02298nas a2200277   4500008004100000022001300041245016000054210006900214260001600283300001600299490000800315520136500323653004401688653002401732653002301756653001801779653002201797653002301819100001201842700001701854700001501871700001901886700001301905700001501918856008701933       2019                        eng d  a0048969700aAssessment of occupant-behavior-based indoor air quality and its impacts on human exposure risk: A case study based on the wildfires in Northern California0 aAssessment of occupantbehaviorbased indoor air quality and its i  cJan-10-2019  a1251 - 12610 v6863 a<p>The recent wildfires in California, U.S., have caused not only significant losses to human life and property, but also serious environmental and health issues. Ambient air pollution from combustion during the fires could increase indoor exposure risks to toxic gases and particles, further exacerbating respiratory conditions. This work aims at addressing existing knowledge gaps in understanding how indoor air quality is affected by outdoor air pollutants during wildfires—by taking into account occupant behaviors (e.g., movement, operation of windows and air-conditioning) which strongly influence building performance and occupant comfort. A novel modeling framework was developed to simulate the indoor exposure risks considering the impact of occupant behaviours by integrating building energy and occupant behaviour modeling with computational fluid dynamics simulation. Occupant behaviors were found to exert significant impacts on indoor air flow patterns and pollutant concentrations, based on which, certain behaviors are recommended during wildfires. Further, the actual respiratory injury level under such outdoor conditions was predicted. The modeling framework and the findings enable a deeper understanding of the actual health impacts of wildfires, as well as informing strategies for mitigating occupant health risk during wildfires</p>10acomputational fluid dynamics siumlation10ahuman exposure risk10aindoor air quality10aNAPA wildfire10aoccupant behavior10arespiratory injury1 aLuo, Na1 aWeng, Wenguo1 aXu, Xiaoyu1 aHong, Tianzhen1 aFu, Ming1 aSun, Kaiyu  uhttps://simulationresearch.lbl.gov/publications/assessment-occupant-behavior-based00542nas a2200133   4500008004100000245013400041210006900175490000800244100001900252700002000271700001800291700001700309856008200326       2018                        eng d00aBidirectional low temperature district energy systems with agent-based control: Performance comparison and operation optimization0 aBidirectional low temperature district energy systems with agent0 v2091 aBunning, Felix1 aWetter, Michael1 aFuchs, Marcus1 aMuller, Dirk  uhttps://simulationresearch.lbl.gov/publications/bidirectional-low-temperature01861nas a2200241   4500008004100000245010500041210006900146260001200215300000900227490000700236520110600243653001701349653002101366653002701387653002201414653001101436100001701447700001601464700001401480700001901494700001701513856008901530       2018                        eng d00aPerformance-Based Evaluation of Courtyard Design in China’s Cold-Winter Hot-Summer Climate Regions0 aPerformanceBased Evaluation of Courtyard Design in China s ColdW  c10/2018  a39500 v103 a<p>Evaluates the performance of the traditional courtyard design of the Jiangnan Museum, located in Jiangsu Province. In the evaluation, the spatial layout of courtyards is adjusted, the aspect ratio is changed, and an ecological buffer space is created. To model and evaluate the performance of the courtyard design, this study applied the Computational fluid dynamics (CFD) software, Parabolic Hyperbolic Or Elliptic Numerical Integration Code Series (PHOENICS), for wind environment simulation, and the EnergyPlus-based software, DesignBuilder, for energy simulation. Results show that a good combination of courtyard layout and aspect ratio can improve the use of natural ventilation by increasing free cooling during hot summers and reducing cold wind in winters. The results also show that ecological buffer areas of a courtyard can reduce cooling loads in summer by approximately 19.6% and heating loads in winter by approximately 22.3%. The study provides insights into the optimal design of a courtyard to maximize its benefit in regulating the microclimate during both winter and summer.</p>10aaspect ratio10acourtyard design10aecological buffer area10aecological effect10alayout1 aXu, Xiaodong1 aLuo, Fenlan1 aWang, Wei1 aHong, Tianzhen1 aFu, Xiuzhang  uhttp://www.mdpi.com/2071-1050/10/11/3950http://www.mdpi.com/2071-1050/10/11/3950/pdf02289nas a2200217   4500008004100000245009600041210006900137490000800206520155700214100002601771700001801797700002701815700002201842700001801864700002301882700001701905700002901922700002001951700001801971856008201989       2017                        eng d00aDynamic equation-based thermo-hydraulic pipe model for district heating and cooling systems0 aDynamic equationbased thermohydraulic pipe model for district he0 v1513 a<p>Simulation and optimisation of district heating and cooling networks requires efficient and realistic models of the individual network elements in order to correctly represent heat losses or gains, temperature propagation and pressure drops. Due to more recent thermal networks incorporating meshing