%0 Journal Article %J Applied Energy %D 2019 %T Data fusion in predicting internal heat gains for office buildings through a deep learning approach %A Zhe Wang %A Tianzhen Hong %A Mary Ann Piette %K data fusion %K deep learning %K Internal heat gains %K Miscellaneous electric loads %K Occupant count %K Predictive control %X

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.

%B Applied Energy %V 240 %P 386 - 398 %8 02/2019 %G eng %U https://linkinghub.elsevier.com/retrieve/pii/S0306261919303630 %! Applied Energy %R 10.1016/j.apenergy.2019.02.066 %0 Journal Article %J Building and Environment %D 2019 %T Inferring occupant counts from Wi-Fi data in buildings through machine learning %A Zhe Wang %A Tianzhen Hong %A Mary Ann Piette %A Marco Pritoni %K Building control %K Machine learning %K Occupancy estimation %K Occupant count %K Random forest %K Wi-Fi data %X

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.

%B Building and Environment %V 158 %P 281 - 294 %8 05/2019 %G eng %U https://linkinghub.elsevier.com/retrieve/pii/S0360132319303336 %! Building and Environment %R 10.1016/j.buildenv.2019.05.015 %0 Journal Article %J Applied Energy %D 2019 %T Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems %A David Blum %A K. Arendt %A Lisa Rivalin %A Mary Ann Piette %A Michael Wetter %A C.T. Veje %K building simulation %K hvac %K Model predictive control %K System identification %X

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.

%B Applied Energy %V 236 %P 410 - 425 %8 02/2019 %G eng %U https://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/plain %! Applied Energy %R 10.1016/j.apenergy.2018.11.093 %0 Journal Article %J Energy %D 2019 %T Predicting plug loads with occupant count data through a deep learning approach %A Zhe Wang %A Tianzhen Hong %A Mary Ann Piette %K deep learning %K Long short term memory network %K Occupant count %K Plug loads %K prediction %K Predictive control %X

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.

%B Energy %V 181 %P 29 - 42 %8 05/2019 %G eng %U https://linkinghub.elsevier.com/retrieve/pii/S0360544219310205 %! Energy %R 10.1016/j.energy.2019.05.138 %0 Journal Article %J Energy and Buildings %D 2018 %T Quantifying the benefits of a building retrofit using an integrated system approach: A case study %A Cynthia Regnier %A Kaiyu Sun %A Tianzhen Hong %A Mary Ann Piette %K Building retrofit %K building simulation %K Energy conservation measures %K energy savings %K integrated design %K integrated system %X

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.

%B Energy and Buildings %V 159 %G eng %R 10.1016/j.enbuild.2017.10.090 %0 Report %D 2017 %T Automatic Generation and Simulation of Urban Building Energy Models Based on City Datasets for City-Scale Building Retrofit Analysis %A Yixing Chen %A Tianzhen Hong %A Mary Ann Piette %K Building Energy Modeling %K CityBES %K Energy conservation measures %K energyplus %K Retrofit Analysis %K Urban Scale %X

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.

%G eng %0 Report %D 2017 %T Electric Load Shape Benchmarking for Small- and Medium-Sized Commercial Buildings %A Xuan Luo %A Tianzhen Hong %A Yixing Chen %A Mary Ann Piette %K benchmarking %K Building energy %K cluster analysis %K load profile %K load shape %K representative load pattern %X

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.

%G eng %0 Report %D 2017 %T Small and Medium Building Efficiency Toolkit and Community Demonstration Program %A Mary Ann Piette %A Tianzhen Hong %A William J. Fisk %A Norman Bourassa %A Wanyu R. Chan %A Yixing Chen %A H.Y. Iris Cheung %A Toshifumi Hotchi %A Margarita Kloss %A Sang Hoon Lee %A Phillip N. Price %A Oren Schetrit %A Kaiyu Sun %A Sarah C. Taylor-Lange %A Rongpeng Zhang %K CBES %K commercial buildings %K energy efficiency %K energy modeling %K energy savings %K indoor air quality %K indoor environmental quality %K outdoor air measurement technology %K outdoor airflow intake rate %K retrofit %K ventilation rate %X

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.

%8 03/2017 %G eng %2 LBNL-2001054 %R 10.7941/S93P70 %0 Conference Paper %B 2016 ACEEE Summer Study on Energy Efficiency in Buildings %D 2016 %T A Tale of Three District Energy Systems: Metrics and Future Opportunities %A Rebecca Zarin Pass %A Michael Wetter %A Mary Ann Piette %X

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

%B 2016 ACEEE Summer Study on Energy Efficiency in Buildings %8 08/2017 %G eng %0 Journal Article %J Energy %D 2015 %T Accelerating the energy retrofit of commercial buildings using a database of energy efficiency performance %A Sang Hoon Lee %A Tianzhen Hong %A Mary Ann Piette %A Geof Sawaya %A Yixing Chen %A Sarah C. Taylor-Lange %K building simulation %K Energy conservation measure %K energy modeling %K energyplus %K High Performance computing %K retrofit %X

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.

