02515nas 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/S037877881831976502420nas a2200253 4500008004100000022001300041245008400054210006900138260001200207300001400219490000800233520166500241653002101906653002101927653002501948653001901973653001801992653001502010100001402025700001902039700002202058700001902080856006702099 2019 eng d a0360132300aInferring occupant counts from Wi-Fi data in buildings through machine learning0 aInferring occupant counts from WiFi data in buildings through ma c05/2019 a281 - 2940 v1583 a

An important approach to curtail building energy consumption is to optimize building control based on occupancy information. Various studies proposed to estimate occupant counts through different approaches and sensors. However, high cost and privacy concerns remain as major barriers, restricting the practice of occupant count detection. In this study, we propose a novel method utilizing data from widely deployed Wi-Fi infrastructure to infer occupant counts through machine learning. Compared with the current indirect measurement methods, our method improves the performance of estimating people count: (1) we avoid privacy concerns by anonymizing and reshuffling the MAC addresses on a daily basis; (2) we adopted a heuristic feature engineer approach to cluster connected devices into different types based on their daily connection duration. We tested the method in an office building located in California. In an area with an average occupancy of 22–27 people and a peak occupancy of 48–74 people, the root square mean error on the test set is less than four people. The error is within two people counts for more than 70% of estimations, and less than six counts for more than 90% of estimations, indicating a relatively high accuracy. The major contribution of this study is proposing a novel and accurate approach to detect occupant counts in a non-intrusive way, i.e., utilizing existing Wi-Fi infrastructure in buildings without requiring the installation of extra hardware or sensors. The method we proposed is generic and could be applied to other commercial buildings to infer occupant counts for energy efficient building control.

10aBuilding control10aMachine learning10aOccupancy estimation10aOccupant count10aRandom forest10aWi-Fi data1 aWang, Zhe1 aHong, Tianzhen1 aPiette, Mary, Ann1 aPritoni, Marco uhttps://linkinghub.elsevier.com/retrieve/pii/S036013231930333601992nas a2200229 4500008004100000022001300041245008400054210006900138260001200207300001200219490000800231520128900239653003601528653001501564653001701579653002601596653001801622653001601640100001901656700002001675856006701695 2019 eng d a0360132300aIntegrating physics-based models with sensor data: An inverse modeling approach0 aIntegrating physicsbased models with sensor data An inverse mode c05/2019 a23 - 310 v1543 a

Physics-based building energy models (e.g., EnergyPlus) rely on some unknown input parameters (e.g., zone air infiltration) that are hard to measure, leading to uncertainty in simulation results especially for existing buildings with varying operating conditions. With the increasing deployment of smart thermostats, zone air temperature data are readily available, posing a new opportunity for building energy modeling if such data can be harnessed. This study presents a novel inverse modeling approach which inverses the zone air heat balance equation and uses the measured zone air temperature to analytically calculate the zone air infiltration rate and zone internal thermal mass (e.g., furniture, interior partitions), which are two important model parameters with great variability and difficult to measure. This paper introduces the technical concept and algorithms of the inverse models, their implementation in EnergyPlus, and verification using EnergyPlus simulated building performance data. The inverse modeling approach provides new opportunities for integrating data from massive IoT sensors and devices to enhance the accuracy of simulation results which are used to inform decision making on energy retrofits and efficiency improvements of existing buildings.

10abuilding performance simulation10aenergyplus10ainfiltration10ainternal thermal mass10ainverse model10asensor data1 aHong, Tianzhen1 aLee, Sang, Hoon uhttps://linkinghub.elsevier.com/retrieve/pii/S036013231930160X02132nas a2200241 4500008004100000022001300041245011800054210006900172260001600241300001400257490000800271520136600279653001501645653001701660653002101677653001701698653001601715653002401731100001201755700001901767700001801786856008601804 2019 eng d a0378778800aAn inverse approach to solving zone air infiltration rate and people count using indoor environmental sensor data0 ainverse approach to solving zone air infiltration rate and peopl cJan-09-2019 a228 - 2420 v1983 a

