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

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

1 aChen, Chien-fei1 aHong, Tianzhen1 ade Rubens, Gerardo, Zarazua1 aYilmaz, Selin1 aBandurski, Karol1 aBélafi, Zsófia, Deme1 aDe Simone, Marilena1 aBavaresco, Mateus, Vinícius1 aWang, Yu1 aLiu, Pei-ling1 aBarthelmes, Verena, M.1 aAdams, Jacqueline1 aD'Oca, Simona1 aPrzybylski, Łukasz uhttps://simulationresearch.lbl.gov/publications/culture-conformity-and-carbon-multi01863nas a2200205 4500008004100000245009300041210006900134260000900203520116400212653002401376653001801400653003501418653001601453653002301469100002401492700001701516700001601533700001901549856008901568 2019 eng d00aComparison of MPC Formulations for Building Control under Commercial Time-of-Use Tariffs0 aComparison of MPC Formulations for Building Control under Commer c20193 a

Most medium and large commercial buildings in the U.S. are subject to complex electricity tariffs that combine both Time-of-Use (TOU) energy and demand charges. This study analyses the performances of different economic Model Predictive Control (MPC) formulations, from the standpoints of monthly bill reduction, load shifting, and peak demand reduction. Simulations are performed on many simplified commercial building models, with multiple TOU demand charges, and under various summer conditions. Results show that compared to energy-only MPC, the traditional method for dealing with demand charges significantly
reduces peak demand and owner bill, however, highlight a lack of load shifting capability. A proposed incremental approach
is presented, which better balances the bill components in the objective function. In the case study presented, this method
can improve monthly bill savings and increase load shifting during demand response events, while keeping a similarly low
peak demand, compared to traditional MPC methods taking into account demand charges.

10acommercial building10ademand charge10aModel predictive control (MPC)10apeak demand10atime-of-use tarrif1 aVan Cutsem, Olivier1 aKayal, Maher1 aBlum, David1 aPritoni, Marco uhttps://simulationresearch.lbl.gov/publications/comparison-mpc-formulations-building02297nas a2200241 4500008004100000022001300041245010400054210006900158260001200227300001400239490000800253520153900261653001601800653001801816653002401834653003301858653001901891653002301910100001401933700001901947700002201966856006701988 2019 eng d a0306261900aData fusion in predicting internal heat gains for office buildings through a deep learning approach0 aData fusion in predicting internal heat gains for office buildin c02/2019 a386 - 3980 v2403 a

Heating, Ventilation, and Air Conditioning (HVAC) is a major energy consumer in buildings. The predictive control has demonstrated a potential to reduce HVAC energy use. To facilitate predictive HVAC control, internal heat gains prediction is required. In this study, we applied Long Short-Term Memory Networks, a special form of deep neural network, to predict miscellaneous electric loads, lighting loads, occupant counts and internal heat gains in two United States office buildings. Compared with the predetermined schedules used in American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standard 90.1, the Long Short-Term Memory Networks method could reduce the prediction errors of internal heat gains from 12% to 8% in Building A, and from 26% to 16% in Building B. It was also found that for internal heat gains prediction, miscellaneous electric loads is a more important feature than occupant counts for two reasons. First, miscellaneous electric loads is the best proxy variable for internal heat gains, as it is the major component of and has the highest correlation coefficient with the internal heat gains. Second, miscellaneous electric loads contain valuable information to predict occupant count, while occupant count could not help improve miscellaneous electric loads prediction. These findings could help researchers and practitioners select the most relevant features to more accurately predict internal heat gains for the implementation of predictive HVAC control in buildings.

10adata fusion10adeep learning10aInternal heat gains10aMiscellaneous electric loads10aOccupant count10aPredictive control1 aWang, Zhe1 aHong, Tianzhen1 aPiette, Mary, Ann uhttps://linkinghub.elsevier.com/retrieve/pii/S030626191930363002420nas 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/S036013231930333602860nas a2200253 4500008004100000022001300041245017500054210006900229260001200298300001400310490000800324520184700332653002402179653000902203653002902212653002602241100001602267700001502283700001802298700002202316700002002338700001502358856023302373 2019 eng d a0306261900aPractical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems0 aPractical factors of envelope model setup and their effects on t c02/2019 a410 - 4250 v2363 a

Model predictive control (MPC) for buildings is attracting significant attention in research and industry due to its potential to address a number of challenges facing the building industry, including energy cost reduction, grid integration, and occupant connectivity. However, the strategy has not yet been implemented at any scale, largely due to the significant effort required to configure and calibrate the model used in the MPC controller. While many studies have focused on methods to expedite model configuration and improve model accuracy, few have studied the impact a wide range of factors have on the accuracy of the resulting model. In addition, few have continued on to analyze these factors' impact on MPC controller performance in terms of final operating costs. Therefore, this study first identifies the practical factors affecting model setup, specifically focusing on the thermal envelope. The seven that are identified are building design, model structure, model order, data set, data quality, identification algorithm and initial guesses, and software tool-chain. Then, through a large number of trials, it analyzes each factor's influence on model accuracy, focusing on grey-box models for a single zone building envelope. Finally, this study implements a subset of the models identified with these factor variations in heating, ventilating, and air conditioning MPC controllers, and tests them in simulation of a representative case that aims to optimally cool a single-zone building with time-varying electricity prices. It is found that a difference of up to 20% in cooling cost for the cases studied can occur between the best performing model and the worst performing model. The primary factors attributing to this were model structure and initial parameter guesses during parameter estimation of the model.

10abuilding simulation10ahvac10aModel predictive control10aSystem identification1 aBlum, David1 aArendt, K.1 aRivalin, Lisa1 aPiette, Mary, Ann1 aWetter, Michael1 aVeje, C.T. uhttps://linkinghub.elsevier.com/retrieve/pii/S0306261918318099https://api.elsevier.com/content/article/PII:S0306261918318099?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0306261918318099?httpAccept=text/plain02376nas a2200241 4500008004100000022001300041245008400054210006900138260001200207300001200219490000800231520164800239653001801887653003501905653001901940653001501959653001501974653002301989100001402012700001902026700002202045856006702067 2019 eng d a0360544200aPredicting plug loads with occupant count data through a deep learning approach0 aPredicting plug loads with occupant count data through a deep le c05/2019 a29 - 420 v1813 a

Predictive control has gained increasing attention for its ability to reduce energy consumption and improve occupant comfort in buildings. The plug loads prediction is a key component for the predictive building controls, as plug loads is a major source of internal heat gains in buildings. This study proposed a novel method to apply the Long-Short-Term-Memory (LSTM) Network, a special form of Recurrent Neural Network, to predict plug loads. The occupant count and the time have been confirmed to drive the plug load profile and thus selected as the features for the plug load prediction. The LSTM network was trained and tested with ground truth occupant count data collected from a real office building in Berkeley, California. Results from the LSTM network markedly improve the prediction accuracy compared with traditional linear regression methods and the classical Artificial Neural Network. 95% of 1-h predictions from LSTM network are within ±1 kW of the actual plug loads, given the average plug loads during the office hour is 8.6 kW. The CV(RMSE) of the predicted plug load is 11% for the next hour, and 20% for the next 8 h. Lastly, we compared four prediction approaches with the office building we monitored: LSTM vs. ARIMA, with occupant counts vs. without occupant counts. It was found, the prediction error of the LSTM approach is around 4% less than the ARIMA approach. Using occupant counts as an exogenous input could further reduce the prediction error by 5%–6%. The findings of this paper could shed light on the plug load prediction for building control optimizations such as model-predictive control.

10adeep learning10aLong short term memory network10aOccupant count10aPlug loads10aprediction10aPredictive control1 aWang, Zhe1 aHong, Tianzhen1 aPiette, Mary, Ann uhttps://linkinghub.elsevier.com/retrieve/pii/S036054421931020501980nas a2200277 4500008004100000022001300041245009800054210006900152260001200221300001400233490000800247520110300255653002201358653004301380653002401423653002601447653003401473653002101507653002001528100001401548700002201562700001601584700001601600700001901616856006701635 2019 eng d a0360132300aThe Squeaky wheel: Machine learning for anomaly detection in subjective thermal comfort votes0 aSqueaky wheel Machine learning for anomaly detection in subjecti c03/2019 a219 - 2270 v1513 a

Anomalous patterns in subjective votes can bias thermal comfort models built using data-driven approaches. A stochastic-based two-step framework to detect outliers in subjective thermal comfort data is proposed to address this problem. The anomaly detection technique involves defining similar conditions using a k-Nearest Neighbor (KNN) method and then quantifying the dissimilarity of the occupants' votes from their peers under similar thermal conditions through a Multivariate Gaussian approach. This framework is used to detect outliers in the ASHRAE Global Thermal Comfort Database I & II. The resulting anomaly-free dataset produced more robust comfort models avoiding dubious predictions. The proposed method has been proven to effectively distinguish outliers from inter-individual variabilities in thermal demand. The proposed anomaly detection framework could easily be applied to other applications with different variables or subjective metrics. Such a tool holds great promise for use in the development of occupancy responsive controls for automated building HVAC systems.

