02294nas a2200193 4500008004100000022001300041245005200054210005200106260001600158300001100174490000800185520173700193100001901930700001701949700001401966700001201980700002001992856008802012 2020 eng d a0360132300aTen questions on urban building energy modeling0 aTen questions on urban building energy modeling cJan-01-2020 a1065080 v1683 a

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

1 aHong, Tianzhen1 aChen, Yixing1 aLuo, Xuan1 aLuo, Na1 aLee, Sang, Hoon uhttps://simulationresearch.lbl.gov/publications/ten-questions-urban-building-energy02543nas a2200277 4500008004100000022001300041245018600054210006900240260001200309300001400321490000800335520142800343653002701771653002701798653001801825653003601843100001901879700002001898700002201918700002101940700002401961700001301985700001501998700001902013856023302032 2018 eng d a0306261900aTranslating climate change and heating system electrification impacts on building energy use to future greenhouse gas emissions and electric grid capacity requirements in California0 aTranslating climate change and heating system electrification im c09/2018 a522 - 5340 v2253 a

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

10aBuilding Energy Demand10aClimate Change Impacts10aelectric grid10aHeating Electrification Effects1 aTarroja, Brian1 aChiang, Felicia1 aAghaKouchak, Amir1 aSamuelsen, Scott1 aRaghavan, Shuba, V.1 aWei, Max1 aSun, Kaiyu1 aHong, Tianzhen uhttps://linkinghub.elsevier.com/retrieve/pii/S0306261918306962https://api.elsevier.com/content/article/PII:S0306261918306962?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0306261918306962?httpAccept=text/plain01971nas a2200193 4500008004100000245013400041210006900175520127100244653001201515653002001527653001701547653004101564653002201605100001401627700001201641700001901653700001601672856008901688 2017 eng d00aTemporal and spatial characteristics of the urban heat island in Beijing and the impact on building design and energy performance0 aTemporal and spatial characteristics of the urban heat island in3 a

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

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

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

 

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

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

10aChina10acode and standard10aenergy consumption10aenergy efficiency10aEnergy Use Intensity10aoutcome-based code1 aYan, Da1 aHong, Tianzhen1 aLi, Cheng1 aZhang, Qi1 aAn, Jingjing1 aHu, shan uhttps://simulationresearch.lbl.gov/publications/thorough-assessment-china%E2%80%99s-standard01862nas a2200133 4500008004100000245007800041210006900119260001200188520137400200100002501574700002001599700002201619856008701641 2016 eng d00aA Tale of Three District Energy Systems: Metrics and Future Opportunities0 aTale of Three District Energy Systems Metrics and Future Opportu c08/20173 a

Improving the sustainability of cities is crucial for meeting climate goals in the next several decades. One way this is being tackled is through innovation in district energy systems, which can take advantage of local resources and economies of scale to improve the performance of whole neighborhoods in ways infeasible for individual buildings. These systems vary in physical size, end use services, primary energy resources, and sophistication of control. They also vary enormously in their choice of optimization metrics while all under the umbrella-goal of improved sustainability.

This paper explores the implications of choice of metric on district energy systems using three case studies: Stanford University, the University of California at Merced, and the Richmond Bay campus of the University of California at Berkeley. They each have a centralized authority to implement large-scale projects quickly, while maintaining data records, which makes them relatively effective at achieving their respective goals. Comparing the systems using several common energy metrics reveals significant differences in relative system merit. Additionally, a novel bidirectional heating and cooling system is presented. This system is highly energy-efficient, and while more analysis is required, may be the basis of the next generation of district energy systems

1 aPass, Rebecca, Zarin1 aWetter, Michael1 aPiette, Mary, Ann uhttps://simulationresearch.lbl.gov/publications/tale-three-district-energy-systems02814nas a2200169 4500008003900000245008800039210006900127520221500196653002402411653002202435653001402457653002202471653001502493100002702508700001902535856009002554 2014 d00aA Technical Framework to Describe Occupant Behavior for Building Energy Simulations0 aTechnical Framework to Describe Occupant Behavior for Building E3 a

Green buildings that fail to meet expected design performance criteria indicate that technology alone does not guarantee high performance. Human influences are quite often simplified and ignored in the design, construction, and operation of buildings. Energy-conscious human behavior has been demonstrated to be a significant positive factor for improving the indoor environment while reducing the energy use of buildings. In our study we developed a new technical framework to describe energy-related human behavior in buildings. The energy-related behavior includes accounting for individuals and groups of occupants and their interactions with building energy services systems, appliances and facilities. The technical framework consists of four key components:

  1. the drivers behind energy-related occupant behavior, which are biological, societal, environmental, physical, and economical in nature
  2. the needs of the occupants are based on satisfying criteria that are either physical (e.g. thermal, visual and acoustic comfort) or non-physical (e.g. entertainment, privacy, and social reward)
  3. the actions that building occupants perform when their needs are not fulfilled
  4. the systems with which an occupant can interact to satisfy their needs

The technical framework aims to provide a standardized description of a complete set of human energy-related behaviors in the form of an XML schema. For each type of behavior (e.g., occupants opening/closing windows, switching on/off lights etc.) we identify a set of common behaviors based on a literature review, survey data, and our own field study and analysis. Stochastic models are adopted or developed for each type of behavior to enable the evaluation of the impact of human behavior on energy use in buildings, during either the design or operation phase. We will also demonstrate the use of the technical framework in assessing the impact of occupancy behavior on energy saving technologies. The technical framework presented is part of our human behavior research, a 5-year program under the U.S. - China Clean Energy Research Center for Building Energy Efficiency.

10abuilding simulation10aenergy efficiency10aframework10aoccupant behavior10aXML schema1 aTurner, William, J. N.1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/technical-framework-describe-occupant00650nas a2200181 4500008003900000245010300039210006900142260001200211100002100223700001800244700001500262700002100277700002100298700002100319700002100340700002300361856008400384 2013 d00aTransforming BIM to BEM: Generation of Building Geometry for the NASA Ames Sustainability Base BIM0 aTransforming BIM to BEM Generation of Building Geometry for the  c01/20131 aO'Donnell, James1 aMaile, Tobias1 aRose, Cody1 aMrazovic, Natasa1 aMorrissey, Elmer1 aRegnier, Cynthia1 aParrish, Kristen1 aBazjanac, Vladimir uhttps://simulationresearch.lbl.gov/publications/transforming-bim-bem-generation00472nas a2200109 4500008003900000245011000039210006900149100001900218700002700237700001400264856008400278 2013 d00aThe Two-Day CERC-BEE Forum on Building Integrated Design and Occupant Behavior: Presentations and Summary0 aTwoDay CERCBEE Forum on Building Integrated Design and Occupant 1 aHong, Tianzhen1 aTurner, William, J. N.1 aLi, Cheng uhttps://simulationresearch.lbl.gov/publications/two-day-cerc-bee-forum-building00537nas a2200145 4500008004100000022001500041245012400056210006900180260001500249100002100264700001700285700001900302700002100321856004900342 2009 eng d aLBNL-2819E00aTechnical Assistance to Beichuan Reconstruction: Creating and Designing Low- to Zero-carbon Communities in New Beichuan0 aTechnical Assistance to Beichuan Reconstruction Creating and Des bLBNLc20091 aXu, Tengfang, T.1 aWang, Chuang1 aHong, Tianzhen1 aLevine, Mark, D. uhttp://www.escholarship.org/uc/item/0vv4m1gb01592nas a2200169 4500008004100000245013000041210006900171490000900240520096100249100002201210700001801232700002701250700002101277700002101298700001801319856008501337 2009 eng d00aTowards a Very Low Energy Building Stock: Modeling the US Commercial Building Stock to Support Policy and Innovation Planning0 aTowards a Very Low Energy Building Stock Modeling the US Commerc0 v37:53 a