decentralised heat and cold sources, the system often has to deal with variable temperatures and mass flow rates, with flow reversal occurring more frequently. This paper presents the mathematical derivation and software implementation in Modelica of a thermo-hydraulic model for thermal networks that meets the above requirements and compares it to both experimental data and a commonly used model. Good correspondence between experimental data from a controlled test set-up and simulations using the presented model was found. Compared to measurement data from a real district heating network, the simulation results led to a larger error than in the controlled test set-up, but the general trend is still approximated closely and the model yields results similar to a pipe model from the Modelica Standard Library. However, the presented model simulates 1.7 (for low number of volumes) to 68 (for highly discretized pipes) times faster than a conventional model for a realistic test case. A working implementation of the presented model is made openly available within the IBPSA Modelica Library. The model is robust in the sense that grid size and time step do not need to be adapted to the flow rate, as is the case in finite volume models.</p>1 avan der Heijde, Brahm1 aFuchs, Marcus1 aTugores, Carles, Ribas1 aSchweiger, Gerald1 aSartor, Kevin1 aBasciotti, Daniele1 aMuller, Dirk1 aNytsch-Geusen, Christoph1 aWetter, Michael1 aHelsen, Lieve  uhttps://simulationresearch.lbl.gov/publications/dynamic-equation-based-thermo02536nas 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<p>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.</p>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-and03410nas 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<p>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:</p><ol><li>Energy Benchmarking providing an Energy Star score</li><li>Load Shape Analysis to identify potential building operation improvements</li><li>Preliminary Retrofit Analysis which uses a custom developed pre-simulated database</li><li>Detailed Retrofit Analysis which utilizes real time EnergyPlus simulations</li></ol><p>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.</p><p>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.</p><p>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.</p>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-efficiency00823nas 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-thermal01769nas a2200121   4500008003900000245010800039210006900147520127800216100002001494700001801514700003101532856008401563       2015                          d00aDesign choices for thermofluid flow components and systems that are exported as Functional Mockup Units0 aDesign choices for thermofluid flow components and systems that 3 a<p>This paper discusses design decisions for exporting Modelica thermofluid flow components as Functional Mockup Units. The purpose is to provide guidelines that will allow building energy simulation programs and HVAC equipment manufacturers to effectively use FMUs for modeling of HVAC components and systems. We provide an analysis for direct input-output dependencies of such components and discuss how these dependencies can lead to algebraic loops that are formed when connecting thermofluid flow components. Based on this analysis, we provide recommendations that increase the computing efficiency of such components and systems that are formed by connecting multiple components. We explain what code optimizations are lost when providing thermofluid flow components as FMUs rather than Modelica code. We present an implementation of a package for FMU export of such components, explain the rationale for selecting the connector variables of the FMUs and finally provide computing benchmarks for different design choices. It turns out that selecting temperature rather than specific enthalpy as input and output signals does not lead to a measurable increase in computing time, but selecting nine small FMUs rather than a large FMU increases computing time by 70%</p>1 aWetter, Michael1 aFuchs, Marcus1 aNouidui, Thierry, Stephane  uhttps://simulationresearch.lbl.gov/publications/design-choices-thermofluid-flow01728nas a2200265   4500008003900000245010500039210006900144260001200213300001200225490000800237520089300245653002401138653002201162653002001184653001501204653002201219100001201241700002101253700001901274700001901293700001701312700002301329700002201352856008801374       2015                          d00aOccupant Behavior Modeling for Building  Performance Simulation: Current State and Future Challenges0 aOccupant Behavior Modeling for Building Performance Simulation C  c11/2015  a264-2780 v1073 a<p>Occupant behavior is now widely recognized as a major contributing factor to uncertainty of building performance. While a surge of research on the topic has occurred over the past four decades, and particularly the past few years, there are many gaps in knowledge and limitations to current methodologies. This paper outlines the state-of-the-art research, current obstacles and future needs and directions for the following four-step iterative process: (1) occupant monitoring and data collection, (2) model development, (3) model evaluation, and (4) model implementation into building simulation tools. Major themes include the need for greater rigor in experimental methodologies; detailed, honest, and candid reporting of methods and results; and development of an efficient means to implement occupant behavior models and integrate them into building energy modeling programs.