10aanomaly detection10aASHRAE global thermal comfort database10aK-nearest neighbors10aMultivariate Gaussian10aOccupancy responsive controls10aSubjective votes10athermal comfort1 aWang, Zhe1 aParkinson, Thomas1 aLi, Peixian1 aLin, Borong1 aHong, Tianzhen uhttps://linkinghub.elsevier.com/retrieve/pii/S036013231930086102513nas a2200241 4500008004100000245010500041210006900146490000800215520166100223653003101884653003301915653003201948653002201980653002102002653002502023100001802048700002502066700002402091700002702115700001902142700002602161856008402187 2018 eng d00aHuman-building interaction at work: Findings from an interdisciplinary cross-country survey in Italy0 aHumanbuilding interaction at work Findings from an interdiscipli0 v1323 a

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

10aHuman-building interaction10aindoor environmental comfort10ainterdisciplinary framework10aoccupant behavior10aoffice buildings10aquestionnaire survey1 aD'Oca, Simona1 aPisello, Anna, Laura1 aDe Simone, Marilena1 aBarthelmes, Verena, M.1 aHong, Tianzhen1 aCorgnati, Stefano, P. uhttps://simulationresearch.lbl.gov/publications/human-building-interaction-work02081nas a2200217 4500008004100000245010200041210006900143490000800212520134200220653002201562653002401584653003301608653001901641653002201660653002201682100002101704700001501725700001901740700002201759856008201781 2018 eng d00aQuantifying the benefits of a building retrofit using an integrated system approach: A case study0 aQuantifying the benefits of a building retrofit using an integra0 v1593 a

Building retrofits provide a large opportunity to significantly reduce energy consumption in the buildings sector. Traditional building retrofits focus on equipment upgrades, often at the end of equipment life or failure, and result in replacement with marginally improved similar technology and limited energy savings. The Integrated System (IS) retrofit approach enables much greater energy savings by leveraging interactive effects between end use systems, enabling downsized or lower energy technologies. This paper presents a case study in Hawaii quantifying the benefits of an IS retrofit approach compared to two traditional retrofit approaches: a Standard Practice of upgrading equipment to meet minimum code requirements, and an Improved Practice of upgrading equipment to a higher efficiency. The IS approach showed an energy savings of 84% over existing building energy use, much higher than the traditional approaches of 13% and 33%. The IS retrofit also demonstrated the greatest energy cost savings potential. While the degree of savings realized from the IS approach will vary by building and climate, these findings indicate that savings on the order of 50% and greater are not possible without an IS approach. It is therefore recommended that the IS approach be universally adopted to achieve deep energy savings.

10aBuilding retrofit10abuilding simulation10aEnergy conservation measures10aenergy savings10aintegrated design10aintegrated system1 aRegnier, Cynthia1 aSun, Kaiyu1 aHong, Tianzhen1 aPiette, Mary, Ann uhttps://simulationresearch.lbl.gov/publications/quantifying-benefits-building01796nas a2200157 4500008004100000245006900041210006800110260001200178520127200190100001601462700001801478700002001496700001601516700002401532856008201556 2018 eng d00aWhen Data Analytics Meet Site Operation: Benefits and Challenges0 aWhen Data Analytics Meet Site Operation Benefits and Challenges c08/20183 a

Demand for using data analytics for energy management in buildings is rising. Such analytics are required for advanced measurement and verification, commissioning, automated fault-detection and diagnosis, and optimal control. While novel analytics algorithms continue to be developed, bottlenecks and challenges arise when deploying them for demonstration, for a number of reasons that do not necessarily have to do with the algorithms themselves. It is important for developers of new technologies to be aware of the challenges and potential solutions during demonstration. Therefore, this paper describes a recent deployment of an automated, physical model-based, FDD and optimal control tool, highlighting its design and as-operated benefits that the tool provides. Furthermore, the paper presents challenges faced during deployment and testing along with solutions used to overcome these challenges. The challenges have been grouped into four categories: Data Management, Physical Model Development and Integration, Software Development and Deployment, and Operator Use. The paper concludes by discussing how challenges with this project generalize to common cases, how they could compare to other projects in their severity, and how they may be addressed.

1 aBlum, David1 aLin, Guanjing1 aSpears, Michael1 aPage, Janie1 aGranderson, Jessica uhttps://simulationresearch.lbl.gov/publications/when-data-analytics-meet-site02592nas a2200193 4500008004100000245013700041210006900178520187800247653002902125653001202154653003302166653001502199653002202214653001602236100001702252700001902269700002202288856008802310 2017 eng d00aAutomatic Generation and Simulation of Urban Building Energy Models Based on City Datasets for City-Scale Building Retrofit Analysis0 aAutomatic Generation and Simulation of Urban Building Energy Mod3 a

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

10aBuilding Energy Modeling10aCityBES10aEnergy conservation measures10aenergyplus10aRetrofit Analysis10aUrban Scale1 aChen, Yixing1 aHong, Tianzhen1 aPiette, Mary, Ann uhttps://simulationresearch.lbl.gov/publications/automatic-generation-and-simulation00991nas a2200157 4500008004100000245017100041210006900212260003100281520034200312100001900654700001800673700001800691700001700709700002200726856008500748 2017 eng d00aDevelopment of Automated Procedures to Generate Reference Building Models for ASHRAE Standard 90.1 and India’s Building Energy Code and Implementation in OpenStudio0 aDevelopment of Automated Procedures to Generate Reference Buildi aSan Francisco, CAc08/20173 a

This paper describes a software system for automatically generating a reference (baseline) building energy model from the proposed (as-designed) building energy model. This system is built using the OpenStudio Software Development Kit (SDK) and is designed to operate on building energy models in the OpenStudio file format. 

1 aParker, Andrew1 aHaves, Philip1 aJegi, Subhash1 aGarg, Vishal1 aRavache, Baptiste uhttps://simulationresearch.lbl.gov/publications/development-automated-procedures02489nas a2200205 4500008004100000245008600041210006900127520180800196653001702004653002002021653002102041653001702062653001502079653003202094100001402126700001902140700001702159700002202176856008502198 2017 eng d00aElectric Load Shape Benchmarking for Small- and Medium-Sized Commercial Buildings0 aElectric Load Shape Benchmarking for Small and MediumSized Comme3 a

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

10abenchmarking10aBuilding energy10acluster analysis10aload profile10aload shape10arepresentative load pattern1 aLuo, Xuan1 aHong, Tianzhen1 aChen, Yixing1 aPiette, Mary, Ann uhttps://simulationresearch.lbl.gov/publications/electric-load-shape-benchmarking03410nas a2200409 4500008004100000245008500041210006900126260001200195520213600207653000902343653002502352653002202377653002002399653001902419653002302438653003302461653003902494653003202533653001302565653002102578100002202599700001902621700002202640700002102662700002002683700001702703700002302720700002202743700002102765700002002786700002302806700001902829700001502848700002802863700002002891856008902911 2017 eng d00aSmall and Medium Building Efficiency Toolkit and Community Demonstration Program0 aSmall and Medium Building Efficiency Toolkit and Community Demon c03/20173 a

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

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

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

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

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

10aCBES10acommercial buildings10aenergy efficiency10aenergy modeling10aenergy savings10aindoor air quality10aindoor environmental quality10aoutdoor air measurement technology10aoutdoor airflow intake rate10aretrofit10aventilation rate1 aPiette, Mary, Ann1 aHong, Tianzhen1 aFisk, William, J.1 aBourassa, Norman1 aChan, Wanyu, R.1 aChen, Yixing1 aCheung, H.Y., Iris1 aHotchi, Toshifumi1 aKloss, Margarita1 aLee, Sang, Hoon1 aPrice, Phillip, N.1 aSchetrit, Oren1 aSun, Kaiyu1 aTaylor-Lange, Sarah, C.1 aZhang, Rongpeng uhttps://simulationresearch.lbl.gov/publications/small-and-medium-building-efficiency02318nas 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-behavior01862nas 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-systems02261nas a2200241 4500008003900000245011100039210006900150260001200219520144900231653002401680653003201704653002001736653001501756653003101771653001301802100002001815700001901835700002201854700001701876700001701893700002801910856008101938 2015 d00aAccelerating the energy retrofit of commercial buildings using a database of energy efficiency performance0 aAccelerating the energy retrofit of commercial buildings using a c07/20153 a

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

10abuilding simulation10aEnergy conservation measure10aenergy modeling10aenergyplus10aHigh Performance computing10aretrofit1 aLee, Sang, Hoon1 aHong, Tianzhen1 aPiette, Mary, Ann1 aSawaya, Geof1 aChen, Yixing1 aTaylor-Lange, Sarah, C. uhttps://simulationresearch.lbl.gov/publications/accelerating-energy-retrofit02732nas a2200265 4500008003900000245013300039210006900172260001200241300001200253490000600265520189500271653001302166653001002179653002202189653001202211653001202223100002102235700001802256700001902274700002102293700002902314700001802343700001502361856009002376 2015 d00aAssessment of the Potential to Achieve Very Low Energy Use in Public Buildings in China with Advanced Window and Shading Systems0 aAssessment of the Potential to Achieve Very Low Energy Use in Pu c05/2015 a668-6990 v53 a

As rapid growth in the construction industry continues to occur in China, the increased demand for a higher standard living is driving significant growth in energy use and demand across the country. Building codes and standards have been implemented to head off this trend, tightening prescriptive requirements for fenestration component measures using methods similar to the US model energy code American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) 90.1. The objective of this study is to (a) provide an overview of applicable code requirements and current efforts within China to enable characterization and comparison of window and shading products, and (b) quantify the load reduction and energy savings potential of several key advanced window and shading systems, given the divergent views on how space conditioning requirements will be met in the future.

