 aCasella, Francesco1 aOtter, Martin1 aProelss, Katrin1 aSielemann, Michael1 aWetter, Michael uhttp://www.ep.liu.se/ecp_article/index.en.aspx?issue=043;article=1301592nas a2200169 4500008004100000245013000041210006900171490000900240520096100249100002201210700001801232700002701250700002101277700002101298700001801319856008501337 2009 eng d00aTowards a Very Low Energy Building Stock: Modeling the US Commercial Building Stock to Support Policy and Innovation Planning0 aTowards a Very Low Energy Building Stock Modeling the US Commerc0 v37:53 a

This paper describes the origin, structure and continuing development of a model of time varying energy consumption in the US commercial building stock. The model is based on a flexible structure that disaggregates the stock into various categories (e.g. by building type, climate, vintage and life-cycle stage) and assigns attributes to each of these (e.g. floor area and energy use intensity by fuel type and end use), based on historical data and user-defined scenarios for future projections. In addition to supporting the interactive exploration of building stock dynamics, the model has been used to study the likely outcomes of specific policy and innovation scenarios targeting very low future energy consumption in the building stock. Model use has highlighted the scale of the challenge of meeting targets stated by various government and professional bodies, and the importance of considering both new construction and existing buildings.

1 aCoffey, Brian, E.1 aBorgeson, Sam1 aSelkowitz, Stephen, E.1 aApte, Joshua, S.1 aMathew, Paul, A.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/towards-very-low-energy-building02680nas a2200133 4500008004100000245008800041210006900129260003600198520216900234100001802403700002202421700002002443856008302463 2008 eng d00aBenchmarking and Equipment and Controls Assessment for a ‘Big Box’ Retail Chain0 aBenchmarking and Equipment and Controls Assessment for a Big Box aAsilomar, California, USAc20083 a

The paper describes work to enable improved energy performance of existing and new retail stores belonging to a national chain and thereby also identify measures and tools that would improve the performance of ‘big box' stores generally. A detailed energy simulation model of a standard store design was developed and used to:

The core enabling task of the project was to develop an energy model of the current standard design using the EnergyPlus simulation program. For the purpose of verification of the model against actual utility bills, the model was reconfigured to represent twelve existing stores (seven relatively new stores and five older stores) in different US climates and simulations were performed using weather data obtained from the National Weather Service. The results of this exercise, which showed generally good agreement between predicted and measured total energy use, suggest that dynamic benchmarking based on energy simulation would be an effective tool for identifying operational problems that affect whole building energy use. The models of the seven newer stores were then configured with manufacturers' performance data for the equipment specified in the current design and used to assess the energy and cost benefits of increasing the efficiency of selected HVAC, lighting and envelope components. The greatest potential for cost-effective energy savings appears to be a substantial increase in the efficiency of the blowers in the roof top units and improvements in the efficiency of the lighting. The energy benefits of economizers on the roof-top units were analyzed and found to be very sensitive to the operation of the exhaust fans used to control building pressurization.