Physics-based simulation of energy use in buildings is widely used in building design and performance rating, controls design and operations. However, various challenges exist in the modeling process. Model parameters such as people count and air infiltration rate are usually highly uncertain, yet they have significant impacts on the simulation accuracy. With the increasing availability and affordability of sensors and meters in buildings, a large amount of measured data has been collected including indoor environmental parameters, such as room air dry-bulb temperature, humidity ratio, and CO2 concentration levels. Fusing these sensor data with traditional energy modeling poses new opportunities to improve simulation accuracy. This study develops a set of physics-based inverse algorithms which can solve the highly uncertain and hard-to-measure building parameters such as zone-level people count and air infiltration rate. A simulation-based case study is conducted to verify the inverse algorithms implemented in EnergyPlus covering various sensor measurement scenarios and different modeling use cases. The developed inverse models can solve the zone people count and air infiltration at sub-hourly resolution using the measured zone air temperature, humidity and/or CO2 concentration given other easy-to-measure model parameters are known.

10aenergyplus10ainfiltration10aInverse problems10apeople count10asensor data10azone air parameters1 aLi, Han1 aHong, Tianzhen1 aSofos, Marina uhttps://simulationresearch.lbl.gov/publications/inverse-approach-solving-zone-air02294nas a2200265 4500008004100000022001300041245010900054210006900163260001200232300001400244490000800258520131500266653003101581653001701612653001501629653002401644653001901668653002201687100001501709700002001724700001901744700001401763700001801777856023301795 2018 eng d a1359431100aImpact of post-rainfall evaporation from porous roof tiles on building cooling load in subtropical China0 aImpact of postrainfall evaporation from porous roof tiles on bui c09/2018 a391 - 4000 v1423 a

Rainfall occurs frequently in subtropical regions of China, with the subsequent water evaporation from building roofs impacting the thermal performance and the energy consumption of buildings. We proposed a novel simulation method using actual meteorological data to evaluate this impact. New features were developed in EnergyPlus to enable the simulation: (1) an evaporation latent heat flux source term was added to the heat balance equation of the external surface and (2) algorithms for the evaporative cooling module (ECM) were developed and implemented into EnergyPlus. The ECM experimental results showed good agreement with the simulated results. The ECM was used to assess the impact of evaporation from porous roof tiles on the cooling load of a one-floor building in subtropical China. The results show that the evaporation process decreased the maximal values of the external and internal roof surface temperatures by up to 6.4 °C and 3.2 °C, respectively, while the lower internal surface temperature decreased the room accumulated cooling load by up to 14.8% during the hot summer period. The enhanced EnergyPlus capability can be used to evaluate the evaporative cooling performance of roofs with water-storage mediums, as well as to quantify their impact on building cooling loads.

10aBuilding energy simulation10acooling load10aenergyplus10aEvaporative Cooling10aRainfall event10aSubtropical China1 aZhang, Lei1 aZhang, Rongpeng1 aHong, Tianzhen1 aZhang, Yu1 aMeng, Qinglin uhttps://linkinghub.elsevier.com/retrieve/pii/S1359431117356107https://api.elsevier.com/content/article/PII:S1359431117356107?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S1359431117356107?httpAccept=text/plain02692nas 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-modeling02000nas a2200205 4500008004100000245008800041210006900129490000800198520130700206653002701513653002001540653002201560653002201582653002701604653002001631100002101651700001901672700001701691856008601708 2017 eng d00aIEA EBC Annex 53: Total Energy Use in Buildings – Analysis and Evaluation Methods0 aIEA EBC Annex 53 Total Energy Use in Buildings Analysis and Eval0 v1523 a