System-level heating and cooling loads and energy use performance were evaluated for a code-compliant large office building using the EnergyPlus building energy simulation program. Commercially-available, highly-insulating, low-emittance windows were found to produce 24-66% lower perimeter zone HVAC electricity use compared to the mandated energy-efficiency standard in force (GB 50189-2005) in cold climates like Beijing. Low-e windows with operable exterior shading produced up to 30-80% reductions in perimeter zone HVAC electricity use in Beijing and 18-38% reductions in Shanghai compared to the standard. The economic context of China is unique since the cost of labor and materials for the building industry is so low. Broad deployment of these commercially available technologies with the proper supporting infrastructure for design, specification, and verification in the field would enable significant reductions in energy use and greenhouse gas emissions in the near term.

10abuilding10aChina10aenergy efficiency10ashading10awindows1 aLee, Eleanor, S.1 aPang, Xiufeng1 aMcNeil, Andrew1 aHoffmann, Sabine1 aThanachareonkit, Anothai1 aLi, Zhengrong1 aDing, Yong uhttps://simulationresearch.lbl.gov/publications/assessment-potential-achieve-very-low02814nas a2200349 4500008003900000245007400039210006900113260001100182490000800193520171300201653003701914653005101951653001402002653003202016653003202048653002602080653002202106653001502128653001502143653001302158653002002171653002402191100001902215700002202234700001702256700002002273700002802293700002002321700001502341700002302356856008502379 2015 d00aCommercial Building Energy Saver: An energy retrofit analysis toolkit0 aCommercial Building Energy Saver An energy retrofit analysis too c9/20150 v1593 a

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

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

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

1 aLee, Sang, Hoon1 aHong, Tianzhen1 aSawaya, Geof1 aChen, Yixing1 aPiette, Mary, Ann uhttps://simulationresearch.lbl.gov/publications/deep-database-energy-efficiency02128nas a2200241 4500008003900000245007200039210006900111260002700180300001400207490000700221520132000228653003001548653003301578653002301611653002201634653002901656653002701685100002001712700001901732700002201751700002801773856008501801 2015 d00aEnergy retrofit analysis toolkit for commercial buildings: A review0 aEnergy retrofit analysis toolkit for commercial buildings A revi bElsevier Ltd.c09/2015 a1087-11000 v893 a

Retrofit analysis toolkits can be used to optimize energy or cost savings from retrofit strategies, accelerating the adoption of ECMs (energy conservation measures) in buildings. This paper provides an up-todate review of the features and capabilities of 18 energy retrofit toolkits, including ECMs and the calculation engines. The fidelity of the calculation techniques, a driving component of retrofit toolkits, were evaluated. An evaluation of the issues that hinder effective retrofit analysis in terms of accessibility, usability, data requirement, and the application of efficiency measures, provides valuable insights into advancing the field forward. Following this review the general concepts were determined: (1) toolkits developed primarily in the private sector use empirically data-driven methods or benchmarking to provide ease of use, (2) almost all of the toolkits which used EnergyPlus or DOE-2 were freely accessible, but suffered from complexity, longer data input and simulation run time, (3) in general, there appeared to be a fine line between having too much detail resulting in a long analysis time or too little detail which sacrificed modeling fidelity. These insights provide an opportunity to enhance the design and development of existing and new retrofit toolkits in the future.

10aBuilding energy retrofit10aEnergy conservation measures10aEnergy efficiency10aEnergy simulation10aRetrofit analysis tools10aWeb-based applications1 aLee, Sang, Hoon1 aHong, Tianzhen1 aPiette, Mary, Ann1 aTaylor-Lange, Sarah, C. uhttps://simulationresearch.lbl.gov/publications/energy-retrofit-analysis-toolkit02052nas a2200217 4500008003900000245008100039210006900120260005100189520129700240100001601537700002401553700002001577700002501597700001801622700001901640700002101659700002201680700002101702700002701723856008401750 2015 d00aGreen, Clean, & Mean: Pushing the Energy Envelope in Tech Industry Buildings0 aGreen Clean Mean Pushing the Energy Envelope in Tech Industry Bu bLawrence Berkeley National Laboratoryc05/20153 a

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

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

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

1 aMills, Evan1 aGranderson, Jessica1 aChan, Wanyu, R.1 aDiamond, Richard, C.1 aHaves, Philip1 aNordman, Bruce1 aMathew, Paul, A.1 aPiette, Mary, Ann1 aRobinson, Gerald1 aSelkowitz, Stephen, E. uhttps://simulationresearch.lbl.gov/publications/green-clean-mean-pushing-energy00387nas a2200121 4500008003900000245003100039210003100070100002000101700001600121700003100137700001800168856007900186 2015 d00aModelica Buildings Library0 aModelica Buildings Library1 aWetter, Michael1 aZuo, Wangda1 aNouidui, Thierry, Stephane1 aPang, Xiufeng uhttps://simulationresearch.lbl.gov/publications/modelica-buildings-library02242nas a2200133 4500008003900000245007600039210006900115520175500184100001501939700001901954700002801973700002202001856008502023 2015 d00aA pattern-based automated approach to building energy model calibration0 apatternbased automated approach to building energy model calibra3 a

Building model calibration is critical in bringing simulated energy use closer to the actual consumption. This paper presents a novel, automated model calibration approach that uses logic linking parameter tuning with bias pattern recognition to overcome some of the disadvantages associated with traditional calibration processes. The pattern-based process contains four key steps: (1) running the original precalibrated energy model to obtain monthly simulated electricity and gas use; (2) establishing a pattern bias, either Universal or Seasonal Bias, by comparing load shape patterns of simulated and actual monthly energy use; (3) using programmed logic to select which parameter to tune first based on bias pattern, weather and input parameter interactions; and (4) automatically tuning the calibration parameters and checking the progress using pattern-fit criteria. The automated calibration algorithm was implemented in the Commercial Building Energy Saver, a web-based building energy retrofit analysis toolkit. The proof of success of the methodology was demonstrated using a case study of an office building located in San Francisco. The case study inputs included the monthly electricity bill, monthly gas bill, original building model and weather data with outputs resulting in a calibrated model that more closely matched that of the actual building energy use profile. The novelty of the developed calibration methodology lies in linking parameter tuning with the underlying logic associated with bias pattern identification. Although there are some limitations to this approach, the pattern-based automated calibration methodology can be universally adopted as an alternative to manual or hierarchical calibration approaches.

1 aSun, Kaiyu1 aHong, Tianzhen1 aTaylor-Lange, Sarah, C.1 aPiette, Mary, Ann uhttps://simulationresearch.lbl.gov/publications/pattern-based-automated-approach02607nas a2200169 4500008003900000245006200039210006000101520204900161100001902210700001802229700001902247700001702266700002302283700002002306700002102326856009002347 2014 d00aA New Model to Simulate Energy Performance of VRF Systems0 aNew Model to Simulate Energy Performance of VRF Systems3 a

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

1 aHong, Tianzhen1 aPang, Xiufeng1 aSchetrit, Oren1 aWang, Liping1 aKasahara, Shinichi1 aYura, Yoshinori1 aHinokuma, Ryohei uhttps://simulationresearch.lbl.gov/publications/new-model-simulate-energy-performance00390nas a2200109 4500008003900000245004500039210004500084100002000129700001900149700002200168856009000190 2014 d00aReview of Existing Energy Retrofit Tools0 aReview of Existing Energy Retrofit Tools1 aLee, Sang, Hoon1 aHong, Tianzhen1 aPiette, Mary, Ann uhttps://simulationresearch.lbl.gov/publications/review-existing-energy-retrofit-tools01865nas a2200145 4500008004100000245014400041210006900185260001200254490000700266520130200273100002101575700001801596700002101614856008401635 2014 eng d00aThe Role of International Partnerships in Delivering Low- Energy Building Design: A Case Study of the Singapore Scientific Planning Process0 aRole of International Partnerships in Delivering Low Energy Buil c05/20140 v143 a

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

1 aParrish, Kristen1 aSingh, Reshma1 aChien, Szu-Cheng uhttps://simulationresearch.lbl.gov/publications/role-international-partnerships00650nas 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-generation01987nas a2200241 4500008004100000022001400041245008500055210006900140260001200209300001200221490000700233520120100240653003901441653002501480653002001505653001501525653003401540100001801574700002001592700002601612700001801638856008901656 2012 eng d a0360-132300aA framework for simulation-based real-time whole building performance assessment0 aframework for simulationbased realtime whole building performanc c08/2012 a100-1080 v543 a

Most commercial buildings do not perform as well in practice as intended by the design and their performances often deteriorate over time. Reasons include faulty construction, malfunctioning equipment, incorrectly configured control systems and inappropriate operating procedures. One approach to addressing this problems is to compare the predictions of an energy simulation model of the building to the measured performance and analyze significant differences to infer the presence and location of faults. This paper presents a framework that allows a comparison of building actual performance and expected performance in real time. The realization of the framework utilized the EnergyPlus, the Building Controls Virtual Test Bed (BCVTB) and the Energy Management and Control System (EMCS) was developed. An EnergyPlus model that represents expected performance of a building runs in real time and reports the predicted building performance at each time step. The BCVTB is used as the software platform to acquire relevant inputs from the EMCS through a BACnet interface and send them to the EnergyPlus and to a database for archiving. A proof-of-concept demonstration is also presented.