</p>10abuilding simulation10aenergy efficiency10aenergy modeling10aenergy use10aoccupant behavior1 aYan, Da1 aO'Brien, William1 aHong, Tianzhen1 aFeng, Xiaohang1 aGunay, Burak1 aTahmasebi, Farhang1 aMahdavi, Ardeshir  uhttps://simulationresearch.lbl.gov/publications/occupant-behavior-modeling-building01903nas a2200229   4500008003900000245004100039210004100080260001200121300001200133490000700145520126600152653002401418653001801442653001401460653002201474653002001496653002401516100001901540700001201559700001901571856008301590       2015                          d00aSimulation of Occupancy in Buildings0 aSimulation of Occupancy in Buildings  c01/2015  a348-3590 v873 a<p>Occupants are involved in a variety of activities in buildings, which drive them to move among rooms, enter or leave a building. In this study, occupancy is defined at four levels and varies with time: (1) the number of occupants in a building, (2) occupancy status of a space, (3) the number of occupants in a space, and (4) the space location of an occupant. Occupancy has a great influence on internal loads and ventilation requirement, thus building energy consumption. Based on a comprehensive review and comparison of literature on occupancy modeling, three representative occupancy models, corresponding to the levels 2–4, are selected and implemented in a software module. Main contributions of our study include: (1) new methods to classify occupancy models, (2) the review and selection of various levels of occupancy models, and (3) new methods to integrate these model into a tool that can be used in different ways for different applications and by different audiences. The software can simulate more detailed occupancy in buildings to improve the simulation of energy use, and better evaluate building technologies in buildings. The occupancy of an office building is simulated as an example to demonstrate the use of the software module.</p>10abuilding simulation10aco-simulation10aoccupancy10aoccupant behavior10asoftware module10astochastic modeling1 aFeng, Xiaohang1 aYan, Da1 aHong, Tianzhen  uhttps://simulationresearch.lbl.gov/publications/simulation-occupancy-buildings02234nas a2200313   4500008003900000245007900039210006900118260001200187300001200199490000700211520134300218653001401561653001501575653001801590653001501608653002401623653002901647653001501676653001301691100001701704700001901721700001301740700001401753700001301767700001501780700001501795700002201810856008801832       2014                          d00aComparison of Building Energy Use Data Between the United States and China0 aComparison of Building Energy Use Data Between the United States  c08/2014  a165-1750 v783 a<p>Buildings in the United States and China consumed 41% and 28% of the total primary energy in 2011, respectively. Good energy data are the cornerstone to understanding building energy performance and supporting research, design, operation, and policy making for low energy buildings. This paper presents initial outcomes from a joint research project under the U.S.–China Clean Energy Research Center for Building Energy Efficiency. The goal is to decode the driving forces behind the discrepancy of building energy use between the two countries; identify gaps and deficiencies of current building energy monitoring, data collection, and analysis; and create knowledge and tools to collect and analyze good building energy data to provide valuable and actionable information for key stakeholders. This paper first reviews and compares several popular existing building energy monitoring systems in both countries. Next a standard energy data model is presented. A detailed, measured building energy data comparison was conducted for a few office buildings in both countries. Finally issues of data collection, quality, sharing, and analysis methods are discussed. It was found that buildings in both countries performed very differently, had potential for deep energy retrofit, but that different efficiency measures should apply.</p>10abuildings10acomparison10adata analysis10adata model10aEnergy benchmarking10aenergy monitoring system10aenergy use10aretrofit1 aXia, Jianjun1 aHong, Tianzhen1 aShen, Qi1 aFeng, Wei1 aYang, Le1 aIm, Piljae1 aLu, Alison1 aBhandari, Mahabir  uhttps://simulationresearch.lbl.gov/publications/comparison-building-energy-use-data03531nas a2200241   4500008003900000245007900039210006900118260002200187300001100209490000800220520280200228653001403030653001503044653002403059653001503083653003103098653001303129100001903142700001303161700001603174700001403190856008503204       2014                          d00aData and Analytics to Inform Energy Retrofit of High Performance Buildings0 aData and Analytics to Inform Energy Retrofit of High Performance  bElsevierc08/2014  a90-1060 v1263 a<p>Buildings consume more than one-third of the world’s primary energy. Reducing energy use in buildings with energy efficient technologies is feasible and also driven by energy policies such as energy benchmarking, disclosure, rating, and