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

10aenergy data definition10aenergy modeling10aenergy monitoring10aoccupant behavior10aPerformance Evaluation10areal energy use1 aYoshino, Hiroshi1 aHong, Tianzhen1 aNord, Natasa uhttps://simulationresearch.lbl.gov/publications/iea-ebc-annex-53-total-energy-use02536nas a2200253 4500008004100000245008200041210006900123490000800192520173700200653002501937653002001962653001501982653002101997653003102018653002202049100001202071700001902083700001502102700002202117700001802139700002202157700001902179856008402198 2017 eng d00aIEA EBC Annex 66: Definition and simulation of occupant behavior in buildings0 aIEA EBC Annex 66 Definition and simulation of occupant behavior 0 v1563 a

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

10abuilding performance10aenergy modeling10aenergy use10aIEA EBC Annex 6610aInterdisciplinary approach10aoccupant behavior1 aYan, Da1 aHong, Tianzhen1 aDong, Bing1 aMahdavi, Ardeshir1 aD'Oca, Simona1 aGaetani, Isabella1 aFeng, Xiaohang uhttps://simulationresearch.lbl.gov/publications/iea-ebc-annex-66-definition-and00823nas a2200229 4500008004100000245011300041210006900154653002400223653002200247653002900269653002300298653002300321653002900344653002900373100001500402700002000417700001400437700001900451700001800470700001800488856008700506 2016 eng d00aThe Impact of Evaporation Process on Thermal Performance of Roofs - Model Development and Numerical Analysis0 aImpact of Evaporation Process on Thermal Performance of Roofs Mo10aEvaporative Cooling10amodel development10aNet zero energy building10aNumerical analysis10aPassive techniques10aPorous building material10aRoof thermal performance1 aZhang, Lei1 aZhang, Rongpeng1 aZhang, Yu1 aHong, Tianzhen1 aMeng, Qinglin1 aFeng, Yanshan uhttps://simulationresearch.lbl.gov/publications/impact-evaporation-process-thermal02209nas a2200181 4500008004100000245009800041210006900139520155000208653001901758653002401777653003101801653003301832653001401865100001501879700001901894700002701913856008701940 2016 eng d00aImproving the accuracy of energy baseline models for commercial buildings with occupancy data0 aImproving the accuracy of energy baseline models for commercial 3 a

More than 80% of energy is consumed during operation phase of a building’s life cycle, so energy efficiency retrofit for existing buildings is considered a promising way to reduce energy use in buildings. The investment strategies of retrofit depend on the ability to quantify energy savings by “measurement and verification” (M&V), which compares actual energy consumption to how much energy would have been used without retrofit (called the “baseline” of energy use). Although numerous models exist for predicting baseline of energy use, a critical limitation is that occupancy has not been included as a variable. However, occupancy rate is essential for energy consumption and was emphasized by previous studies. This study develops a new baseline model which is built upon the Lawrence Berkeley National Laboratory (LBNL) model but includes the use of building occupancy data. The study also proposes metrics to quantify the accuracy of prediction and the impacts of variables. However, the results show that including occupancy data does not significantly improve the accuracy of the baseline model, especially for HVAC load. The reasons are discussed further. In addition, sensitivity analysis is conducted to show the influence of parameters in baseline models. The results from this study can help us understand the influence of occupancy on energy use, improve energy baseline prediction by including the occupancy factor, reduce risks of M&V and facilitate investment strategies of energy efficiency retrofit.

10abaseline model10abuilding energy use10aEnergy Efficiency Retrofit10aMeasurement and verification10aoccupancy1 aLiang, Xin1 aHong, Tianzhen1 aShen, Geoffrey, Qiping uhttps://simulationresearch.lbl.gov/publications/improving-accuracy-energy-baseline02318nas 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-behavior02767nas a2200193 4500008003900000245008400039210006900123520210600192653002202298653002602320653002002346653003102366653002202397653002302419100001402442700001902456700001202475856008602487 2015 d00aAn Insight into Actual Energy Use and Its Drivers in High-Performance Buildings0 aInsight into Actual Energy Use and Its Drivers in HighPerformanc3 a