%B Energy %8 07/2015 %2 LBNL-1004494 %& 738 %R 10.1016/j.energy.2015.07.107 %0 Journal Article %J Applied Energy %D 2015 %T Commercial Building Energy Saver: An energy retrofit analysis toolkit %A Tianzhen Hong %A Mary Ann Piette %A Yixing Chen %A Sang Hoon Lee %A Sarah C. Taylor-Lange %A Rongpeng Zhang %A Kaiyu Sun %A Phillip N. Price %K Building Technologies Department %K Building Technology and Urban Systems Division %K buildings %K buildings energy efficiency %K Commercial Building Systems %K conservation measures %K energy efficiency %K energy use %K energyplus %K External %K Retrofit Energy %K simulation research %X

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.

%B Applied Energy %V 159 %8 9/2015 %2 LBNL-1004502 %& 298 %R 10.1016/j.apenergy.2015.09.002 %0 Journal Article %D 2015 %T DEEP: A Database of Energy Efficiency Performance to Accelerate Energy Retrofitting of Commercial Buildings %A Sang Hoon Lee %A Tianzhen Hong %A Geof Sawaya %A Yixing Chen %A Mary Ann Piette %X

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.

%2 LBNL-180309 %0 Journal Article %D 2015 %T Energy retrofit analysis toolkit for commercial buildings: A review %A Sang Hoon Lee %A Tianzhen Hong %A Mary Ann Piette %A Sarah C. Taylor-Lange %K Building energy retrofit %K Energy conservation measures %K Energy efficiency %K Energy simulation %K Retrofit analysis tools %K Web-based applications %X

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.

%I Elsevier Ltd. %V 89 %P 1087-1100 %8 09/2015 %2 LBNL-1004503 %& 1087 %R 10.1016/j.energy.2015.06.112 %0 Government Document %D 2015 %T Green, Clean, & Mean: Pushing the Energy Envelope in Tech Industry Buildings %A Evan Mills %A Jessica Granderson %A Wanyu R. Chan %A Richard C. Diamond %A Philip Haves %A Bruce Nordman %A Paul A. Mathew %A Mary Ann Piette %A Gerald Robinson %A Stephen E. Selkowitz %X

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”).

%I Lawrence Berkeley National Laboratory %8 05/2015 %2 LBNL-1005070E %0 Journal Article %D 2015 %T A pattern-based automated approach to building energy model calibration %A Kaiyu Sun %A Tianzhen Hong %A Sarah C. Taylor-Lange %A Mary Ann Piette %X

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.

%2 LBNL-1004495 %0 Report %D 2014 %T Review of Existing Energy Retrofit Tools %A Sang Hoon Lee %A Tianzhen Hong %A Mary Ann Piette %2 LBNL-6774E %0 Conference Proceedings %B 2010 ACEEE Summer Study on Energy Efficiency in Buildings %D 2010 %T Systems Approach to Energy Efficient Building Operation: Case Studies and Lessons Learned in a University Campus %A Satish Narayanan %A Michael G. Apte %A Philip Haves %A Mary Ann Piette %A John Elliott %X

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.

%B 2010 ACEEE Summer Study on Energy Efficiency in Buildings %I Omnipress %C Asilomar, California, USA %@ 0-918249-60-0 %G eng %0 Conference Proceedings %B 2006 ACEEE Summer Study on Energy Efficiency in Buildings %D 2006 %T Dynamic Controls for Energy Efficiency and Demand Response: Framework Concepts and a New Construction Case Study in New York %A Sila Kiliccote %A Mary Ann Piette %A David S. Watson %A Glenn D. Hughes %B 2006 ACEEE Summer Study on Energy Efficiency in Buildings %C Pacific Grove, CA, USA %8 08/2006 %G eng %0 Conference Paper %B 2004 ACEEE Summer Study on Energy Efficiency in Buildings %D 2004 %T Peak Demand Reduction from Pre-Cooling with Zone Temperature Reset in an Office Building %A Peng Xu %A Philip Haves %A Mary Ann Piette %A James E. Braun %K demand shifting (pre-cooling) %X

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.

%B 2004 ACEEE Summer Study on Energy Efficiency in Buildings %C Pacific Grove, CA %8 08/2004 %2 LBNL-55800 %0 Journal Article %J Energy and Buildings %D 2001 %T Analysis of an Information Monitoring and Diagnostic System to Improve Building Operations %A Mary Ann Piette %A Satkartar T. Khalsa %A Philip Haves %K building control system %K building operation %K imds %X

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.

%B Energy and Buildings %V 33 %P 783-792 %8 10/2001 %G eng %N 8 %2 LBNL-46038 %& 783 %R 10.1016/S0378-7788(01)00068-8 %0 Conference Proceedings %B International Conference for Enhancing Building Operations %D 2001 %T Demand Relief and Weather Sensitivity in Large California Commercial Buildings %A Satkartar Kinney %A Mary Ann Piette %A Lixing Gu %A Philip Haves %X

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.

%B International Conference for Enhancing Building Operations %C Austin, TX %8 07/2001 %G eng %0 Conference Proceedings %B 8th National Conference on Building Commissioning PECI %D 2000 %T Use of an Information Monitoring and Diagnostic System for Commissioning and Ongoing Operations %A Mary Ann Piette %A Satkartar T. Khalsa %A Philip Haves %X 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 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. Background %B 8th National Conference on Building Commissioning PECI %8 05/2000 %G eng %U http://imds.lbl.gov/pubs/paper383.pdf