labeling in both the developed and developing countries. Current energy retrofits focus on the existing building stocks, especially older buildings, but the growing number of new high performance buildings built around the world raises a question that how these buildings perform and whether there are retrofit opportunities to further reduce their energy use. This is a new and unique problem for the building industry. Traditional energy audit or analysis methods are inadequate to look deep into the energy use of the high performance buildings. This study aims to tackle this problem with a new holistic approach powered by building performance data and analytics. First, three types of measured data are introduced, including the time series energy use, building systems operating conditions, and indoor and outdoor environmental parameters. An energy data model based on the ISO Standard 12655 is used to represent the energy use in buildings in a three-level hierarchy. Secondly, a suite of analytics were proposed to analyze energy use and to identify retrofit measures for high performance buildings. The data-driven analytics are based on monitored data at short time intervals, and cover three levels of analysis – energy profiling, benchmarking and diagnostics. Thirdly, the analytics were applied to a high performance building in California to analyze its energy use and identify retrofit opportunities, including: (1) analyzing patterns of major energy end-use categories at various time scales, (2) benchmarking the whole building total energy use as well as major end-uses against its peers, (3) benchmarking the power usage effectiveness for the data center, which is the largest electricity consumer in this building, and (4) diagnosing HVAC equipment using detailed time-series operating data. Finally, a few energy efficiency measures were identified for retrofit, and their energy savings were estimated to be 20% of the whole-building electricity consumption. Based on the analyses, the building manager took a few steps to improve the operation of fans, chillers, and data centers, which will lead to actual energy savings. This study demonstrated that there are energy retrofit opportunities for high performance buildings and detailed measured building performance data and analytics can help identify and estimate energy savings and to inform the decision making during the retrofit process. Challenges of data collection and analytics were also discussed to shape best practice of retrofitting high performance buildings.</p>10aAnalytics10adata model10aEnergy benchmarking10aenergy use10aHigh performance buildings10aretrofit1 aHong, Tianzhen1 aYang, Le1 aHill, David1 aFeng, Wei  uhttps://simulationresearch.lbl.gov/publications/data-and-analytics-inform-energy03143nas a2200229   4500008003900000024002700039245009400066210006900160260004200229520232200271653003802593653003402631653000802665653003302673653002502706100001802731700002002749700002002769700002102789700002402810856007902834       2014                          d  aCEC‐500‐2015‐00100aDevelopment of Diagnostic and Measurement and Verification Tools for Commercial Buildings0 aDevelopment of Diagnostic and Measurement and Verification Tools  bCalifornia Energy Commissionc09/20143 a<p>This research developed new measurement and verification tools and new automated fault detection and diagnosis tools, and deployed them in the Universal Translator. The Universal Translator is a tool, developed by Pacific Gas and Electric, that manages large sets of measured data from building control systems and enables off‐line analysis of building performance. There were four technical projects following the program administration tasks identified as Project 1:</p><ol><li>Program Administration</li><li>Methods and Tools to Reduce the Cost of Measurement and Verification.</li><li>Fault Detection and Diagnostics for Commercial Heating, Ventilating, and Air‐ Conditioning Systems.</li><li>Test Procedures and Tools to Characterize Fan and Duct System Performance in Large Commercial Buildings.</li><li>Universal Translator Development: Integration of Application Programming Interface.</li></ol><p>Project 1 consisted of administrative tasks related to the project.</p><p>Project 2 addressed the need for less expensive measurement and verification tools to determine the costs and benefits of retrofits and retro‐commissioning at both the individual building level and the utility program level.</p><p>Project 3 extended previous work on fault detection and diagnosis to additional systems and subsystems, including dual duct heating, ventilating and air‐conditioning systems and fan‐coil terminal units.</p><p>Project 4 combined previous work on duct leakage and fan modeling to develop a performance assessment method for existing fan/duct systems that could also be used in the analysis of retrofit measures identified by the tools in Projects 2 and 3 using the EnergyPlus simulation program to help select the most cost‐effective package of improvements.</p><p>Some of the diagnostic methods and tools developed in projects 2 through 4 were incorporated in the Universal Translator via a new application programming interface that was specified, developed and tested in Project 5. Combined, these tools support analyses of energy savings produced by new construction commissioning, retro‐commissioning, improved routine operations and code compliance. The new application programming interface could also facilitate future development, testing and deployment of new diagnostic tools.