02297nas 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/S036054421931020502081nas 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-building02592nas 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-simulation02489nas 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-efficiency01862nas 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-retrofit02814nas a2200349 4500008003900000245007400039210006900113260001100182490000800193520171300201653003701914653005101951653001402002653003202016653003202048653002602080653002202106653001502128653001502143653001302158653002002171653002402191100001902215700002202234700001702256700002002273700002802293700002002321700001502341700002302356856008502379 2015 d00aCommercial Building Energy Saver: An energy retrofit analysis toolkit0 aCommercial Building Energy Saver An energy retrofit analysis too c9/20150 v1593 a

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

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

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

1 aLee, Sang, Hoon1 aHong, Tianzhen1 aSawaya, Geof1 aChen, Yixing1 aPiette, Mary, Ann uhttps://simulationresearch.lbl.gov/publications/deep-database-energy-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-energy02242nas 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-approach00390nas 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-tools02269nas 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-efficient00582nas 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-efficiency02097nas 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-cooling01870nas 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.pdf