10abuilding controls virtual test bed10abuilding performance10aenergy modeling10aenergyplus10areal-time building simulation1 aPang, Xiufeng1 aWetter, Michael1 aBhattacharya, Prajesh1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/framework-simulation-based-real-time02060nas a2200277 4500008003900000245009300039210006900132260001200201520117000213653001701383653001801400653001501418653003601433653002301469653001501492100001901507700002301526700001801549700003101567700001701598700001701615700002501632700001701657700002001674856008801694 2012 d00aMonitoring-based HVAC Commissioning of an Existing Office Building for Energy Efficiency0 aMonitoringbased HVAC Commissioning of an Existing Office Buildin c10/20123 a

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

10abenchmarking10acommissioning10aenergyplus10afault detection and diagnostics10afunctional testing10atrend data1 aEarni, Shankar1 aWoodworth, Spencer1 aPang, Xiufeng1 aHernandez-Maldonado, Jorge1 aYin, Rongxin1 aWang, Liping1 aGreenberg, Steve, E.1 aFiegel, John1 aRubalcava, Alma uhttps://simulationresearch.lbl.gov/publications/monitoring-based-hvac-commissioning00703nas a2200205 4500008003900000022001300039245012000052210006900172260001200241300001400253490000700267653002400274653001500298653002500313653001800338100001700356700002100373700001800394856008500412 2012 d a0378778800aUncertainties in energy consumption introduced by building operations and weather for a medium-size office building0 aUncertainties in energy consumption introduced by building opera c10/2012 a152 - 1580 v5310aBuilding Operations10aenergyplus10aMonte Carlo Analysis10aUncertainties1 aWang, Liping1 aMathew, Paul, A.1 aPang, Xiufeng uhttps://simulationresearch.lbl.gov/publications/uncertainties-energy-consumption01281nas a2200145 4500008004100000245008300041210006900124260002900193520073500222100003100957700002100988700001601009700002001025856009001045 2012 eng d00aValidation and Application of the Room Model of the Modelica Buildings Library0 aValidation and Application of the Room Model of the Modelica Bui aMunich, Germanyc09/20123 a

The Modelica Buildings library contains a package with a model for a thermal zone that computes heat transfer through the building envelope and within a room. It considers various heat transfer phenomena of a room, including conduction, convection, short-wave and long-wave radiation. The first part of this paper describes the physical phenomena considered in the room model. The second part validates the room model by using a standard test suite provided by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). The third part focuses on an application where the room model is used for simulation-based controls of a window shading device to reduce building energy consumption.

1 aNouidui, Thierry, Stephane1 aPhalak, Kaustubh1 aZuo, Wangda1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/validation-and-application-room-model01404nas a2200169 4500008003900000245008200039210006900121260001200190520081400202100001801016700002601034700003101060700002001091700001701111700001801128856008801146 2011 d00aBacNet and Analog/Digital Interfaces of the Building Controls Virtual Testbed0 aBacNet and AnalogDigital Interfaces of the Building Controls Vir c11/20113 a

This paper gives an overview of recent developments in the Building Controls Virtual Test Bed (BCVTB), a framework for co-simulation and hardware-in-the-loop.

First, a general overview of the BCVTB is presented. Second, we describe the BACnet interface, a link which has been implemented to couple BACnet devices to the BCVTB. We present a case study where the interface was used to couple a whole building simulation program to a building control system to assess in real-time the performance of a real building. Third, we present the ADInterfaceMCC, an analog/digital interface that allows a USB-based analog/digital converter to be linked to the BCVTB. In a case study, we show how the link was used to couple the analog/digital converter to a building simulation model for local loop control.

1 aHaves, Philip1 aBhattacharya, Prajesh1 aNouidui, Thierry, Stephane1 aWetter, Michael1 aLi, Zhengwei1 aPang, Xiufeng uhttps://simulationresearch.lbl.gov/publications/bacnet-and-analogdigital-interfaces00629nas a2200169 4500008004100000245008300041210006900124260003100193300001500224100003100239700002000270700001700290700001800307700002600325700001800351856009000369 2011 eng d00aBACnet and Analog/Digital Interfaces of the Building Controls Virtual Test Bed0 aBACnet and AnalogDigital Interfaces of the Building Controls Vir aSydney, Australiac11/2011 ap. 294-3011 aNouidui, Thierry, Stephane1 aWetter, Michael1 aLi, Zhengwei1 aPang, Xiufeng1 aBhattacharya, Prajesh1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/bacnet-and-analogdigital-interfaces-000768nas a2200241 4500008004100000245005700041210005700098260002300155653004300178653002400221653001500245653001100260653001200271653001300283653001800296653003000314100002200344700001800366700001800384700001700402700001900419856008800438 2011 eng d00aModeling and simulation of HVAC faults in EnergyPlus0 aModeling and simulation of HVAC faults in EnergyPlus aAustraliac11/201110aadvanced building software: energyplus10abuilding simulation10aenergyplus10afaults10afouling10amodeling10asensor offset10asimulation research group1 aBasarkar, Mangesh1 aHaves, Philip1 aPang, Xiufeng1 aWang, Liping1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/modeling-and-simulation-hvac-faults00466nas a2200133 4500008003900000245005800039210005800097260001200155100002200167700001800189700001700207700001900224856008900243 2011 d00aModeling and simulation of HVAC Results in EnergyPlus0 aModeling and simulation of HVAC Results in EnergyPlus c11/20111 aBasarkar, Mangesh1 aPang, Xiufeng1 aWang, Liping1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/modeling-and-simulation-hvac-results00504nas a2200121 4500008004100000245012700041210006900168300001200237490000800249100001800257700001900275856008800294 2011 eng d00aPrevention of Compressor Short Cycling in Direct-Expansion (DX) Rooftop Units, Part 1: Theoretical Analysis and Simulation0 aPrevention of Compressor Short Cycling in DirectExpansion DX Roo a666-6760 v1171 aPang, Xiufeng1 aLiu, Mingsheng uhttps://simulationresearch.lbl.gov/publications/prevention-compressor-short-cycling00492nas a2200121 4500008004100000245011300041210006900154300001200223490000800235100001800243700001900261856009000280 2011 eng d00aPrevention of Compressor Short Cycling in Direct-Expansion (DX) Rooftop Units— Part 2: Field Investigation0 aPrevention of Compressor Short Cycling in DirectExpansion DX Roo a677-6850 v1171 aPang, Xiufeng1 aLiu, Mingsheng uhttps://simulationresearch.lbl.gov/publications/prevention-compressor-short-cycling-001677nas a2200181 4500008004100000245010100041210006900142260003100211300001700242520102700259100001801286700002601304700001901330700001801349700002001367700001901387856008901406 2011 eng d00aReal-time Building Energy Simulation using EnergyPlus and the Building Controls Virtual Test Bed0 aRealtime Building Energy Simulation using EnergyPlus and the Bui aSydney, Australiac11/2011 ap. 2890-28963 a

Most commercial buildings do not perform as well in practice as intended by the design and their performances often deteriorate over time. Reasons include faulty construction, malfunctioning equipment, incorrectly configured control systems and inappropriate operating procedures (Haves et al., 2001, Lee et al., 2007). To address this problem, the paper presents a simulation-based whole building performance monitoring tool that allows a comparison of building actual performance and expected performance in real time. The tool continuously acquires relevant building model input variables from existing Energy Management and Control System (EMCS). It then reports expected energy consumption as simulated of EnergyPlus. The Building Control Virtual Test Bed (BCVTB) is used as the software platform to provide data linkage between the EMCS, an EnergyPlus model, and a database. This paper describes the integrated real-time simulation environment. A proof-of-concept demonstration is also presented in the paper.