1 aHaves, Philip1 aCoffey, Brian, E.1 aWilliams, Scott uhttps://simulationresearch.lbl.gov/publications/benchmarking-and-equipment-and00500nas a2200133 4500008004100000245009200041210006900133260001800202300000800220100001800228700001600246700001600262856008800278 2007 eng d00aComputational fluid dynamics for indoor environment modeling: past, present, and future0 aComputational fluid dynamics for indoor environment modeling pas aSendai, Japan a1-91 aChen, Qingyan1 aZhang, Zhao1 aZuo, Wangda uhttps://simulationresearch.lbl.gov/publications/computational-fluid-dynamics-indoor00514nas a2200133 4500008004100000245007300041210006900114260002800183100002100211700002000232700002100252700002000273856008700293 2007 eng d00aComputer Model of a University Building Using the EnergyPlus Program0 aComputer Model of a University Building Using the EnergyPlus Pro aBeijing, Chinac09/20071 aMonfet, Danielle1 aZmeureanu, Radu1 aCharneux, Roland1 aLemire, Nicolas uhttps://simulationresearch.lbl.gov/publications/computer-model-university-building00592nas a2200145 4500008004100000245011600041210006900157260002800226100002300254700001700277700002000294700002300314700002200337856008700359 2007 eng d00aPotential of Buried Pipes Systems and Derived Techniques for Passive Cooling of Buildings in Brazilian Climates0 aPotential of Buried Pipes Systems and Derived Techniques for Pas aBeijing, Chinac09/20071 aHollmuller, Pierre1 aCarlo, Joyce1 aOrdenes, Martin1 aWestphal, Fernando1 aLamberts, Roberto uhttps://simulationresearch.lbl.gov/publications/potential-buried-pipes-systems-and00400nas a2200121 4500008004100000245004600041210004600087260001800133300001200151100001600163700001800179856008100197 2007 eng d00aReal time airflow simulation in buildings0 aReal time airflow simulation in buildings aSendai, Japan a459-4661 aZuo, Wangda1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/real-time-airflow-simulation00501nas a2200133 4500008004100000245006700041210006700108260002800175100002100203700002400224700002000248700001500268856008400283 2007 eng d00aSimulation Enhanced Prototyping of an Experimental Solar House0 aSimulation Enhanced Prototyping of an Experimental Solar House aBeijing, Chinac09/20071 aChoudhary, Ruchi1 aAugenbroe, Godfried1 aGentry, Russell1 aHu, Huafen uhttps://simulationresearch.lbl.gov/publications/simulation-enhanced-prototyping00465nas a2200121 4500008004100000245005800041210005800099260002800157100002100185700002500206700002300231856008900254 2007 eng d00aSimulation of Energy Management Systems in EnergyPlus0 aSimulation of Energy Management Systems in EnergyPlus aBeijing, Chinac09/20071 aEllis, Peter, G.1 aTorcellini, Paul, A.1 aCrawley, Drury, B. uhttps://simulationresearch.lbl.gov/publications/simulation-energy-management-systems00426nas a2200121 4500008004100000245005500041210005500096260001900151300001200170100001600182700001800198856008800216 2007 eng d00aValidation of fast fluid dynamics for room airflow0 aValidation of fast fluid dynamics for room airflow aBeijing, China a980-9831 aZuo, Wangda1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/validation-fast-fluid-dynamics-room00483nas a2200121 4500008004100000245008300041210006900124260002800193100001900221700001800240700001900258856008400277 2007 eng d00aVAV System Optimization through Continuous Commissioning in an Office Building0 aVAV System Optimization through Continuous Commissioning in an O aSan Francisco, CAc20071 aCho, Young-Hum1 aPang, Xiufeng1 aLiu, Mingsheng uhttps://simulationresearch.lbl.gov/publications/vav-system-optimization-through00535nas a2200133 4500008004100000245011600041210006900157260003200226100001500258700002000273700001300293700001500306856008000321 2006 eng d00aThe Application of Building Energy Simulation and Calibration in Two High-Rise Commercial Buildings in Shanghai0 aApplication of Building Energy Simulation and Calibration in Two aCambridge, MA, USAc08/20061 aPan, Yiqun1 aHuang, Zhizhong1 aWu, Gang1 aChen, Chen uhttps://simulationresearch.lbl.gov/publications/application-building-energy00625nas a2200169 4500008004100000245009000041210006900131260002500200100002600225700001200251700002400263700002400287700001900311700001600330700002100346856008800367 2006 eng d00aCarbon Nanotube (CNT)-Centric Thermal Management of Future High Power Microprocessors0 aCarbon Nanotube CNTCentric Thermal Management of Future High Pow aAtlanta, GAc03/20061 aBhattacharya, Prajesh1 aWei, X.1 aFedorov, Andrei, G.1 aJoshi, Yogendra, K.1 aBajwa, Navdeep1 aCao, Anyuan1 aAjayan, Pulickel uhttps://simulationresearch.lbl.gov/publications/carbon-nanotube-cnt-centric-thermal00546nas a2200121 4500008004100000245012400041210006900165260003200234100002300266700002600289700002300315856008600338 2006 eng d00aExperience Testing EnergyPlus With the IEA HVAC Bestest E300-E545 Series and IEA HVAC Bestest Fuel-Fired Furnace Series0 aExperience Testing EnergyPlus With the IEA HVAC Bestest E300E545 aCambridge, MA, USAc08/20061 aWitte, Michael, J.1 aHenninger, Robert, H.1 aCrawley, Drury, B. uhttps://simulationresearch.lbl.gov/publications/experience-testing-energyplus-iea00480nas a2200133 4500008004100000245005400041210005400095260003600149100001700185700002000202700002000222700001900242856008500261 2006 eng d00aHalfway to Zero Energy in a Large Office Building0 aHalfway to Zero Energy in a Large Office Building aPacific Grove, CA, USAc08/20061 aHanson, Mark1 aCarlson, Steven1 aSammartano, Dan1 aTaylor, Thomas uhttps://simulationresearch.lbl.gov/publications/halfway-zero-energy-large-office00853nas a2200133 4500008004100000245007200041210006900113260001500182520040600197100001300603700001800616700002000634856006500654 2006 eng d00aA Library of HVAC Component Models for use in Automated Diagnostics0 aLibrary of HVAC Component Models for use in Automated Diagnostic aBoston, MA3 a

The paper describes and documents a library of equipment reference models developed for automated fault detection and diagnosis of secondary HVAC system (air handling units and air distribution systems). The models are used to predict the performance that would be expected in the absence of faults. The paper includes a description of the use of automatic documentation methods in the library.