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

10aactual energy use10abuilding technologies10adriving factors10ahigh-performance buildings10aintegrated design10aperformance rating1 aLi, Cheng1 aHong, Tianzhen1 aYan, Da uhttps://simulationresearch.lbl.gov/publications/insight-actual-energy-use-and-its00559nas a2200181 4500008003900000245005300039210005300092100001900145700001400164700002500178700001200203700001400215700001400229700001500243700001500258700001700273856008700290 2014 d00aIntegrated Design for High Performance Buildings0 aIntegrated Design for High Performance Buildings1 aHong, Tianzhen1 aLi, Cheng1 aDiamond, Richard, C.1 aYan, Da1 aZhang, Qi1 aZhou, Xin1 aGuo, Siyue1 aSun, Kaiyu1 aWang, Jingyi uhttps://simulationresearch.lbl.gov/publications/integrated-design-high-performance01431nas a2200133 4500008003900000245005600039210005300095260001200148520099100160100001701151700001801168700002101186856009001207 2012 d00aAn Improved Simple Chilled Water Cooling Coil Model0 aImproved Simple Chilled Water Cooling Coil Model c08/20123 a

The accurate prediction of cooling and dehumidification coil performance is important in model-based fault detection and in the prediction of HVAC system energy consumption for support of both design and operations. It is frequently desirable to use a simple cooling coil model that does not require detailed specification of coil geometry and material properties. The approach adopted is to match the overall UA of the coil to the rating conditions and to estimate the air-side and water-side components of the UA using correlations developed by Holmes (1982). This approach requires some geometrical information about the coil and the paper investigates the sensitivity of the overall performance prediction to uncertainties in this information, including assuming a fixed ratio of air-side to water-side UA at the rating condition. Finally, simulation results from different coil models are compared, and experimental data are used to validate the improved cooling coil model.

1 aWang, Liping1 aHaves, Philip1 aBuhl, Walter, F. uhttps://simulationresearch.lbl.gov/publications/improved-simple-chilled-water-cooling02242nas a2200181 4500008004100000245007300041210006900114260003300183520159100216653003201807653002401839653002401863653003001887653001801917100001801935700001901953856008801972 2012 eng d00aAn In-Depth Analysis of Space Heating Energy Use in Office Buildings0 aInDepth Analysis of Space Heating Energy Use in Office Buildings aAsilomar, CAbACEEEc08/20123 a

Space heating represents the largest end use in the U.S. buildings and consumes more than 7 trillion Joules of site energy annually [USDOE]. Analyzing building space heating performance and identifying methods for saving energy are quite important. Hence, it is crucial to identify and evaluate key driving factors to space heating energy use to support the design and operation of low energy buildings.

In this study, the prototypical small and large-size office buildings of the USDOE commercial reference buildings, which comply with ASHRAE Standard 90.1-2004, are selected. Key design and operation factors were identified to evaluate their degrees of impact for space heating energy use. Simulation results demonstrate that some of the selected building design and operation parameters have more significant impacts on space heating energy use than others, on the other hand, good operation practice can save more space heating energy than raising design efficiency levels of an office building. Influence of weather data used in simulations on space heating energy is found to be significant. The simulated space heating energy use is further benchmarked against those from similar office buildings in two U.S. commercial buildings databases to better understand the discrepancies.

Simulated results from this study and space heating energy use collected from building databases can both vary in two potentially well overlapped wide ranges depending on details of building design and operation, not necessarily that simulation always under-predicts the reality.