</p>10aapplication programming interface10afault detection and diagnosis10aM&V10aMeasurement and verification10aUniversal Translator1 aHaves, Philip1 aWray, Craig, P.1 aJump, David, A.1 aVeronica, Daniel1 aFarley, Christopher  uhttps://simulationresearch.lbl.gov/publications/development-diagnostic-and05291nas a2200193   4500008003900000245004400039210004400083260001200127520474700139100001904886700001404905700001504919700001704934700001304951700001304964700001504977700002204992856008305014       2013                          d00aBuilding Energy Monitoring and Analysis0 aBuilding Energy Monitoring and Analysis  c06/20133 a<p>U.S. and China are the world's top two economics. Together they consumed one-third of the world's primary energy. It is an unprecedented opportunity and challenge for governments, researchers and industries in both countries to join together to address energy issues and global climate change. Such joint collaboration has huge potential in creating new jobs in energy technologies and services.</p><p>Buildings in the US and China consumed about 40% and 25% of the primary energy in both countries in 2010 respectively. Worldwide, the building sector is the largest contributor to the greenhouse gas emission. Better understanding and improving the energy performance of buildings is a critical step towards sustainable development and mitigation of global climate change.</p><p>This project aimed to develop a standard methodology for building energy data definition, collection, presentation, and analysis; apply the developed methods to a standardized energy monitoring platform, including hardware and software, to collect and analyze building energy use data; and compile offline statistical data and online real-time data in both countries for fully understanding the current status of building energy use. This helps decode the driving forces behind the discrepancy of building energy use between the two countries; identify gaps and deficiencies of current building energy monitoring, data collection, and analysis; and create knowledge and tools to collect and analyze good building energy data to provide valuable and actionable information for key stakeholders.</p><p>Key research findings were summarized as follows:</p><ul><li>Identified the need for a standard data model and platform to collect, process, analyze, and exchange building performance data due to different definitions of energy use and boundary, difficulty in exchanging data, and lack of current standards.</li><li>Compared energy monitoring systems to identify gaps, including iSagy, Pulse Energy, SkySpark, sMap, EPP, ION, and Metasys.</li><li>Contributed to develop a standard data model to represent energy use in buildings (ISO standard 12655 and a Chinese national standard)</li><li>Determined that buildings in the United States and China are very different in design, operation, maintenance, occupant behavior: U.S. buildings have more stringent comfort standards regarding temperature, ventilation, lighting, and hot-water use and therefore higher internal loads and operating hours, and China buildings having higher lighting energy use, seasonal HVAC operation, more operators, more use of natural ventilation, less outdoor ventilation air, and wider range of comfort temperature.</li><li>Completed data collection for six office buildings, one in UC Merced campus, one in Sacramento, one in Berkeley, one in George Tech campus, and two in Beijing.</li><li>Compiled a source book of 10 selected buildings in the United States and China with detailed descriptions of the buildings, data points, and monitoring systems, and containing energy analysis of each building and an energy benchmarking among all buildings.</li><li>Recognized limited availability of quality data, particularly with long periods of time-interval data, and general lack of value for good data and large datasets.</li><li>Compiled a building energy database, with detailed energy end use at 1-hour or 15-minute time interval, of six office buildings — four in the U.S. and two in China. The database is available to the public and is a valuable resource for building research.</li><li>Developed methods and used them in data analysis of building performance for the five buildings with adequate data, including energy benchmarking, profiling (daily, weekly, monthly), and diagnostics.</li><li>Recommended energy efficiency measures for building retrofit in both countries. U.S. buildings show more potential savings by reducing operation time, reducing plug-loads, expanding comfort temperature range, and turning off lights or equipment when not in use; while Chinese buildings can save energy by increasing lighting system efficiency, and improving envelope insulation and HVAC equipment efficiency.</li></ul><p>The research outputs from the project can help better understand energy performance of buildings, improve building operations to reduce energy waste and increase efficiency, identify retrofit opportunities for existing buildings, and provide guideline to improve the design of new buildings. The standardized energy monitoring and analysis platform as well as the collected real building data can also be used for other CERC projects that need building energy measurements, and be further linked to building energy benchmarking and rating/labeling systems.