1 aXu, Peng1 aHaves, Philip1 aCurtil, Dimitri uhttp://www.ibpsa.us/pub/simbuild2006/papers/SB06_034_041.pdf00526nas a2200121 4500008004100000245008900041210006900130260003200199100003100231700002700262700002700289856008800316 2006 eng d00aLow Energy Cooling Technologies for Sub-Tropical/Warm Humid Climate Building Systems0 aLow Energy Cooling Technologies for SubTropicalWarm Humid Climat aCambridge, MA, USAc08/20061 aChowdhury, Ashfaque, Ahmed1 aRasul, Mohammad, Golam1 aKamal, Mohammad, Masud uhttps://simulationresearch.lbl.gov/publications/low-energy-cooling-technologies-sub00530nas a2200109 4500008004100000245014700041210006900188260003200257100002400289700002300313856008400336 2006 eng d00aMethodology for Analyzing the Technical Potential for Energy Performance in the U.S. Commercial Buildings Sector With Detailed Energy Modeling0 aMethodology for Analyzing the Technical Potential for Energy Per aCambridge, MA, USAc08/20061 aGriffith, Brent, T.1 aCrawley, Drury, B. uhttps://simulationresearch.lbl.gov/publications/methodology-analyzing-technical00504nas a2200133 4500008004100000245006100041210006000102260003600162100002500198700001800223700001800241700002300259856008800282 2006 eng d00aZero Energy Buildings: A Critical Look at the Definition0 aZero Energy Buildings A Critical Look at the Definition aPacific Grove, CA, USAc08/20061 aTorcellini, Paul, A.1 aPless, Shanti1 aDeru, Michael1 aCrawley, Drury, B. uhttps://simulationresearch.lbl.gov/publications/zero-energy-buildings-critical-look00530nas a2200133 4500008004100000245008400041210006900125260003000194100002300224700001800247700002100265700002400286856008600310 2005 eng d00aContrasting the Capabilities of Building Energy Performance Simulation Programs0 aContrasting the Capabilities of Building Energy Performance Simu aMontreal, Canadac08/20051 aCrawley, Drury, B.1 aHand, Jon, W.1 aKummert, Michael1 aGriffith, Brent, T. uhttps://simulationresearch.lbl.gov/publications/contrasting-capabilities-building00624nas a2200157 4500008004100000245013600041210007000177260001200247490000700259100002100266700001700287700002600304700002600330700001700356856009300373 2005 eng d00aEffects of double glazed façade on energy consumption, thermal comfort and condensation for a typical office building in Singapore0 aEffects of double glazed façade on energy consumption thermal co c06/20050 v371 aWong, Nyuk, Hien1 aWang, Liping1 aChandra, Aida, Noplie1 aPandey, Anupama, Rana1 aWei, Xiaolin uhttps://simulationresearch.lbl.gov/publications/effects-double-glazed-fa%C3%A7ade-energy01819nas a2200145 4500008004100000245005600041210005600097260003000153520132100183100001801504700002301522700001801545700002201563856008801585 2005 eng d00aImproving the Data Available to Simulation Programs0 aImproving the Data Available to Simulation Programs aMontreal, Canadac08/20053 a

Building performance simulation tools have significantly improved in quality and depth of analysis capability over the past thirty-five years. Yet despite these increased capabilities, simulation programs still depend on user entry for significant data about building components, loads, and other typically scheduled inputs. This often forces users to estimate values or find previously compiled sets of data for these inputs. Often there is little information about how the data were derived, what purposes it is fit for, which standards apply, uncertainty associated with each data field as well as a general description of the data.

A similar problem bedeviled access to weather data and Crawley, Hand, and Lawrie (1999) described a generalized weather data format developed for use with two energy simulation programs which has subsequently lead to a repository which is accessed by thousands of practitioners each year.

This paper describes a generalized format and data documentation for user input—whether it is building envelope components, scheduled loads, or environmental emissions—the widgets upon which all models are dependant. We present several examples of the new input data format including building envelope component, a scheduled occupant load, and environmental emissions.