1 aPang, Xiufeng1 aBhattacharya, Prajesh1 aO'Neill, Zheng1 aHaves, Philip1 aWetter, Michael1 aBailey, Trevor uhttps://simulationresearch.lbl.gov/publications/real-time-building-energy-simulation00569nas a2200157 4500008004100000245006300041210006300104260003000167100001900197700001900216700002700235700002600262700001800288700001800306856008700324 2010 eng d00aAutomated Continuous Commissioning of Commercial Buildings0 aAutomated Continuous Commissioning of Commercial Buildings aWashington, D.C.c11/20101 aBailey, Trevor1 aO'Neill, Zheng1 aShashanka, Madhusudana1 aBhattacharya, Prajesh1 aHaves, Philip1 aPang, Xiufeng uhttps://simulationresearch.lbl.gov/publications/automated-continuous-commissioning00555nas 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-vav02269nas a2200169 4500008004100000020001800041245011700059210006900176260004100245520162500286100002201911700002201933700001801955700002201973700001801995856008602013 2010 eng d a0-918249-60-000aSystems Approach to Energy Efficient Building Operation: Case Studies and Lessons Learned in a University Campus0 aSystems Approach to Energy Efficient Building Operation Case Stu aAsilomar, California, USAbOmnipress3 a

This paper reviews findings from research conducted at a university campus to develop a robust systems approach to monitor and continually optimize building energy performance. The field analysis, comprising three projects, included detailed monitoring, model-based analysis of system energy performance, and implementation of optimized control strategies for both district and building-scale systems. One project used models of the central cooling plant and campus building loads, and weather forecasts to analyze and optimize the energy performance of a district cooling system, comprising chillers, pumps and a thermal energy storage system. Fullscale implementation of policies devised with a model predictive control approach produced energy savings of about 5%, while demonstrating that the heuristic policies implemented by the operators were close to optimal during peak cooling season and loads. Research was also conducted to evaluate whole building monitoring and control methods. A second project performed in a campus building combined sub-metered end-use data, performance benchmarks, energy simulations and thermal load estimators to create a web-based energy performance visualization tool prototype. This tool provides actionable energy usage information to aid in facility operation and to enable performance improvement. In a third project, an alternative to demand controlled ventilation enabled by direct measurements of building occupancy levels was assessed. Simulations were used to show 5-15% reduction in building HVAC system energy usage when using estimates of actual occupancy levels.

1 aNarayanan, Satish1 aApte, Michael, G.1 aHaves, Philip1 aPiette, Mary, Ann1 aElliott, John uhttps://simulationresearch.lbl.gov/publications/systems-approach-energy-efficient02303nas a2200169 4500008004100000245008100041210006900122300001200191490000800203520174400211100001401955700001901969700001801988700001502006700002202021856009002043 2009 eng d00aCCLEP Reduces Energy Consumption by More than 50% for a Luxury Shopping Mall0 aCCLEP Reduces Energy Consumption by More than 50 for a Luxury Sh a492-5010 v1153 a

The Continuous Commissioning Leading Project (CCLEP) process is an ongoing process to apply system optimization theory and advanced technologies to commercial retrofit projects. It was developed by Liu et al (2006) through a U.S. Department of Energy grant to the University of Nebraska and the Omaha Public Power District (OPPD) for continuous commissioning applications in commercial retrofit projects. The CCLEP process, procedures and seven case study results have already been presented (Liu et al 2006).

CCLEP was applied to a luxury shopping mall and office building. The case study building has ten single fan dual-duct VAV AHUs, 123 dual-duct pneumatic controller pressure independent terminal boxes, and a central heating and cooling plant. Major retrofit efforts include upgrading pneumatic to DDC controls for all AHUs, installing main hot deck dampers, replacing the boiler, installing VFD on fans and pumps, and installing Fan Airflow Stations (FAS) and Pump Waterflow Stations (PWS). This paper presents the optimal control strategies, which include main hot deck damper control, supply fan control integrated with FAS, return fan control, optimal control for terminal boxes, chilled water temperature and chilled water pump speed control, hot water temperature and hot water pump control. The measured hourly utility data after CCLEP show that annual HVAC electricity consumption is reduced by 56% and gas use is reduced by 36%.

This paper demonstrates the energy savings and system performance improvement through retrofits and optimal system control. This paper will present the case study building information, CCLEP major retrofits, CCLEP optimal control strategies, CCLEP results and conclusions

1 aWu, Lixia1 aLiu, Mingsheng1 aPang, Xiufeng1 aWang, Gang1 aLewis, Thomas, G. uhttps://simulationresearch.lbl.gov/publications/cclep-reduces-energy-consumption-more02419nas 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-operation02217nas a2200217 4500008004100000020002200041245006700063210006400130250000700194260006500201490000700266520151600273653001301789100002101802700002301823700001801846700002001864700002301884700002001907856007201927 2009 eng d a978-91-7393-513-500aStandardization of thermo-fluid modeling in Modelica.Fluid 1.00 aStandardization of thermofluid modeling in ModelicaFluid 10 a13 aComo, ItalybLinköping University Electronic Pressc09/20090 v433 a

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

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

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

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

10amodelica1 aFranke, Rüdiger1 aCasella, Francesco1 aOtter, Martin1 aProelss, Katrin1 aSielemann, Michael1 aWetter, Michael uhttp://www.ep.liu.se/ecp_article/index.en.aspx?issue=043;article=1300473nas a2200097 4500008004100000245012400041210006900165260002800234100002500262856008800287 2007 eng d00aAdvanced Zone Simulation in EnergyPlus: Incorporation of Variable Properties and Phase Change Material (PCM) Capability0 aAdvanced Zone Simulation in EnergyPlus Incorporation of Variable aBeijing, Chinac09/20071 aPedersen, Curtis, O. uhttps://simulationresearch.lbl.gov/publications/advanced-zone-simulation-energyplus00430nas 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-air00503nas a2200121 4500008004100000245008100041210006900122260002800191100002500219700003100244700002000275856008600295 2007 eng d00aEnergy Index Evaluation of Buildings in Function of the External Temperature0 aEnergy Index Evaluation of Buildings in Function of the External aBeijing, Chinac09/20071 aPapa, Renata, Pietra1 aJota, Patricia, Romeiro da1 aAssis, Eleonora uhttps://simulationresearch.lbl.gov/publications/energy-index-evaluation-buildings00519nas 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-fan00483nas a2200121 4500008004100000245008300041210006900124260002800193100001900221700001800240700001900258856008400277 2007 eng d00aVAV System Optimization through Continuous Commissioning in an Office Building0 aVAV System Optimization through Continuous Commissioning in an O aSan Francisco, CAc20071 aCho, Young-Hum1 aPang, Xiufeng1 aLiu, Mingsheng uhttps://simulationresearch.lbl.gov/publications/vav-system-optimization-through00535nas a2200133 4500008004100000245011600041210006900157260003200226100001500258700002000273700001300293700001500306856008000321 2006 eng d00aThe Application of Building Energy Simulation and Calibration in Two High-Rise Commercial Buildings in Shanghai0 aApplication of Building Energy Simulation and Calibration in Two aCambridge, MA, USAc08/20061 aPan, Yiqun1 aHuang, Zhizhong1 aWu, Gang1 aChen, Chen uhttps://simulationresearch.lbl.gov/publications/application-building-energy00529nas a2200133 4500008004100000245010800041210006900149300001200218490000800230100002200238700002600260700002400286856008500310 2006 eng d00aBrownian Motion Based Convective- Conductive Model for the Effective Thermal Conductivity of Nanofluids0 aBrownian Motion Based Convective Conductive Model for the Effect a588-5950 v1281 aPrasher, Ravi, S.1 aBhattacharya, Prajesh1 aPhelan, Patrick, E. uhttps://simulationresearch.lbl.gov/publications/brownian-motion-based-convective00457nas a2200121 4500008004100000245006500041210006500106260002600171100001800197700001300215700001900228856008800247 2006 eng d00aCase Study of Continuous Commissioning in an Office Building0 aCase Study of Continuous Commissioning in an Office Building aShenzhen, Chinac20061 aPang, Xiufeng1 aZheng, B1 aLiu, Mingsheng uhttps://simulationresearch.lbl.gov/publications/case-study-continuous-commissioning02020nas a2200265 4500008004100000245010800041210006900149260001200218300001400230490000700244520118600251653003801437653002501475653002401500100002601524700001301550700001601563700001301579700001201592700002401604700002201628700002001650700001301670856007101683 2006 eng d00aCharacterization of the Temperature Oscillation Technique to Measure the Thermal Conductivity of Fluids0 aCharacterization of the Temperature Oscillation Technique to Mea c08/2006 a2950-29560 v493 a

The temperature oscillation technique to measure the thermal diffusivity of a fluid consists of filling a cylindrical volume with the fluid, applying an oscillating temperature boundary condition at the two ends of the cylinder, measuring the amplitude and phase of the temperature oscillation at any point inside the cylinder, and finally calculating the fluid thermal diffusivity from the amplitude and phase values of the temperature oscillations at the ends and at the point inside the cylinder. Although this experimental technique was introduced by Santucci and co-workers nearly two decades ago, its application is still limited, perhaps because of the perceived difficulties in obtaining accurate results. Here, we attempt to clarify this approach by first estimating the maximum size of the liquid’s cylindrical volume, performing a systematic series of experiments to find the allowable amplitude and frequency of the imposed temperature oscillations, and then validating our experimental setup and the characterization method by measuring the thermal conductivity of pure water at different temperatures and comparing our results with previously published work.