10abuilding energy performance10abuilding simulation10asimulation research10asimulation research group10aspace heating1 aLin, Hung-Wen1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/depth-analysis-space-heating-energy00475nas 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-schemes00555nas a2200157 4500008004100000245009400041210006900135300001200204490000800216100001900224700001900243700001500262700001600277700001800293856008600311 2010 eng d00aImpacts of Static Pressure Reset on VAV System Air Leakage, Fan Power, and Thermal Energy0 aImpacts of Static Pressure Reset on VAV System Air Leakage Fan P a428-4360 v1161 aLiu, Mingsheng1 aFeng, Jingjuan1 aWang, Zhan1 aZheng, Keke1 aPang, Xiufeng uhttps://simulationresearch.lbl.gov/publications/impacts-static-pressure-reset-vav00464nas 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-dynamics01911nas a2200145 4500008004100000245011700041210006900158260003100227300001200258520137900270100001801649700002001667700001601687856006201703 2009 eng d00aAn implementation of co-simulation for performance prediction of innovative integrated HVAC systems in buildings0 aimplementation of cosimulation for performance prediction of inn aGlasgow, Scotlandc07/2009 a724-7313 aIntegrated performance simulation of buildings and heating, ventilation and air-conditioning (HVAC)systems can help reducing energy consumption and increasing level of occupant comfort. However, no singe building performance simulation (BPS) tool offers sufficient capabilities and flexibilities to accommodate the ever-increasing complexity and rapid innovations in building and system technologies. One way to alleviate this problem is to use co-simulation. The co-simulation approach represents a particular case of simulation scenario where at least two simulators solve coupled differential-algebraic systems of equations and exchange data that couples these equations during the time integration. This paper elaborates on issues important for co-simulation realization and discusses multiple possibilities to justify the particular approach implemented in a co-simulation prototype. The prototype is verified and validated against the results obtained from the traditional simulation approach. It is further used in a case study for the proof-of-concept, to demonstrate the applicability of the method and to highlight its benefits. Stability and accuracy of different coupling strategies are analyzed to give a guideline for the required coupling frequency. The paper concludes by defining requirements and recommendations for generic co-simulation implementations.1 aTrcka, Marija1 aWetter, Michael1 aHensen, Jan uhttp://www.ibpsa.org/proceedings/BS2009/BS09_0724_731.pdf02419nas 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-operation00431nas a2200097 4500008004100000245008800041210006900129260002900198100002300227856008300250 2008 eng d00aIFC BIM-based Methodology for Semi-Automated Building Energy Performance Simulation0 aIFC BIMbased Methodology for SemiAutomated Building Energy Perfo aSantiago, Chilec07/20081 aBazjanac, Vladimir uhttps://simulationresearch.lbl.gov/publications/ifc-bim-based-methodology-semi00568nas a2200145 4500008004100000245009800041210006900139260002800208100001900236700002100255700002000276700001800296700001800314856009000332 2008 eng d00aIntegrating the Specification, Acquisition and Processing of Building Performance Information0 aIntegrating the Specification Acquisition and Processing of Buil aBeijing, Chinac10/20081 aKeller, Martin1 aO'Donnell, James1 aMenzel, Karsten1 aKeane, Marcus1 aGökçe, Ufuk uhttps://simulationresearch.lbl.gov/publications/integrating-specification-acquisition00542nas a2200133 4500008004100000245014400041210006900185260001200254300001400266490000700280100001700287700002100304856008300325 2007 eng d00aThe impacts of facade and ventilation strategies on indoor thermal environment for a naturally ventilated residential building in Singapore0 aimpacts of facade and ventilation strategies on indoor thermal e c12/2007 a4006-40150 v421 aWang, Liping1 aWong, Nyuk, Hien uhttps://simulationresearch.lbl.gov/publications/impacts-facade-and-ventilation00519nas a2200133 4500008004100000245009500041210006900136260002500205100001400230700001900244700001500263700001800278856008900296 