1 aHand, Jon, W.1 aCrawley, Drury, B.1 aDonn, Michael1 aLawrie, Linda, K. uhttps://simulationresearch.lbl.gov/publications/improving-data-available-simulation00369nas a2200121 4500008004100000022001500041245003000056210002800086100002000114700001800134700001900152856007600171 2004 eng d aLBNL-5465800aBuildOpt 1.0.1 validation0 aBuildOpt 101 validation1 aWetter, Michael1 aPolak, Elijah1 aCarey, Van, P. uhttps://simulationresearch.lbl.gov/publications/buildopt-101-validation00460nas a2200109 4500008004100000245006700041210006500108260003600173100002900209700002300238856008900261 2004 eng d00aComparative Analysis of One-Dimensional Slat-Type Blind Models0 aComparative Analysis of OneDimensional SlatType Blind Models aBoulder, Colorado, USAc08/20041 aChantrasrisalai, Chanvit1 aFisher, Daniel, E. uhttps://simulationresearch.lbl.gov/publications/comparative-analysis-one-dimensional00835nas a2200277 4500008004100000245002600041210002500067260003600092100002300128700002200151700002500173700003000198700002300228700002400251700002400275700002100299700001900320700002600339700001800365700002300383700002100406700002400427700002100451700001500472856007000487 2004 eng d00aEnergyPlus: An Update0 aEnergyPlus An Update aBoulder, Colorado, USAc08/20041 aCrawley, Drury, B.1 aLawrie, Linda, K.1 aPedersen, Curtis, O.1 aWinkelmann, Frederick, C.1 aWitte, Michael, J.1 aStrand, Richard, K.1 aLiesen, Richard, J.1 aBuhl, Walter, F.1 aHuang, Yu, Joe1 aHenninger, Robert, H.1 aGlazer, Jason1 aFisher, Daniel, E.1 aShirley, Don, B.1 aGriffith, Brent, T.1 aEllis, Peter, G.1 aGu, Lixing uhttps://simulationresearch.lbl.gov/publications/energyplus-update00520nas a2200121 4500008004100000245009100041210006900132260003600201100002300237700002600260700002300286856008900309 2004 eng d00aExperience Testing EnergyPlus With the ASHRAE 1052-RP Building Fabric Analytical Tests0 aExperience Testing EnergyPlus With the ASHRAE 1052RP Building Fa aBoulder, Colorado, USAc08/20041 aWitte, Michael, J.1 aHenninger, Robert, H.1 aCrawley, Drury, B. uhttps://simulationresearch.lbl.gov/publications/experience-testing-energyplus-ashrae00484nas a2200121 4500008004100000245010400041210006900145260001200214490000700226100002400233700001800257856008700275 2004 eng d00aFramework for Coupling Room Air Models to Heat Balance Model Load and Energy Calculations (RP-1222)0 aFramework for Coupling Room Air Models to Heat Balance Model Loa c04/20040 v101 aGriffith, Brent, T.1 aChen, Qingyan uhttps://simulationresearch.lbl.gov/publications/framework-coupling-room-air-models00495nas a2200133 4500008004100000245005400041210005300095260003800148100002300186700002500209700001800234700002000252856008900272 2004 eng d00aGraph-Theoretic Methods in Simulation Using SPARK0 aGraphTheoretic Methods in Simulation Using SPARK aArlington, Virginia, USAc04/20041 aSowell, Edward, F.1 aMoshier, Michael, A.1 aHaves, Philip1 aCurtil, Dimitri uhttps://simulationresearch.lbl.gov/publications/graph-theoretic-methods-simulation-000479nas a2200109 4500008004100000245009400041210006900135260003600204100001900240700002400259856008600283 2004 eng d00aImprovement of the ASHRAE Secondary HVAC Toolkit Simple Cooling Coil Model for Simulation0 aImprovement of the ASHRAE Secondary HVAC Toolkit Simple Cooling  aBoulder, Colorado, USAc08/20041 aChillar, Rahul1 aLiesen, Richard, J. uhttps://simulationresearch.lbl.gov/publications/improvement-ashrae-secondary-hvac00461nas 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-fluid00443nas a2200109 4500008004100000245006200041210006200103260003600165100002400201700001900225856008900244 2004 eng d00aVariable Heat Recovery in Double Bundle Electric Chillers0 aVariable Heat Recovery in Double Bundle Electric Chillers aBoulder, Colorado, USAc08/20041 aLiesen, Richard, J.1 aChillar, Rahul uhttps://simulationresearch.lbl.gov/publications/variable-heat-recovery-double-bundle01417nas a2200145 4500008004100000245008800041210006900129490000700198520090100205100001901106700001801125700001801143700002201161856008801183 2002 eng d00aOn Approaches to Couple Energy Simulation and Computational Fluid Dynamics Programs0 aApproaches to Couple Energy Simulation and Computational Fluid D0 v373 a