10aTemperature oscillation technique10aThermal conductivity10athermal diffusivity1 aBhattacharya, Prajesh1 aNara, S.1 aVijayan, P.1 aTang, T.1 aLai, W.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aSong, David, W.1 aWang, J. uhttp://www.sciencedirect.com/science/article/pii/S001793100600144X02020nas a2200265 4500008004100000245010800041210006900149260001200218300001400230490000700244520118600251653003801437653002501475653002401500100002601524700001301550700001601563700001301579700001201592700002401604700002201628700002001650700001301670856007101683 2006 eng d00aCharacterization of the Temperature Oscillation Technique to Measure the Thermal Conductivity of Fluids0 aCharacterization of the Temperature Oscillation Technique to Mea c08/2006 a2950-29560 v493 a

The temperature oscillation technique to measure the thermal diffusivity of a fluid consists of filling a cylindrical volume with the fluid, applying an oscillating temperature boundary condition at the two ends of the cylinder, measuring the amplitude and phase of the temperature oscillation at any point inside the cylinder, and finally calculating the fluid thermal diffusivity from the amplitude and phase values of the temperature oscillations at the ends and at the point inside the cylinder. Although this experimental technique was introduced by Santucci and co-workers nearly two decades ago, its application is still limited, perhaps because of the perceived difficulties in obtaining accurate results. Here, we attempt to clarify this approach by first estimating the maximum size of the liquid’s cylindrical volume, performing a systematic series of experiments to find the allowable amplitude and frequency of the imposed temperature oscillations, and then validating our experimental setup and the characterization method by measuring the thermal conductivity of pure water at different temperatures and comparing our results with previously published work.

10aTemperature oscillation technique10aThermal conductivity10athermal diffusivity1 aBhattacharya, Prajesh1 aNara, S.1 aVijayan, P.1 aTang, T.1 aLai, W.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aSong, David, W.1 aWang, J. uhttp://www.sciencedirect.com/science/article/pii/S001793100600144X00582nas a2200133 4500008004100000245012900041210006900170260003600239100002000275700002200295700002200317700002200339856008700361 2006 eng d00aDynamic Controls for Energy Efficiency and Demand Response: Framework Concepts and a New Construction Case Study in New York0 aDynamic Controls for Energy Efficiency and Demand Response Frame aPacific Grove, CA, USAc08/20061 aKiliccote, Sila1 aPiette, Mary, Ann1 aWatson, David, S.1 aHughes, Glenn, D. uhttps://simulationresearch.lbl.gov/publications/dynamic-controls-energy-efficiency00533nas a2200133 4500008004100000245010900041210006900150300001400219490000600233100002200239700002600261700002400287856008800311 2006 eng d00aEffect of Aggregation Kinetics on the Thermal Conductivity of Nanoscale Colloidal Solutions (Nanofluids)0 aEffect of Aggregation Kinetics on the Thermal Conductivity of Na a1529-15340 v61 aPrasher, Ravi, S.1 aBhattacharya, Prajesh1 aPhelan, Patrick, E. uhttps://simulationresearch.lbl.gov/publications/effect-aggregation-kinetics-thermal00490nas a2200121 4500008004100000245007500041210006900116260002500185100002200210700002600232700002400258856008600282 2006 eng d00aEffect of Coloidal Chemistry on the Thermal Conductivity of Nanofluids0 aEffect of Coloidal Chemistry on the Thermal Conductivity of Nano aChicago, ILc11/20061 aPrasher, Ravi, S.1 aBhattacharya, Prajesh1 aPhelan, Patrick, E. uhttps://simulationresearch.lbl.gov/publications/effect-coloidal-chemistry-thermal00533nas a2200121 4500008004100000245010300041210006900144260003200213100002700245700002400272700002600296856008900322 2006 eng d00aThe Energy Performance of the Cold-Formed Steel-Frame and Wood-Frame Houses Developed for Thailand0 aEnergy Performance of the ColdFormed SteelFrame and WoodFrame Ho aCambridge, MA, USAc08/20061 aMahattanataw, Prechaya1 aPuvanant, Charunpat1 aMongkolsawat, Darunee uhttps://simulationresearch.lbl.gov/publications/energy-performance-cold-formed-steel01265nas a2200169 4500008004100000245004200041210004200083260001200125300001200137490000600149520075900155100002200914700002600936700002400962700002200986856008701008 2006 eng d00aEnhanced Mass Transport in Nanofluids0 aEnhanced Mass Transport in Nanofluids c03/2006 a419-4230 v63 a

Thermal conductivity enhancement in nanofluids, which are liquids containing suspended nanoparticles, has been attributed to localized convection arising from the nanoparticles' Brownian motion. Because convection and mass transfer are similar processes, the objective here is to visualize dye diffusion in nanofluids. It is observed that dye diffuses faster in nanofluids compared to that in water, with a peak enhancement at a nanoparticle volume fraction, φ, of 0.5%. A possible change in the slope of thermal conductivity enhancement at that same φ signifies that convection becomes less important at higher φ. The enhanced mass transfer in nanofluids can be utilized to improve diffusion in microfluidic devices.

1 aKrishnamurthy, S.1 aBhattacharya, Prajesh1 aPhelan, Patrick, E.1 aPrasher, Ravi, S. uhttps://simulationresearch.lbl.gov/publications/enhanced-mass-transport-nanofluids02402nas a2200145 4500008004100000022001400041245010900055210006900164300001500233490000700248520187300255100001802128700002002146856009002166 2006 eng d a1052-623400aPrecision control for generalized pattern search algorithms with adaptive precision function evaluations0 aPrecision control for generalized pattern search algorithms with a650-669
0 v163 a

In the literature on generalized pattern search algorithms, convergence to a stationary point of a once continuously differentiable cost function is established under the assumption that the cost function can be evaluated exactly. However, there is a large class of engineering problems where the numerical evaluation of the cost function involves the solution of systems of differential algebraic equations. Since the termination criteria of the numerical solvers often depend on the design parameters, computer code for solving these systems usually defines a numerical approximation to the cost function that is discontinuous with respect to the design parameters. Standard generalized pattern search algorithms have been applied heuristically to such problems, but no convergence properties have been stated. In this paper we extend a class of generalized pattern search algorithms to include a subprocedure that adaptively controls the precision of the approximating cost functions. The numerical approximations to the cost function need not define a continuous function. Our algorithms can be used for solving linearly constrained problems with cost functions that are at least locally Lipschitz continuous. Assuming that the cost function is smooth, we prove that our algorithms converge to a stationary point. Under the weaker assumption that the cost function is only locally Lipschitz continuous, we show that our algorithms converge to points at which the Clarke generalized directional derivatives are nonnegative in predefined directions. An important feature of our adaptive precision scheme is the use of coarse approximations in the early iterations, with the approximation precision controlled by a test. We show by numerical experiments that such an approach leads to substantial time savings in minimizing computationally expensive functions.

1 aPolak, Elijah1 aWetter, Michael uhttps://simulationresearch.lbl.gov/publications/precision-control-generalized-pattern00504nas a2200133 4500008004100000245006100041210006000102260003600162100002500198700001800223700001800241700002300259856008800282 2006 eng d00aZero Energy Buildings: A Critical Look at the Definition0 aZero Energy Buildings A Critical Look at the Definition aPacific Grove, CA, USAc08/20061 aTorcellini, Paul, A.1 aPless, Shanti1 aDeru, Michael1 aCrawley, Drury, B. uhttps://simulationresearch.lbl.gov/publications/zero-energy-buildings-critical-look01926nas a2200133 4500008004100000245011300041210006900154300001200223490000700235520142300242100002001665700001801685856008901703 2005 eng d00aBuilding design optimization using a convergent pattern search algorithm with adaptive precision simulations0 aBuilding design optimization using a convergent pattern search a a603-6120 v373 a

We propose a simulation–precision control algorithm that can be used with a family of derivative free optimization algorithms to solve optimization problems in which the cost function is defined through the solutions of a coupled system of differential algebraic equations (DAEs). Our optimization algorithms use coarse precision approximations to the solutions of the DAE system in the early iterations and progressively increase the precision as the optimization approaches a solution. Such schemes often yield a significant reduction in computation time. We assume that the cost function is smooth but that it can only be approximated numerically by approximating cost functions that are discontinuous in the design parameters. We show that this situation is typical for many building energy optimization problems.We present a new building energy and daylighting simulation program, which constructs approximations to the cost function that converge uniformly on bounded sets to a smooth function as precision is increased.We prove that for our simulation program, our optimization algorithms construct sequences of iterates with stationary accumulation points. We present numerical experiments in which we minimize the annual energy consumption of an office building for lighting, cooling and heating. In these examples, our precision control algorithm reduces the computation time up to a factor of four.