2007 eng d00aIntegrated Static Pressure Reset with Fan Air Flow Station in Dual-duct VAV System Control0 aIntegrated Static Pressure Reset with Fan Air Flow Station in Du aLong Beach, CAc20071 aWu, Lixia1 aLiu, Mingsheng1 aWang, Gang1 aPang, Xiufeng uhttps://simulationresearch.lbl.gov/publications/integrated-static-pressure-reset-fan00524nas a2200133 4500008004100000245012700041210006900168260000900237300001200246490000700258100001700265700002100282856008700303 2007 eng d00aInvestigation of the possibility of applying natural ventilation for thermal comfort in residential buildings in Singapore0 aInvestigation of the possibility of applying natural ventilation c2007 a190-1990 v501 aWang, Liping1 aWong, Nyuk, Hien uhttps://simulationresearch.lbl.gov/publications/investigation-possibility-applying00350nas a2200109 4500008004100000245002900041210002900070260002400099100002200123700002100145856007400166 2006 eng d00aIFC to CONTAM Translator0 aIFC to CONTAM Translator aBoston, MAc08/20061 aBasarkar, Mangesh1 aSwami, Muthasamy uhttps://simulationresearch.lbl.gov/publications/ifc-contam-translator00531nas a2200109 4500008004100000245017300041210006900214260001200283100001700295700002100312856008800333 2006 eng d00aThe impacts of facade designs: orientations, window to wall ratios and shading devices on indoor environment for naturally ventilated residential buildings in Singapore0 aimpacts of facade designs orientations window to wall ratios and c09/20061 aWang, Liping1 aWong, Nyuk, Hien uhttps://simulationresearch.lbl.gov/publications/impacts-facade-designs-orientations00445nas a2200109 4500008004100000245006700041210006700108260003200175100001900207700002400226856008500250 2006 eng d00aImplementation of an Earth Tube System Into EnergyPlus Program0 aImplementation of an Earth Tube System Into EnergyPlus Program aCambridge, MA, USAc08/20061 aLee, Kwang, Ho1 aStrand, Richard, K. uhttps://simulationresearch.lbl.gov/publications/implementation-earth-tube-system00496nas a2200109 4500008004100000245014400041210006900185260000900254100001700263700002100280856008500301 2005 eng d00aThe impacts of facade and ventilation strategies on indoor thermal environment for a naturally ventilated residential building in Singapore0 aimpacts of facade and ventilation strategies on indoor thermal e c20051 aWang, Liping1 aWong, Nyuk, Hien uhttps://simulationresearch.lbl.gov/publications/impacts-facade-and-ventilation-001819nas 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-simulation00501nas a2200121 4500008004100000050001700041245009700058210006900155260002500224100002300249700001800272856008900290 2004 eng d aLBNL/PUB-90700aIFC HVAC interface to EnergyPlus - A case of expanded interoperability for energy simulation0 aIFC HVAC interface to EnergyPlus A case of expanded interoperabi aBoulder, COc08/20041 aBazjanac, Vladimir1 aMaile, Tobias uhttps://simulationresearch.lbl.gov/publications/ifc-hvac-interface-energyplus-case-000480nas a2200109 4500008004100000245009600041210006900137260003600206100002300242700001800265856008700283 2004 eng d00aIFC HVAC Interface to EnergyPlus: A Case of Expanded Interoperability for Energy Simulation0 aIFC HVAC Interface to EnergyPlus A Case of Expanded Interoperabi aBoulder, Colorado, USAc08/20041 aBazjanac, Vladimir1 aMaile, Tobias uhttps://simulationresearch.lbl.gov/publications/ifc-hvac-interface-energyplus-case00479nas 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-hvac00488nas a2200121 4500008004100000050001700041245008400058210006900142260003600211490000600247100002300253856009000276 2003 eng d aLBNL/PUB-90800aImproving building energy performance simulation with software interoperability0 aImproving building energy performance simulation with software i aEindhoven, Netherlandsc08/20030 v11 aBazjanac, Vladimir uhttps://simulationresearch.lbl.gov/publications/improving-building-energy-performance02220nas 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-and01533nas a2200205 4500008004100000245005900041210005800100260000900158300001200167490000700179520095600186653000801142653002401150653000901174653002301183653000801206100001901214700001401233856008001247 1997 eng d00aIISABRE: An integrated building simulation environment0 aIISABRE An integrated building simulation environment c1997 a219-2240 v323 a