</p>1 aHong, Tianzhen1 aFeng, Wei1 aLu, Alison1 aXia, Jianjun1 aYang, Le1 aShen, Qi1 aIm, Piljae1 aBhandari, Mahabir  uhttps://simulationresearch.lbl.gov/publications/building-energy-monitoring-and02060nas a2200277   4500008003900000245009300039210006900132260001200201520117000213653001701383653001801400653001501418653003601433653002301469653001501492100001901507700002301526700001801549700003101567700001701598700001701615700002501632700001701657700002001674856008801694       2012                          d00aMonitoring-based HVAC Commissioning of an Existing Office Building for Energy Efficiency0 aMonitoringbased HVAC Commissioning of an Existing Office Buildin  c10/20123 a<p>The performance of Heating, Ventilation and Air Conditioning (HVAC) systems may fail to satisfy design expectations due to improper equipment installation, equipment degradation, sensor failures, or incorrect control sequences. Commissioning identifies and implements cost-effective operational and maintenance measures in buildings to bring them up to the design intent or optimum operation. An existing office building is used as a case study to demonstrate the process of commissioning. Building energy benchmarking tools are applied to evaluate the energy performance for screening opportunities at the whole building level. A large natural gas saving potential was indicated by the building benchmarking results. Faulty operations in the HVAC systems, such as improper operations of air-side economizers, simultaneous heating and cooling, and ineffective optimal start, were identified through trend data analyses and functional testing. The energy saving potential for each commissioning measure is quantified with a calibrated building simulation model. An actual energy saving of 10% was realized after the implementations of cost-effective measures.</p>10abenchmarking10acommissioning10aenergyplus10afault detection and diagnostics10afunctional testing10atrend data1 aEarni, Shankar1 aWoodworth, Spencer1 aPang, Xiufeng1 aHernandez-Maldonado, Jorge1 aYin, Rongxin1 aWang, Liping1 aGreenberg, Steve, E.1 aFiegel, John1 aRubalcava, Alma  uhttps://simulationresearch.lbl.gov/publications/monitoring-based-hvac-commissioning02363nas a2200241   4500008004100000245007600041210006900117260002600186520159800212653002401810653001401834653001001848653002401858653003101882653001801913653003001931100002101961700001401982700001301996700001902009700001402028856007902042       2012                        eng d00aA Retrofit Tool for Improving Energy Efficiency of Commercial Buildings0 aRetrofit Tool for Improving Energy Efficiency of Commercial Buil  aAsilomar, CAc08/20123 a<p>Existing buildings will dominate energy use in commercial buildings in the United States for three decades or longer and even in China for the about two decades. Retrofitting these buildings to improve energy efficiency and reduce energy use is thus critical to achieving the target of reducing energy use in the buildings sector. However there are few evaluation tools that can quickly identify and evaluate energy savings and cost effectiveness of energy conservation measures (ECMs) for retrofits, especially for buildings in China. This paper discusses methods used to develop such a tool and demonstrates an application of the tool for a retrofit analysis. The tool builds on a building performance database with pre-calculated energy consumption of ECMs for selected commercial prototype buildings using the EnergyPlus program. The tool allows users to evaluate individual ECMs or a package of ECMs. It covers building envelope, lighting and daylighting, HVAC, plug loads, service hot water, and renewable energy. The prototype building can be customized to represent an actual building with some limitations. Energy consumption from utility bills can be entered into the tool to compare and calibrate the energy use of the prototype building. The tool currently can evaluate energy savings and payback of ECMs for shopping malls in China. We have used the tool to assess energy and cost savings for retrofit of the prototype shopping mall in Shanghai. Future work on the tool will simplify its use and expand it to cover other commercial building types and other countries.&nbsp;</p>10abuilding simulation10abuildings10aChina10acommercial building10aenergy efficiency measures10aretrofit tool10asimulation research group1 aLevine, Mark, D.1 aFeng, Wei1 aKe, Jing1 aHong, Tianzhen1 aZhou, Nan  uhttp://aceee.org/files/proceedings/2012/data/papers/0193-000098.pdf#page=103910nas a2200229   4500008004100000245013300041210006900174260006100243300001200304490000600316520299300322653002403315653004003339653007503379653003403454653001903488653003403507653001003541100001903551700002203570856008803592       2010                        eng d00aAssessment of Energy Savings Potential from the Use of Demand Control Ventilation Systems in General Office Spaces in California0 aAssessment of Energy Savings Potential from the Use of Demand Co  aBerkeleybLawrence Berkeley National