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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 Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE); National Natural Science Foundation of China (NSFC); 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. https://doi.org/10.1016/j.apenergy.2016.06.141
Liang, Xin, Hong, Tianzhen, and Shen, Geoffrey Qiping. 2016. "Improving the accuracy of energy baseline models for commercial buildings with occupancy data". United States. https://doi.org/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},
url = {https://www.osti.gov/biblio/1436599}, journal = {Applied Energy},
issn = {0306-2619},
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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Cited by: 54 works
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Works referenced in this record:

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Works referencing / citing this record:

An energy-saving retrofit baseline determination method for large-scale building based on investigation data
journal, January 2019


Deep Learning in Modeling Energy Cost of Buildings in the Public Sector
book, May 2019

  • Zekić-Sušac, Marijana; Knežević, Marinela; Scitovski, Rudolf
  • 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019): Seville, Spain, May 13–15, 2019, Proceedings, p. 101-110
  • https://doi.org/10.1007/978-3-030-20055-8_10

Building simulation: Ten challenges
journal, April 2018


On the quality evaluation of behavioural models for building performance applications
journal, September 2016