An integrated building simulation environment, IISABRE, is introduced. IISABRE consists of CABD, BTP and IISPAM. CABD is an AutoCAD-based building descriptor enabling users to draw a building and define information. Some design tools are built into CABD, and a STEP-based building database can be generated, which provides an open mechanism to share the building database with other programs. BTP is a program for the detailed dynamic simulation of building thermal performance. With a PC 486DX50 (8M RAM) running in MS-Windows 3.11, BTP needs about 40 minutes to calculate the annual hourly energy demand for a building with 20 zones. IISPAM is a knowledge-based system for translating the STEP-based building database into ASCII-based data files for BTP. IISABRE can be widely employed in the field of building environmental engineering in order to improve the energy efficiency of buildings and the thermal comfort of the indoor environment.

10abtp10abuilding simulation10adest10aenergy performance10agui1 aHong, Tianzhen1 aJiang, Yi uhttps://simulationresearch.lbl.gov/publications/iisabre-integrated-building00355nas a2200109 4500008004100000245003800041210003800079260000900117100001400126700001900140856008600159 1995 eng d00aIntegrated building design system0 aIntegrated building design system c19951 aJiang, Yi1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/integrated-building-design-system00561nas a2200145 4500008004100000245010900041210006900150490000800219100002300227700001900250700001800269700002100287700002000308856008700328 1994 eng d00aInvestigation of the Reliability of Building Emulators for Testing Energy Management and Control Systems0 aInvestigation of the Reliability of Building Emulators for Testi0 v1001 aPeitsman, Henk, C.1 aWang, Shengwei1 aHaves, Philip1 aKärki, Satu, H.1 aPark, Cheol, P. uhttps://simulationresearch.lbl.gov/publications/investigation-reliability-building00380nas a2200121 4500008004100000245003000041210003000071260003100101100001600132700001800148700001700166856007500183 1992 eng d00aImpacts of Climate Change0 aImpacts of Climate Change aSolihull, Englandc05/19921 aHulme, Mike1 aHaves, Philip1 aBoardman, B. uhttps://simulationresearch.lbl.gov/publications/impacts-climate-change00456nas a2200109 4500008004100000245007600041210006900117260002900186100002300215700001800238856009000256 1990 eng d00aThe Influence of Tuning on the Performance of a Building Control System0 aInfluence of Tuning on the Performance of a Building Control Sys aLiège, Belgiumc12/19901 aDexter, Arthur, L.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/influence-tuning-performance-building01406nas a2200193 4500008003900000024001800039245010700057210006900164260002400233520072900257653002500986653001901011653002401030653001601054653001701070100002001087700001601107856008901123 1985 d aEEB-BED-85-0500aImplications of Office Building Thermal Mass and Multi-day Temperature Profiles for Cooling Strategies0 aImplications of Office Building Thermal Mass and Multiday Temper aDenver, COc08/19853 a

This paper describes a study of the cooling energy requirements that result from thermal storage in building mass, and suggests methods for predicting and controlling its energy cost implications. The study relies on computer simulations of energy use for a large office building prototype in El Paso, TX using the DOE-2 building energy analysis program. Increased Monday cooling energy requirements resulting from the weekend shut-down of HVAC systems are documented. Predictors of energy use and peak demands, which account for thermal storage in building mass, are described. Load-shifting, sub-cooling and pre-cooling equipment operating strategies are evaluated with explicit reference to utility rate schedules.

10acommercial buildings10acooling energy10aenergy conservation10apeak demand10athermal mass1 aEto, Joseph, H.1 aPowell, Gay uhttps://simulationresearch.lbl.gov/publications/implications-office-building-thermal00486nas a2200121 4500008004100000245006100041210005700102260006900159100001800228700001800246700001600264856008400280 1985 eng d00aThe Integration of Graphic and Thermal Simulation Models0 aIntegration of Graphic and Thermal Simulation Models aPinner, Middlesex, UKbWembley, Online Publications Ltdc10/19851 aGreen, Cedric1 aHaves, Philip1 aHuddy, Paul uhttps://simulationresearch.lbl.gov/publications/integration-graphic-and-thermal