Laboratoryc06/2010  a117-1240 v33 a<p>Demand controlled ventilation (DCV) was evaluated for general office spaces in California. A medium size office building meeting the prescriptive requirements of the 2008 California building energy efficiency standards (CEC 2008) was assumed in the building energy simulations performed with the EnergyPlus program to calculate the DCV energy savings potential in five typical California climates. Three design occupancy densities and two minimum ventilation rates were used as model inputs to cover a broader range of design variations. The assumed values of minimum ventilation rates in offices without DCV, based on two different measurement methods, were 81 and 28 cfm per occupant. These rates are based on the co‐author's unpublished analyses of data from EPA's survey of 100 U.S. office buildings. These minimum ventilation rates exceed the 15 to 20 cfm per person required in most ventilation standards for offices. The cost effectiveness of applying DCV in general office spaces was estimated via a life cycle cost analyses that considered system costs and energy cost reductions.</p><p>The results of the energy modeling indicate that the energy savings potential of DCV is largest in the desert area of California (climate zone 14), followed by Mountains (climate zone 16), Central Valley (climate zone 12), North Coast (climate zone 3), and South Coast (climate zone 6).</p><p>The results of the life cycle cost analysis show DCV is cost effective for office spaces if the typical minimum ventilation rates without DCV is 81 cfm per person, except at the low design occupancy of 10 people per 1000 ft<sup>2</sup> in climate zones 3 and 6. At the low design occupancy of 10 people per 1000 ft<sup>2</sup>, the greatest DCV life cycle cost savings is a net present value (NPV) of $0.52/ft<sup>2</sup> in climate zone 14, followed by $0.32/ft<sup>2</sup> in climate zone 16 and $0.19/ft<sup>2</sup> in climate zone 12. At the medium design occupancy of 15 people per 1000 ft<sup>2</sup>, the DCV savings are higher with a NPV $0.93/ft<sup>2</sup> in climate zone 14, followed by $0.55/ft<sup>2</sup> in climate zone 16, $0.46/ft<sup>2</sup> in climate zone 12, $0.30/ft<sup>2</sup> in climate zone 3, $0.16/ft<sup>2</sup> in climate zone 3. At the high design occupancy of 20 people per 1000 ft<sup>2</sup>, the DCV savings are even higher with a NPV $1.37/ft<sup>2</sup> in climate zone 14, followed by $0.86/ft<sup>2</sup> in climate zone 16, $0.84/ft<sup>2</sup> in climate zone 3, $0.82/ft<sup>2</sup> in climate zone 12, and $0.65/ft<sup>2</sup> in climate zone 6.</p><p>DCV was not found to be cost effective if the typical minimum ventilation rate without DCV is 28 cfm per occupant, except at high design occupancy of 20 people per 1000 ft<sup>2</sup> in climate zones 14 and 16.</p><p>Until the large uncertainties about the base case ventilation rates in offices without DCV are reduced, the case for requiring DCV in general office spaces will be a weak case.</p>10abuilding simulation10acalifornia building energy standard10aCommercial Building Ventilation and Indoor Environmental Quality Group10ademand controlled ventilation10aenergy savings10aindoor environment department10aother1 aHong, Tianzhen1 aFisk, William, J.  uhttps://simulationresearch.lbl.gov/publications/assessment-energy-savings-potential00555nas 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-vav02004nas a2200301   4500008004100000022001400041245013400055210006900189260001200258300001100270490000700281520102100288653001501309653001801324653002601342653003601368653003201404653001501436653002401451100001401475700002401489700002201513700002001535700001601555700002101571700002101592856008901613       2009                        eng d  a1573-198700aAnisotropy invariant Reynolds stress model of turbulence (AIRSM) and its application on attached and separated wall-bounded flows0 aAnisotropy invariant Reynolds stress model of turbulence AIRSM a  c07/2009  a81-1030 v833 a<p>Numerical predictions with a differential Reynolds stress closure, which in its original formulation explicitly takes into account possible states of turbulence on the anisotropy-invariant map, are presented. Thus the influence of anisotropy of turbulence on the modeled terms in the governing equations for the Reynolds stresses is accounted for directly. The anisotropy invariant Reynolds stress model (AIRSM) is implemented and validated in different finite-volume codes. The standard wall-function approach is employed as initial step in order to predict simple and complex wall-bounded flows undergoing large separation. Despite the use of simple wall functions, the model performed satisfactory in predicting these flows. The predictions of the AIRSM were also compared with existing Reynolds stress models and it was found that the present model results in improved convergence compared with other models. Numerical issues involved in the implementation and application of the model are also addressed.</p>10aAnisotrpoy10aInvariant map10aReynolds stress model10aReynolds-averaged Navier-Stokes10aSeparated wall-bounded flow10aTurbulence10aTurbulence modeling1 aKumar, V.1 aFrohnapfel, Bettina1 aJovanović, Jovan1 aBreuer, Michael1 aZuo, Wangda1 aHadzić, Ibrahim1 aLechner, Richard  uhttps://simulationresearch.lbl.gov/publications/anisotropy-invariant-reynolds-stress02217nas 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<p>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.