Energy simulation (ES) and computational fluid dynamics (CFD) can play important roles in building design by providing complementary information about the buildings' environmental performance. However, separate applications of ES and CFD are usually unable to give an accurate prediction of building performance due to the assumptions involved in the separate calculations. Integration of ES and CFD eliminates many of these assumptions since the information provided by the models is complementary. Several different approaches to integrating ES and CFD are described. In order to bridge the discontinuities of time-scale, spatial resolution and computing speed between ES and CFD programs, a staged coupling strategy for different problems is proposed. The paper illustrates a typical dynamic coupling process by means of an example implemented using the EnergyPlus and MIT-CFD programs.

1 aZhai, Zhiqiang1 aChen, Qingyan1 aHaves, Philip1 aKlems, Joseph, H. uhttps://simulationresearch.lbl.gov/publications/approaches-couple-energy-simulation02220nas a2200145 4500008004100000024001700041245013300058210006900191260003900260520164100299100002001940700001801960700001601978856008001994 2002 eng d aLBNL - 5113400aThe Integration of Engineering and Architecture: a Perspective on Natural Ventilation for the new San Francisco Federal Building0 aIntegration of Engineering and Architecture a Perspective on Nat aAsilomar, California, USAc08/20023 a

A description of the in-progress design of a new Federal Office Building for San Francisco is used to illustrate a number of issues arising in the design of large, naturally ventilated office buildings. These issues include the need for an integrated approach to design involving the architects, mechanical and structural engineers, lighting designers and specialist simulation modelers. In particular, the use of natural ventilation, and the avoidance of air-conditioning, depends on the high degree of exposed thermal mass made possible by the structural scheme and by the minimization of solar heat gains while maintaining the good daylighting that results from optimization of the façade. Another issue was the need for a radical change in interior space planning in order to enhance the natural ventilation; all the individual enclosed offices are located along the central spine of each floorplate rather than at the perimeter. The role of integration in deterring the undermining of the design through value engineering is discussed. The comfort criteria for the building were established based on the recent extension to the ASHRAE comfort standard based on the adaptive model for naturally ventilated buildings. The building energy simulation program EnergyPlus was used to compare the performance of different natural ventilation strategies. The results indicate that, in the San Francisco climate, wind-driven ventilation provides sufficient nocturnal cooling to maintain comfortable conditions and that external chimneys do not provide significant additional ventilation at times when it when it would be beneficial.

1 aMcConahey, Erin1 aHaves, Philip1 aChirst, Tim uhttps://simulationresearch.lbl.gov/publications/integration-engineering-and01524nas a2200181 4500008004100000050001500041245009700056210006900153260003600222300001000258490000600268520090100274100001901175700001801194700002201212700001801234856009001252 2001 eng d aLBNL-4828600aStrategies for Coupling Energy Simulation Programs and Computational Fluid Dynamics Programs0 aStrategies for Coupling Energy Simulation Programs and Computati aRio de Janeiro, Brazilc08/2001 a59-660 v13 a

Energy simulation (ES) and computational fluid dynamics (CFD) can play important roles in building design by providing complementary information about the buildings' environmental performance. However, separate applications of ES and CFD are usually unable to give an accurate prediction of building performance due to the assumptions involved in the separate calculations. Integration of ES and CFD eliminates many of these assumptions since the information provided by the models is complementary. Several different approaches to integrating ES and CFD are described. In order to bridge the discontinuities of time-scale, spatial resolution and computing speed between ES and CFD programs, a staged coupling strategy for different problems is proposed. The paper illustrates a typical dynamic coupling process by means of an example implemented using the EnergyPlus and MIT-CFD programs.

1 aZhai, Zhiqiang1 aChen, Qingyan1 aKlems, Joseph, H.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/strategies-coupling-energy-simulation01366nas a2200145 4500008004100000245010700041210006900148260002800217520080900245100001801054700002201072700002001094700001901114856008701133 2001 eng d00aUse of Whole Building Simulation in On-Line Performance Assessment: Modeling and Implementation Issues0 aUse of Whole Building Simulation in OnLine Performance Assessmen aRio de Janeiroc08/20013 a

The application of model-based performance assessment at the whole building level is explored. The information requirements for a simulation to predict the actual performance of a particular real building, as opposed to estimating the impact of design options, are addressed with particular attention to common sources of input error and important deficiencies in most simulation models. The role of calibrated simulations is discussed. The communication requirements for passive monitoring and active testing are identified and the possibilities for using control system communications protocols to link on-line simulation and energy management and control systems are discussed. The potential of simulation programs to act as "plug-and-play" components on building control networks is discussed.

