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Title: Improving the accuracy of energy baseline models for commercial buildings with occupancy data

Abstract

More than 80% of energy is consumed during operation phase of a building's life cycle, so energy efficiency retrofit for existing buildings is considered a promising way to reduce energy use in buildings. The investment strategies of retrofit depend on the ability to quantify energy savings by “measurement and verification” (M&V), which compares actual energy consumption to how much energy would have been used without retrofit (called the “baseline” of energy use). Although numerous models exist for predicting baseline of energy use, a critical limitation is that occupancy has not been included as a variable. However, occupancy rate is essential for energy consumption and was emphasized by previous studies. This study develops a new baseline model which is built upon the Lawrence Berkeley National Laboratory (LBNL) model but includes the use of building occupancy data. The study also proposes metrics to quantify the accuracy of prediction and the impacts of variables. However, the results show that including occupancy data does not significantly improve the accuracy of the baseline model, especially for HVAC load. The reasons are discussed further. In addition, sensitivity analysis is conducted to show the influence of parameters in baseline models. To conclude, the results from this studymore » can help us understand the influence of occupancy on energy use, improve energy baseline prediction by including the occupancy factor, reduce risks of M&V and facilitate investment strategies of energy efficiency retrofit.« less

Authors:
 [1]; ORCiD logo [2];  [3]
  1. Shanghai Jiao Tong Univ. (China). School of International and Public Affairs; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Building Technology and Urban Systems Division
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Building Technology and Urban Systems Division
  3. Hong Kong Polytechnic Univ. (China). Dept. of Building and Real Estate
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE); National Natural Science Foundation of China (NNSFC); Hong Kong Polytechnic Univ. (China); Rutgers Univ., Piscataway, NJ (United States); International Energy Agency (IEA) Energy in Buildings and Community (EBC) Programme Annex 66
OSTI Identifier:
1436599
Alternate Identifier(s):
OSTI ID: 1399815
Grant/Contract Number:
AC02-05CH11231; 71271184
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 179; Journal Issue: C; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; 29 ENERGY PLANNING, POLICY, AND ECONOMY; Baseline model; Occupancy; Building energy use; Measurement and verification; Energy efficiency retrofit

Citation Formats

Liang, Xin, Hong, Tianzhen, and Shen, Geoffrey Qiping. Improving the accuracy of energy baseline models for commercial buildings with occupancy data. United States: N. p., 2016. Web. doi:10.1016/j.apenergy.2016.06.141.
Liang, Xin, Hong, Tianzhen, & Shen, Geoffrey Qiping. Improving the accuracy of energy baseline models for commercial buildings with occupancy data. United States. doi:10.1016/j.apenergy.2016.06.141.
Liang, Xin, Hong, Tianzhen, and Shen, Geoffrey Qiping. Thu . "Improving the accuracy of energy baseline models for commercial buildings with occupancy data". United States. doi:10.1016/j.apenergy.2016.06.141. https://www.osti.gov/servlets/purl/1436599.
@article{osti_1436599,
title = {Improving the accuracy of energy baseline models for commercial buildings with occupancy data},
author = {Liang, Xin and Hong, Tianzhen and Shen, Geoffrey Qiping},
abstractNote = {More than 80% of energy is consumed during operation phase of a building's life cycle, so energy efficiency retrofit for existing buildings is considered a promising way to reduce energy use in buildings. The investment strategies of retrofit depend on the ability to quantify energy savings by “measurement and verification” (M&V), which compares actual energy consumption to how much energy would have been used without retrofit (called the “baseline” of energy use). Although numerous models exist for predicting baseline of energy use, a critical limitation is that occupancy has not been included as a variable. However, occupancy rate is essential for energy consumption and was emphasized by previous studies. This study develops a new baseline model which is built upon the Lawrence Berkeley National Laboratory (LBNL) model but includes the use of building occupancy data. The study also proposes metrics to quantify the accuracy of prediction and the impacts of variables. However, the results show that including occupancy data does not significantly improve the accuracy of the baseline model, especially for HVAC load. The reasons are discussed further. In addition, sensitivity analysis is conducted to show the influence of parameters in baseline models. To conclude, the results from this study can help us understand the influence of occupancy on energy use, improve energy baseline prediction by including the occupancy factor, reduce risks of M&V and facilitate investment strategies of energy efficiency retrofit.},
doi = {10.1016/j.apenergy.2016.06.141},
journal = {Applied Energy},
number = C,
volume = 179,
place = {United States},
year = {Thu Jul 07 00:00:00 EDT 2016},
month = {Thu Jul 07 00:00:00 EDT 2016}
}