</p><p>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;</p><p>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.</p><p>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.</p>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=1300430nas a2200121   4500008004100000245004800041210004800089260002800137100001900165700001900184700001800203856008700221       2007                        eng d00aEconomizer Control Using Mixed Air Enthalpy0 aEconomizer Control Using Mixed Air Enthalpy  aSan Francisco, CAc20071 aFeng, Jingjuan1 aLiu, Mingsheng1 aPang, Xiufeng  uhttps://simulationresearch.lbl.gov/publications/economizer-control-using-mixed-air00475nas a2200121   4500008004100000245006500041210006500106260002800171100002300199700001800222700003100240856008200271       2007                        eng d00aUse of Simulation Tools for Managing Buildings Energy Demand0 aUse of Simulation Tools for Managing Buildings Energy Demand  aBeijing, Chinac09/20071 aHernandez, Alberto1 aNeto, Flávio1 aFiorelli, Augusto, Sanzovo  uhttps://simulationresearch.lbl.gov/publications/use-simulation-tools-managing00625nas 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-thermal00476nas a2200109   4500008004100000245009800041210006900139260003000208100002300238700002000261856008500281       2005                        eng d00aModeling Ground Source Heat Pump Systems in a Building Energy Simulation Program (EnergyPlus)0 aModeling Ground Source Heat Pump Systems in a Building Energy Si  aMontreal, canadac08/20051 aFisher, Daniel, E.1 aRees, Simon, J.  uhttps://simulationresearch.lbl.gov/publications/modeling-ground-source-heat-pump00460nas 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-update00527nas a2200133   4500008004100000245012500041210006900166300001200235490000700247100002100254700001800275700001500293856008500308       2003                        eng d00aResearch of ANN Internal Model Self-tuning Control Applied in Combustion Process Control of Heating Furnace in Oil Field0 aResearch of ANN Internal Model Selftuning Control Applied in Com  a108-1120 v341 aJiang, Yongcheng1 aPang, Xiufeng1 aFu, Shaobo  uhttps://simulationresearch.lbl.gov/publications/research-ann-internal-model-self01937nas a2200169   4500008004100000024001500041245006700056210006700123260002900190520137000219100002301589700002001612700001801632700001701650700001301667856008701680       2002                        eng d  aLBNL-5136500aHVAC Component Data Modeling Using Industry Foundation Classes0 aHVAC Component Data Modeling Using Industry Foundation Classes  aLiège, Belgiumc12/20023 a<p>The Industry Foundation Classes (IFC) object data model of buildings is being developed by the International Alliance for Interoperability (IAI). The aim is to support data sharing and exchange in the building and construction industry across the life-cycle of a building.</p><p>This paper describes a number of aspects of a major extension of the HVAC part of the IFC data model. First is the introduction of a more generic approach for handling HVAC components. This includes type information, which corresponds to catalog data, occurrence information, which defines item-specific attributes such as location and connectivity, and performance history information, which documents the actual performance of the component instance over time. Other IFC model enhancements include an extension of the connectivity model used to specify how components forming a system can be traversed and the introduction of time-based data streams.</p><p>This paper includes examples of models of particular types of HVAC components, such as boilers and actuators, with all attributes included in the definitions. The paper concludes by describing the on-going process of model testing, implementation and integration into the complete IFC model and how the model can be used by software developers to support interoperability between HVAC-oriented design and analysis tools.</p>1 aBazjanac, Vladimir1 aForester, James1 aHaves, Philip1 aSucic, Darko1 aXu, Peng  uhttps://simulationresearch.lbl.gov/publications/hvac-component-data-modeling-using00468nas a2200121   4500008004100000245007000041210006700111260001800178100002300196700002400219700001800243856008500261       1994                        eng d00aFault Detection in Air-Conditioning Systems Using A.I. Techniques0 aFault Detection in AirConditioning Systems Using AI Techniques  aYork, England1 aDexter, Arthur, L.1 aFargus, Richard, S.1 aHaves, Philip  uhttps://simulationresearch.lbl.gov/publications/fault-detection-air-conditioning00600nas a2200157   4500008004100000245008700041210006900128260002900197100002300226700002300249700002400272700001800296700002200314700002500336856008100361       1994                        eng d00aModel-Based Approaches to Fault Detection and Diagnosis in Air-Conditioning System0 aModelBased Approaches to Fault Detection and Diagnosis in AirCon  aLiège, Belgiumc12/19941 aBenouarets, Mourad1 aDexter, Arthur, L.1 aFargus, Richard, S.1 aHaves, Philip1 aSalsbury, Tim, I.1 aWright, Jonathan, A.  uhttps://simulationresearch.lbl.gov/publications/model-based-approaches-fault