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

1 aCoffey, Brian, E.1 aBorgeson, Sam1 aSelkowitz, Stephen, E.1 aApte, Joshua, S.1 aMathew, Paul, A.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/towards-very-low-energy-building00454nas a2200121 4500008004100000245005700041210005700098260002900155100002100184700001900205700001800224856009000242 2006 eng d00aThermal Performance Simulation of an Atrium Building0 aThermal Performance Simulation of an Atrium Building aToronto, Canadac05/20061 aGöçer, Özgür1 aTavil, Aslihan1 aÖzkan, Ertan uhttps://simulationresearch.lbl.gov/publications/thermal-performance-simulation-atrium00472nas a2200109 4500008004100000245011500041210006900156260000900225100001700234700002100251856009000272 2005 eng d00aThermal analysis of climate environments based on weather data in Singapore for naturally ventilated buildings0 aThermal analysis of climate environments based on weather data i c20051 aWang, Liping1 aWong, Nyuk, Hien uhttps://simulationresearch.lbl.gov/publications/thermal-analysis-climate-environments00465nas a2200121 4500008004100000245007100041210006900112490000700181100002200188700002600210700002400236856008300260 2005 eng d00aThermal Conductivity of Nanoscale Colloidal Solutions (Nanofluids)0 aThermal Conductivity of Nanoscale Colloidal Solutions Nanofluids0 v941 aPrasher, Ravi, S.1 aBhattacharya, Prajesh1 aPhelan, Patrick, E. uhttps://simulationresearch.lbl.gov/publications/thermal-conductivity-nanoscale00977nas a2200133 4500008004100000245002400041210002300065260001700088520061600105100001900721700002200740700001900762856006200781 2005 eng d00aTwo DOE-2 functions0 aTwo DOE2 functions aCanadac20053 aThis paper presents two DOE-2 functions to expand the modeling capability of DOE-2.1E, a popular calculation engine for building energy simulations. The first function models sensible and total heat recovery between outside air and exhaust air, with optional evaporative precooling of exhaust air before the heat recovery. The existing heat recovery of DOE-2 only allows preheating outside air when exhaust air is more than 10°F warmer than outside air. The second function models distributed energy storage for direct expansion air conditioners which cannot be modeled by any existing system type of DOE-2.1E.1 aHong, Tianzhen1 aEley, Charles, N.1 aKolderup, Erik uhttp://www.ibpsa.org/proceedings/BS2005/BS05_0419_426.pdf00374nas a2200121 4500008004100000245002400041210002300065260003000088100001900118700002200137700001900159856007400178 2005 eng d00aTwo DOE-2 Functions0 aTwo DOE2 Functions aMontreal, Canadac08/20051 aHong, Tianzhen1 aEley, Charles, N.1 aKolderup, Erik uhttps://simulationresearch.lbl.gov/publications/two-doe-2-functions-000571nas a2200121 4500008004100000245014700041210006900188260004400257100002400301700001900325700001900344856008600363 2004 eng d00aTransferred Just on Paper? Why Doesn't the Reality of Transferring/Adapting Energy Efficiency Codes and Standards Come Close to the Potential?0 aTransferred Just on Paper Why Doesnt the Reality of Transferring aPacific Grove, California, USAc08/20041 aDeringer, Joseph, J1 aIyer, Maithili1 aHuang, Yu, Joe uhttps://simulationresearch.lbl.gov/publications/transferred-just-paper-why-doesnt00555nas a2200169 4500008003900000245004100039210004100080260003500121100002500156700002200181700002400203700002200227700002000249700002500269700003000294856006100324 1989 d00aThermal Energy Storage System Sizing0 aThermal Energy Storage System Sizing aVancouver, BC, Canadac01/19891 aDumortier, Dominique1 aKammerud, Ron, C.1 aBirdsall, Bruce, E.1 aAndersson, Brandt1 aEto, Joseph, H.1 aCarroll, William, L.1 aWinkelmann, Frederick, C. uhttp://www.ibpsa.org/proceedings/BS1989/BS89_357_362.pdf00424nas a2200109 4500008004100000245006200041210006200103260002900165100001800194700001700212856008500229 1988 eng d00aTowards an Environment for HVAC Control System Evaluation0 aTowards an Environment for HVAC Control System Evaluation aOstend, Belgiumc09/19881 aHaves, Philip1 aTrewella, D. uhttps://simulationresearch.lbl.gov/publications/towards-environment-hvac-control01170nas a2200133 4500008004100000245006500041210006100106260002500167490000800192520072500200100001500925700001800940856007800958 1980 eng d00aThe Thermal Benefits and Cost Effectiveness of Earth Berming0 aThermal Benefits and Cost Effectiveness of Earth Berming aAmherst, MAc10/19800 v5.13 a

A number of advantages are claimed for earth sheltered buildings; the earth provides both insulation and thermal storage and also serves to reduce infiltration and noise. This paper seeks to quantify the thermal advantages of both earth sheltering and perimeter insulation by comparing the simulated thermal performance of an earth sheltered house, a house with perimeter insulation and a house with neither. The fuel savings are then compared to the estimated construction costs to determine cost-effectiveness. The major saving from an earth sheltered building is obtained in colder climates where the effective elevation of the frost line due to the earth berms considerably reduces the cost of the foundation.

1 aSpeltz, J.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/thermal-benefits-and-cost