1 aWetter, Michael1 aPolak, Elijah uhttps://simulationresearch.lbl.gov/publications/building-design-optimization-using-000455nas a2200121 4500008004100000245006400041210006400105260002500169100001800194700001300212700001900225856008900244 2005 eng d00aBuilding Pressure Control in VAV System with Relief Air Fan0 aBuilding Pressure Control in VAV System with Relief Air Fan aPittsburgh, PAc20051 aPang, Xiufeng1 aZheng, B1 aLiu, Mingsheng uhttps://simulationresearch.lbl.gov/publications/building-pressure-control-vav-system00481nas a2200121 4500008004100000245006800041210006800109260002500177100002600202700002400228700002200252856008500274 2005 eng d00aComputational Analysis of the Colloidal Stability of Nanofluids0 aComputational Analysis of the Colloidal Stability of Nanofluids aOrlando, FLc11/20051 aBhattacharya, Prajesh1 aPhelan, Patrick, E.1 aPrasher, Ravi, S. uhttps://simulationresearch.lbl.gov/publications/computational-analysis-colloidal00424nas a2200121 4500008004100000245005100041210005100092260002500143100001300168700001900181700001800200856008400218 2005 eng d00aContinuous Commissioning of an Office Building0 aContinuous Commissioning of an Office Building aPittsburgh, PAc20051 aZheng, B1 aLiu, Mingsheng1 aPang, Xiufeng uhttps://simulationresearch.lbl.gov/publications/continuous-commissioning-office00634nas a2200181 4500008004100000245008100041210006900122260003100191100001600222700002600238700001300264700001200277700002400289700002200313700002000335700001300355856008400368 2005 eng d00aEffect of Particle Material on the Static Thermal Conductivity of Nanofluids0 aEffect of Particle Material on the Static Thermal Conductivity o aSan Francisco, CAc07/20051 aVijayan, P.1 aBhattacharya, Prajesh1 aNara, S.1 aLai, W.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aSong, David, W.1 aWang, J. uhttps://simulationresearch.lbl.gov/publications/effect-particle-material-static00624nas a2200157 4500008004100000245013600041210007000177260001200247490000700259100002100266700001700287700002600304700002600330700001700356856009300373 2005 eng d00aEffects of double glazed façade on energy consumption, thermal comfort and condensation for a typical office building in Singapore0 aEffects of double glazed façade on energy consumption thermal co c06/20050 v371 aWong, Nyuk, Hien1 aWang, Liping1 aChandra, Aida, Noplie1 aPandey, Anupama, Rana1 aWei, Xiaolin uhttps://simulationresearch.lbl.gov/publications/effects-double-glazed-fa%C3%A7ade-energy01543nas a2200217 4500008004100000020001800041245013200059210006900191260003100260520078000291100001301071700002601084700001601110700001201126700001801138700002401156700002201180700002001202700001701222856008601239 2005 eng d a0-7918-4221-500aExperimental Determination of the Effect of Varying Base Fluid and Temperature on the Static Thermal Conductivity of Nanofluids0 aExperimental Determination of the Effect of Varying Base Fluid a aOrlando, FLbASMEc11/20053 a

The heat transfer abilities of fluids can be improved by adding small particles of sizes of the order of nanometers. Recently a lot of research has been done in evaluating the thermal conductivity of nanofluids using various nanoparticles. In our present work we address this issue by conducting a series of experiments to determine the effective thermal conductivity of alumina-nanofluids by varying the base fluid with water and antifreeze liquids like ethylene glycol and propylene glycol. Temperature oscillation method is used to find the thermal conductivity of the nanofluid. The results show the thermal conductivity enhancement of nanofluids depends on viscosity of the base fluid. Finally the results are validated with a recently proposed theoretical model.

1 aNara, S.1 aBhattacharya, Prajesh1 aVijayan, P.1 aLai, W.1 aRosenthal, W.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aSong, David, W.1 aWang, Jinlin uhttps://simulationresearch.lbl.gov/publications/experimental-determination-effect00465nas 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-nanoscale00476nas a2200121 4500008004100000245008500041210006900126260002200195100001300217700001800230700001900248856008700267 2005 eng d00aUsing a Fan Air Flow Station to Control Building Static Pressure in a VAV System0 aUsing a Fan Air Flow Station to Control Building Static Pressure aOrlando, FLc20051 aZheng, B1 aPang, Xiufeng1 aLiu, Mingsheng uhttps://simulationresearch.lbl.gov/publications/using-fan-air-flow-station-control00453nas a2200121 4500008004100000245007400041210006900115300001200184490000700196100002100203700001800224856008900242 2004 eng d00aANN Modeling and Self-tuning Control of the Oil Field Heating Furnace0 aANN Modeling and Selftuning Control of the Oil Field Heating Fur a338-2400 v121 aJiang, Yongcheng1 aPang, Xiufeng uhttps://simulationresearch.lbl.gov/publications/ann-modeling-and-self-tuning-control01573nas a2200217 4500008004100000245009500041210006900136260001200205300001600217490000700233520084100240653001901081653002101100653004601121100002601167700001501193700002001208700002401228700002201252856008101274 2004 eng d00aBrownian Dynamics Simulation to Determine the Effective Thermal Conductivity of Nanofluids0 aBrownian Dynamics Simulation to Determine the Effective Thermal  c06/2004 a6492–64940 v953 a

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

10acomplex fluids10aDisperse systems10aThermal conduction in nonmetallic liquids1 aBhattacharya, Prajesh1 aSaha, S.K.1 aYadav, Ajay, K.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S. uhttps://simulationresearch.lbl.gov/publications/brownian-dynamics-simulation00513nas a2200133 4500008004100000245011300041210006900154260001200223300001200235490000700247100002000254700001800274856008700292 2004 eng d00aBuilding Design Optimization Using a Convergent Pattern Search Algorithm with Adaptive Precision Simulations0 aBuilding Design Optimization Using a Convergent Pattern Search A c09/2004 a603-6120 v371 aWetter, Michael1 aPolak, Elijah uhttps://simulationresearch.lbl.gov/publications/building-design-optimization-using00369nas a2200121 4500008004100000022001500041245003000056210002800086100002000114700001800134700001900152856007600171 2004 eng d aLBNL-5465800aBuildOpt 1.0.1 validation0 aBuildOpt 101 validation1 aWetter, Michael1 aPolak, Elijah1 aCarey, Van, P. uhttps://simulationresearch.lbl.gov/publications/buildopt-101-validation01637nas a2200145 4500008004100000245010400041210006900145260001200214300001200226490000700238520111900245100002001364700001801384856008901402 2004 eng d00aA convergent optimization method using pattern search algorithms with adaptive precision simulation0 aconvergent optimization method using pattern search algorithms w c11/2004 a327-3380 v253 a

Thermal building simulation programs, such as EnergyPlus, compute numerical approximations to solutions of systems of differential algebraic equations. We show that the exact solutions of these systems are usually smooth in the building design parameters, but that the numerical approximations are usually discontinuous due to adaptive solvers and finite precision computations. If such approximate solutions are used in conjunction with optimization algorithms that depend on smoothness of the cost function, one needs to compute high precision solutions, which can be prohibitively expensive if used for all iterations. For such situations, we have developed an adaptive simulation–precision control algorithm that can be used in conjunction with a family of derivative free optimization algorithms. We present the main ingredients of the composite algorithms, we prove that the resulting composite algorithms construct sequences with stationary accumulation points, and we show by numerical experiments that using coarse approximations in the early iterations can significantly reduce computation time.

1 aWetter, Michael1 aPolak, Elijah uhttps://simulationresearch.lbl.gov/publications/convergent-optimization-method-using00504nas a2200121 4500008004100000245009000041210006900131260002600200100002600226700002400252700002200276856008400298 2004 eng d00aDetermining the Effective Viscosity of a Nanofluid Using Brownian Dynamics Simulation0 aDetermining the Effective Viscosity of a Nanofluid Using Brownia aHonolulu, HIc03/20041 aBhattacharya, Prajesh1 aPhelan, Patrick, E.1 aPrasher, Ravi, S. uhttps://simulationresearch.lbl.gov/publications/determining-effective-viscosity00835nas 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-update00745nas a2200181 4500008004100000245019400041210006900235260002500304100002600329700001600355700001300371700001300384700002400397700002200421700001300443700002000456856008700476 2004 eng d00aEvaluation of the Temperature Oscillation Technique to Calculate Thermal Conductivity of Water and Systematic Measurement of the Thermal Conductivity of Aluminum Oxide – Water Nanofluiids0 aEvaluation of the Temperature Oscillation Technique to Calculate aAnaheim, CAc11/20041 aBhattacharya, Prajesh1 aVijayan, P.1 aTang, T.1 aNara, S.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aWang, J.1 aSong, David, W. uhttps://simulationresearch.lbl.gov/publications/evaluation-temperature-oscillation00461nas a2200133 4500008004100000245005300041210005200094260001200146100001600158700002400174700002000198700002600218856008300244 2004 eng d00aNumerical Tools For Particle- Fluid Interactions0 aNumerical Tools For Particle Fluid Interactions c02/20041 aCalhoun, R.1 aPhelan, Patrick, E.1 aYadav, Ajay, K.1 aBhattacharya, Prajesh uhttps://simulationresearch.lbl.gov/publications/numerical-tools-particle-fluid02097nas a2200157 4500008003900000245009300039210006900132260003100201520151300232653003401745100001301779700001801792700002201810700002101832856008601853 2004 d00aPeak Demand Reduction from Pre-Cooling with Zone Temperature Reset in an Office Building0 aPeak Demand Reduction from PreCooling with Zone Temperature Rese aPacific Grove, CAc08/20043 a

The objective of this study was to demonstrate the potential for reducing peak-period electrical demand in moderate-weight commercial buildings by modifying the control of the HVAC system. An 80,000 ft2 office building with a medium-weight building structure and high window-to-wall ratio was used for a case study in which zone temperature set-points were adjusted prior to and during occupancy. HVAC performance data and zone temperatures were recorded using the building control system. Additional operative temperature sensors for selected zones and power meters for the chillers and the AHU fans were installed for the study. An energy performance baseline was constructed from data collected during normal operation. Two strategies for demand shifting using the building thermal mass were then programmed in the control system and implemented progressively over a period of one month. It was found that a simple demand limiting strategy performed well in this building. This strategy involved maintaining zone temperatures at the lower end of the comfort region during the occupied period up until 2 pm. Starting at 2 pm, the zone temperatures were allowed to float to the high end of the comfort region. With this strategy, the chiller power was reduced by 80-100% (1 - 2.3 W/ft2) during normal peak hours from 2 - 5 pm, without causing any thermal comfort complaints. The effects on the demand from 2 - 5 pm of the inclusion of pre-cooling prior to occupancy are unclear.