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  • This report describes the National Renewable Energy Laboratory's (NREL) methodology to assess and improve the accuracy of whole-building energy analysis for residential buildings.
  • Trustworthy savings calculations are critical to convincing investors in energy efficiency projects of the benefit and cost-effectiveness of such investments and their ability to replace or defer supply-side capital investments. However, today’s methods for measurement and verification (M&V) of energy savings constitute a significant portion of the total costs of efficiency projects. They also require time-consuming manual data acquisition and often do not deliver results until years after the program period has ended. The rising availability of “smart” meters, combined with new analytical approaches to quantifying savings, has opened the door to conducting M&V more quickly and at lower cost,more » with comparable or improved accuracy. These meter- and software-based approaches, increasingly referred to as “M&V 2.0”, are the subject of surging industry interest, particularly in the context of utility energy efficiency programs. Program administrators, evaluators, and regulators are asking how M&V 2.0 compares with more traditional methods, how proprietary software can be transparently performance tested, how these techniques can be integrated into the next generation of whole-building focused efficiency programs. This paper expands recent analyses of public-domain whole-building M&V methods, focusing on more novel M&V2.0 modeling approaches that are used in commercial technologies, as well as approaches that are documented in the literature, and/or developed by the academic building research community. We present a testing procedure and metrics to assess the performance of whole-building M&V methods. We then illustrate the test procedure by evaluating the accuracy of ten baseline energy use models, against measured data from a large dataset of 537 buildings. The results of this study show that the already available advanced interval data baseline models hold great promise for scaling the adoption of building measured savings calculations using Advanced Metering Infrastructure (AMI) data. Median coefficient of variation of the root mean squared error (CV(RMSE)) was less than 25% for every model tested when twelve months of training data were used. With even six months of training data, median CV(RMSE) for daily energy total was under 25% for all models tested. Finally, these findings can be used to build confidence in model robustness, and the readiness of these approaches for industry uptake and adoption« less
  • © 2016. Trustworthy savings calculations are critical to convincing investors in energy efficiency projects of the benefit and cost-effectiveness of such investments and their ability to replace or defer supply-side capital investments. However, today's methods for measurement and verification (M & V) of energy savings constitute a significant portion of the total costs of efficiency projects. They also require time-consuming manual data acquisition and often do not deliver results until years after the program period has ended. The rising availability of "smart" meters, combined with new analytical approaches to quantifying savings, has opened the door to conducting M & Vmore » more quickly and at lower cost, with comparable or improved accuracy. These meter- and software-based approaches, increasingly referred to as "M & V 2.0", are the subject of surging industry interest, particularly in the context of utility energy efficiency programs. Program administrators, evaluators, and regulators are asking how M & V 2.0 compares with more traditional methods, how proprietary software can be transparently performance tested, how these techniques can be integrated into the next generation of whole-building focused efficiency programs.This paper expands recent analyses of public-domain whole-building M & V methods, focusing on more novel M & V 2.0 modeling approaches that are used in commercial technologies, as well as approaches that are documented in the literature, and/or developed by the academic building research community. We present a testing procedure and metrics to assess the performance of whole-building M & V methods. We then illustrate the test procedure by evaluating the accuracy of ten baseline energy use models, against measured data from a large dataset of 537 buildings. The results of this study show that the already available advanced interval data baseline models hold great promise for scaling the adoption of building measured savings calculations using Advanced Metering Infrastructure (AMI) data. Median coefficient of variation of the root mean squared error (CV(RMSE)) was less than 25% for every model tested when twelve months of training data were used. With even six months of training data, median CV(RMSE) for daily energy total was under 25% for all models tested. These findings can be used to build confidence in model robustness, and the readiness of these approaches for industry uptake and adoption.« less