1 aHaves, Philip1 aSalsbury, Tim, I.1 aClaridge, David1 aLiu, Mingsheng uhttps://simulationresearch.lbl.gov/publications/use-whole-building-simulation-line01863nas 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-design01168nas a2200169 4500008004100000245007600041210006900117260001200186300001200198490000700210520062300217653002400840100001900864700001900883700001500902856008100917 2000 eng d00aBuilding simulation: an overview of development and information sources0 aBuilding simulation an overview of development and information s c05/2000 a347-3610 v353 a

We review the state-of-the-art on the development and application of computer-aided building simulation by addressing some crucial questions in the field. Although the answers are not intended to be comprehensive, they are sufficiently varied to provide an overview ranging from the historical and technical development to choosing a suitable simulation program and performing building simulation. Popular icons of major interested agencies and simulation tools and key information sources are highlighted. Future trends in the design and operation of energy-efficient ‘green' buildings are briefly described.

10abuilding simulation1 aHong, Tianzhen1 aChou, Siaw, K.1 aBong, T.Y. uhttps://simulationresearch.lbl.gov/publications/building-simulation-overview01723nas a2200217 4500008004100000245005700041210005500098260001200153300001200165490000700177520108800184653002401272653001501296653001001311653002601321653001701347100001901364700001901383700001501402856008801417 1999 eng d00aA design day for building load and energy estimation0 adesign day for building load and energy estimation c07/1999 a469-4770 v343 a

We describe how a design day for building energy performance simulation can be selected from a typical meteorological year of a location. The advantages of the design day weather file are its simplicity and flexibility in use with simulation programs. The design day is selected using a weather parameter comprising the daily average dry bulb temperature and total solar insolation. The selection criterion addresses the balance between the need to minimise the part-load performance of the air-conditioning systems and plants and the number of hours of load not met. To validate the versatility of the design day weather file, we compare simulation results of the peak load and load profile of a building obtained from the DOE-2.1E code and a specially developed load estimation program, PEAKLOAD. PEAKLOAD is developed using the transfer function method and ASHRAE databases. Comparative results are in good agreement, indicating that a design day thus selected can be used when quick answers are required and simulations using a TMY file cannot be easily done or justified.

10abuilding simulation10adesign day10adoe-210apeak load calculation10aweather data1 aHong, Tianzhen1 aChou, Siaw, K.1 aBong, T.Y. uhttps://simulationresearch.lbl.gov/publications/design-day-building-load-and-energy00555nas a2200169 4500008003900000245004100039210004100080260003500121100002500156700002200181700002400203700002200227700002000249700002500269700003000294856006100324 1989 d00aThermal Energy Storage System Sizing0 aThermal Energy Storage System Sizing aVancouver, BC, Canadac01/19891 aDumortier, Dominique1 aKammerud, Ron, C.1 aBirdsall, Bruce, E.1 aAndersson, Brandt1 aEto, Joseph, H.1 aCarroll, William, L.1 aWinkelmann, Frederick, C. uhttp://www.ibpsa.org/proceedings/BS1989/BS89_357_362.pdf01698nas a2200169 4500008004100000245006200041210006000103260001200163300001200175490000600187520117800193100001601371700002101387700002201408700001801430856008001448 1987 eng d00aPerformance of Roofpond Cooled Residences in U.S. Climate0 aPerformance of Roofpond Cooled Residences in US Climate c01/1987 a265-2920 v43 a

The thermal advantages of a roofpond as an element of a residential cooling system are described. The authors conducted heat transfer experiments at two roofpond residences (RPRs) at Trinity University; the authors used data from these experiments to validate RPR simulations. Results of measurements of vertical and horizontal temperature differences within roofponds are discussed. Horizontal heat transfer within one water bag was effective. Thermal resistance at the outer surface of a water bag with a deflated glazing can be significant. Simulation shows that an RPR can provide comfort without supplemental sensible cooling during almost all hours of a typical summer in any U.S climate. Ceiling fans are important in most climates. In the most demanding climates, the residence and the pond insulating panels must have high R-value. A map is included that provides RPR design guidance. The simulations indicate that dehumidification will be required to control mold, mildew, and ceiling condensation in an RPR in most climates; energy and power displacement by an RPR is sensitive to the humidity control required and the efficiency of the dehumidifier used.