10ademand shifting (pre-cooling)1 aXu, Peng1 aHaves, Philip1 aPiette, Mary, Ann1 aBraun, James, E. uhttps://simulationresearch.lbl.gov/publications/peak-demand-reduction-pre-cooling00512nas a2200145 4500008004100000245007700041210006900118300001200187490000700199100001800206700002100224700001800245700001500263856008800278 2003 eng d00aComputer Measurement and Automation System for Gas-fired Heating Furnace0 aComputer Measurement and Automation System for Gasfired Heating  a374-3780 v351 aPang, Xiufeng1 aJiang, Yongcheng1 aMiao, Yan-shu1 aXiong, Jun uhttps://simulationresearch.lbl.gov/publications/computer-measurement-and-automation01634nas a2200241 4500008004100000245010400041210006900145260002700214300001400241490000800255520083600263653002201099653001801121653002201139653001901161653001701180653003201197100002001229700001801249700002401267700001601291856008501307 2003 eng d00aA convergent optimization method using pattern search algorithms with adaptive precision simulation0 aconvergent optimization method using pattern search algorithms w aEindhoven, Netherlands a1393-14000 vIII3 a

In solving optimization problems for building design and control, the cost function is often evaluated using a detailed building simulation program. These programs contain code features that cause the cost function to be discontinuous. Optimization algorithms that require smoothness can fail on such problems. Evaluating the cost function is often so time-consuming that stochastic optimization algorithms are run using only a few simulations, which decreases the probability of getting close to a minimum. To show how applicable direct search, stochastic, and gradient-based optimization algorithms are for solving such optimization problems, we compare the performance of these algorithms in minimizing cost functions with different smoothness. We also explain what causes the large discontinuities in the cost functions.

10acoordinate search10adirect search10agenetic algorithm10ahooke–jeeves10aoptimization10aparticle swarm optimization1 aWetter, Michael1 aPolak, Elijah1 aAugenbroe, Godfried1 aHensen, Jan uhttps://simulationresearch.lbl.gov/publications/convergent-optimization-method-000573nas a2200145 4500008004100000245010100041210006900142260002700211100002600238700001500264700002000279700002400299700002200323856008200345 2003 eng d00aDetermining the Effective Thermal Conductivity of a Nanofluid Using Brownian Dynamics Simulation0 aDetermining the Effective Thermal Conductivity of a Nanofluid Us aLas Vegas, NVc07/20031 aBhattacharya, Prajesh1 aSaha, S.K.1 aYadav, Ajay, K.1 aPhelan, Patrick, E.1 aPrasher, Ravi, S. uhttps://simulationresearch.lbl.gov/publications/determining-effective-thermal00527nas 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-self00472nas a2200109 4500008004100000245009400041210006900135260002800204100002600232700002400258856008000282 2002 eng d00aModeling the Behavior of F1-ATPase Biomolecular Motors Using Brownian Dynamics Simulation0 aModeling the Behavior of F1ATPase Biomolecular Motors Using Brow aScottsdale, AZc09/20021 aBhattacharya, Prajesh1 aPhelan, Patrick, E. uhttps://simulationresearch.lbl.gov/publications/modeling-behavior-f1-atpase00482nas a2200109 4500008004100000245010100041210006900142260002600211100002600237700002400263856008500287 2002 eng d00aUnderstanding the Behavior of an F1-ATPase Biomolecular Motor Using Brownian Dynamics Simulation0 aUnderstanding the Behavior of an F1ATPase Biomolecular Motor Usi aBerkeley, CAc06/20021 aBhattacharya, Prajesh1 aPhelan, Patrick, E. uhttps://simulationresearch.lbl.gov/publications/understanding-behavior-f1-atpase01870nas a2200193 4500008004100000245009500041210006900136260001200205300001200217490000700229520122600236653002801462653002301490653000901513100002201522700002601544700001801570856008801588 2001 eng d00aAnalysis of an Information Monitoring and Diagnostic System to Improve Building Operations0 aAnalysis of an Information Monitoring and Diagnostic System to I c10/2001 a783-7920 v333 a

This paper discusses a demonstration of a technology to address the problem that buildings do not perform as well as anticipated during design. We partnered with an innovative building operator to evaluate a prototype information monitoring and diagnostic system (IMDS). The IMDS consists of a set of high-quality sensors, data acquisition software and hardware, and data visualization software including a web-based remote access system, that can be used to identify control problems and equipment faults. The information system allowed the operators to make more effective use of the building control system and freeing up time to take care of other tenant needs. They report observing significant improvements in building comfort, potentially improving tenant health and productivity. The reduction in the labor costs to operate the building is about US$ 20,000 per year, which alone could pay for the information system in about 5 years. A control system retrofit based on findings from the information system is expected to reduce energy use by 20% over the next year, worth over US$ 30,000 per year in energy cost savings. The operators are recommending that similar technology be adopted in other buildings.

10abuilding control system10abuilding operation10aimds1 aPiette, Mary, Ann1 aKhalsa, Satkartar, T.1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/analysis-information-monitoring-and01526nas a2200145 4500008004100000245008300041210006900124260002400193520099600217100002201213700002201235700001501257700001801272856009001290 2001 eng d00aDemand Relief and Weather Sensitivity in Large California Commercial Buildings0 aDemand Relief and Weather Sensitivity in Large California Commer aAustin, TXc07/20013 a

A great deal of research has examined the weather sensitivity of energy consumption in commercial buildings; however, the recent power crisis in California has given greater importance to peak demand. Several new loadshedding programs have been implemented or are under consideration. Historically, the target customers have been large industrial users who can reduce the equivalent load of several large office buildings. While the individual load reduction from an individual office building may be less significant, there is ample opportunity for load reduction in this area. The load reduction programs and incentives for industrial customers may not be suitable for commercial building owners. In particular, industrial customers are likely to have little variation in load from day to day. Thus a robust baseline accounting for weather variability is required to provide building owners with realistic targets that will encourage them to participate in load shedding programs.

1 aKinney, Satkartar1 aPiette, Mary, Ann1 aGu, Lixing1 aHaves, Philip uhttps://simulationresearch.lbl.gov/publications/demand-relief-and-weather-sensitivity01940nas a2200133 4500008004100000245010000041210006900141260001200210520147600222100002201698700002601720700001801746856004201764 2000 eng d00aUse of an Information Monitoring and Diagnostic System for Commissioning and Ongoing Operations0 aUse of an Information Monitoring and Diagnostic System for Commi c05/20003 aThis paper discusses a demonstration of a technology to address the problem that buildings do not perform as well as anticipated during design. We partnered with an innovative building operator to evaluate a prototype Information Monitoring and Diagnostic System (IMDS). The IMDS consists of a set of high-quality sensors, data acquisition software and hardware, and data visualization software, including a web-based remote access system that can be used to identify control problems and equipment faults. The IMDS allowed the operators to make more effective use of the control system, freeing up time to take care of other tenant needs. The operators report observing significant improvements in building comfort, potentially improving tenant health and productivity. Reduction in hours to operate the building are worth about $20,000 per year, which alone could pay for the IMDS in about five years. A control system retrofit based on findings from the IMDS is expected to reduce energy use by 20 percent over the next year, worth over $30,000 per year in energy cost savings. The operators recommend that similar technology be adopted in other buildings. While the current IMDS is oriented toward manual, human-based diagnostic techniques, we also evaluated automated diagnostic techniques. Strategies for utilizing results from this demonstration to influence commercial building performance monitoring for commissioning and operations will be discussed. Background1 aPiette, Mary, Ann1 aKhalsa, Satkartar, T.1 aHaves, Philip uhttp://imds.lbl.gov/pubs/paper383.pdf00448nas a2200133 4500008004100000245004600041210004600087300001200133490000700145100002400152700002200176700002600198856009000224 1995 eng d00aNanofluids for Heat Transfer Applications0 aNanofluids for Heat Transfer Applications a255-2750 v141 aPhelan, Patrick, E.1 aPrasher, Ravi, S.1 aBhattacharya, Prajesh uhttps://simulationresearch.lbl.gov/publications/nanofluids-heat-transfer-applications00561nas 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-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-thermal