1 aClark, Gene1 aLoxsom, Fred, M.1 aDoderer, Earl, S.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/performance-roofpond-cooled00671nas a2200193 4500008003900000245006700039210006200106260004300168490000800211100002400219700002100243700002400264700001700288700001600305700002200321700002100343700003000364856008300394 1985 d00aThe DOE-2 Computer Program for Thermal Simulation of Buildings0 aDOE2 Computer Program for Thermal Simulation of Buildings bAmerican Institute of Physicsc01/19850 v1351 aBirdsall, Bruce, E.1 aBuhl, Walter, F.1 aCurtis, Richard, B.1 aErdem, Ender1 aEto, Joseph1 aHirsch, James, J.1 aOlson, Karen, H.1 aWinkelmann, Frederick, C. uhttps://simulationresearch.lbl.gov/publications/doe-2-computer-program-thermal00595nas a2200181 4500008003900000245004700039210004200086260002300128100002400151700002400175700002100199700001700220700002000237700002200257700002100279700003000300856008300330 1984 d00aThe DOE-2 Building Energy Analysis Program0 aDOE2 Building Energy Analysis Program aSingaporec05/19841 aCurtis, Richard, B.1 aBirdsall, Bruce, E.1 aBuhl, Walter, F.1 aErdem, Ender1 aEto, Joseph, H.1 aHirsch, James, J.1 aOlson, Karen, H.1 aWinkelmann, Frederick, C. uhttps://simulationresearch.lbl.gov/publications/doe-2-building-energy-analysis00478nas a2200133 4500008004100000245006100041210006100102260001700163100001600180700002100196700001800217700002200235856008700257 1983 eng d00aResults of Validated Simulations of Roof Pond Residences0 aResults of Validated Simulations of Roof Pond Residences aSanta Fe, NM1 aClark, Gene1 aLoxsom, Fred, M.1 aHaves, Philip1 aDoderer, Earl, S. uhttps://simulationresearch.lbl.gov/publications/results-validated-simulations-roof01768nas a2200169 4500008004100000245009200041210006900133260002500202300001200227490000600239520120000245100001801445700001601463700001801479700001501497856008601512 1982 eng d00aAccuracy of a Simple Method of Estimating the Minimum Temperature of a Sealed Roof Pond0 aAccuracy of a Simple Method of Estimating the Minimum Temperatur aHouston, TXc07/1982 a709-7140 v53 a

Detailed heat flux and temperature measurements have been made in two residential scale roof pond buildings in San Antonio, Texas from July to November 1981. The minimum temprature of the 4 in deep roof pond sealed in PVC bags was approximately equal to the minimum ambient dry bulb temperature. The sensitivity of this equality to changes in meteorological conditions, maximum pond temperature and thermal load is evaluated using the measurements. Verified simulations are then used to evaluate the sensitivity of this equality to changes in the thermal load, and to changes in the depth, surface emittance and surface thermal resistance of the sealed pond in various climates. For the range of roof pond design options of interest in passive cooling of buildings, the minimum pond temperature was found to be within 2 F of the minimum ambient temperature in all climates considered. The equality of these minimum temperatures is advocated as a useful rule of thumb for feasibility assessment and as part of a simplified design methodology. The simulated minimum pond temperature was found to be surprisingly insensitive to a 50% decrease in the fraction of pond area exposed to the sky.

1 aSchutt, Brady1 aClark, Gene1 aHaves, Philip1 aMerino, M. uhttps://simulationresearch.lbl.gov/publications/accuracy-simple-method-estimating00578nas a2200157 4500008004100000022002900041245004800070210004800118260008900166100001500255700001800270700001600288700001800304700001600322856008200338 1981 eng d a0895530333 978089553033200aHeat Loss Rates from Wetted Tilted Surfaces0 aHeat Loss Rates from Wetted Tilted Surfaces aMiami Beach, FLbAmerican Section of the International Solar Energy Societyc11/19811 aHaines, R.1 aHaves, Philip1 aVollink, D.1 aBowen, Arthur1 aClark, Gene uhttps://simulationresearch.lbl.gov/publications/heat-loss-rates-wetted-tilted00500nas a2200133 4500008004100000245007400041210006900115260002300184100002100207700001600228700001500244700001800259856008900277 1981 eng d00aMeasurement of Components of Heat Transfer in Passive Cooling Systems0 aMeasurement of Components of Heat Transfer in Passive Cooling Sy aMiami, FLc11/19811 aLoxsom, Fred, M.1 aClark, Gene1 aMerino, M.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/measurement-components-heat-transfer00416nas a2200121 4500008004100000245004800041210004800089260002500137100001800162700001500180700001600195856008300211 1980 eng d00aHeat Transfer in Passively Cooled Buildings0 aHeat Transfer in Passively Cooled Buildings aOrlando, FLc07/19801 aHaves, Philip1 aBently, D.1 aClark, Gene uhttps://simulationresearch.lbl.gov/publications/heat-transfer-passively-cooled01326nas a2200229 4500008004100000245005900041210005500100260001200155300001200167490000800179520057600187653003200763653003400795653002500829653003300854653004000887653002500927653001900952100001800971700002200989856008501011 1975 eng d00aThe Orientation of the Magnetic Field in Radio Sources0 aOrientation of the Magnetic Field in Radio Sources c11/1975 a53P-56P0 v1733 a

Recent data on the polarization of extragalactic radio sources are used to investigate the distribution of Delta, the angle between the major axis of a source and the intrinsic position angle of the E vector of linear polarization. Previous work on this subject has led to widely divergent conclusions. It is found that sources of high radio luminosity usually have Delta near 90 deg, implying that the magnetic fields in such sources are oriented along the major axis. For radio galaxies with low luminosity, on the other hand, Delta tends to lie nearer zero deg.

10aextragalactic radio sources10amagnetic field configurations10apolarization (waves)10apolarization characteristics10apolarized electromagnetic radiation10aradiant flux density10aradio galaxies1 aHaves, Philip1 aConway, Robin, G. uhttps://simulationresearch.lbl.gov/publications/orientation-magnetic-field-radio01622nas a2200301 4500008004100000245004700041210004300088260001200131300001200143490000800155520072700163653002600890653003200916653001900948653001900967653002300986653004001009653001201049653002501061653002001086653002501106653001801131653002601149100001801175700002201193700002001215856008501235 1974 eng d00aThe Polarization of Radio Sources at 31 CM0 aPolarization of Radio Sources at 31 CM c10/1974 a117-1310 v1693 a

Measurements of the linear polarization of extragalactic radio sources have been made over a range of wavelengths in order to study both the properties of the sources themselves and the Faraday rotation along the line of sight to the observer. As part of a continuing program of such measurements the flux densities and integrated polarizations of 226 sources (including 134 quasars) were observed at 966 MHz (lambda 31 cm), to complement previous measurements at lambda 49 and lambda 74 cm (Conway et al. 1972). These results have been combined with others at shorter wavelengths in a discussion of the polarization properties of quasars (Conway et al. 1974). All the sources have angular sizes of 1 arcmin or less

10aAstronomical Catalogs10aextragalactic radio sources10afaraday effect10ainterferometry10amicrowave emission10apolarized electromagnetic radiation10aQuasars10aradiant flux density10aradio astronomy10astatistical analysis10atables (data)10avery high frequencies1 aHaves, Philip1 aConway, Robin, G.1 aStannard, David uhttps://simulationresearch.lbl.gov/publications/polarization-radio-sources-31-cm01668nas a2200193 4500008004100000245003800041210003400079260001200113300001200125490000800137520111500145100002201260700001801282700002601300700002001326700002501346700002401371856007901395 1974 eng d00aThe Radio Polarization of Quasars0 aRadio Polarization of Quasars c07/1974 a137-1620 v1683 a

Observations over a wide range of wavelengths, 2.2 ≤ λ ≤ 73 cm, have been combined to define the wavelength variation of the degree of linear polarization m(λ) for 120 quasars with known redshift. For the majority, m(λ) decreases monotonically with increasing wavelength but for 35 sources the polarization curve is inverted at short wavelengths. A classification is given, based on both the polarization curve and the radio spectrum, and the results are interpreted in terms of the presence or absence of opaque components in the source. The depolarization which occurs at long wavelengths is accounted for by a combination of spectral effects and Faraday depolarization. For 46 steep-spectrum sources the depolarization curve appears to be dominated by the Faraday effect, and has been used to deduce the electron density within the radiating components. In this group of sources the correlation between depolarization and redshift noted by Kronberg et al. is confirmed and strengthened. A discussion is given of some theoretical models of radio sources in the light of the depolarization data.

1 aConway, Robin, G.1 aHaves, Philip1 aKronberg, Philipp, P.1 aStannard, David1 aVallée, Jacques, P.1 aWardle, John, F. C. uhttps://simulationresearch.lbl.gov/publications/radio-polarization-quasars