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Title: Electric load shape benchmarking for small- and medium-sized commercial buildings

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 analysismore » 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.« less
Authors:
 [1] ; ORCiD logo [1] ;  [1] ;  [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Grant/Contract Number:
AC02-05CH11231
Type:
Accepted Manuscript
Journal Name:
Applied Energy
Additional Journal Information:
Journal Volume: 204; Journal Issue: C; Journal ID: ISSN 0306-2619
Publisher:
Elsevier
Research Org:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY, AND ECONOMY
OSTI Identifier:
1436616

Luo, Xuan, Hong, Tianzhen, Chen, Yixing, and Piette, Mary Ann. Electric load shape benchmarking for small- and medium-sized commercial buildings. United States: N. p., Web. doi:10.1016/j.apenergy.2017.07.108.
Luo, Xuan, Hong, Tianzhen, Chen, Yixing, & Piette, Mary Ann. Electric load shape benchmarking for small- and medium-sized commercial buildings. United States. doi:10.1016/j.apenergy.2017.07.108.
Luo, Xuan, Hong, Tianzhen, Chen, Yixing, and Piette, Mary Ann. 2017. "Electric load shape benchmarking for small- and medium-sized commercial buildings". United States. doi:10.1016/j.apenergy.2017.07.108. https://www.osti.gov/servlets/purl/1436616.
@article{osti_1436616,
title = {Electric load shape benchmarking for small- and medium-sized commercial buildings},
author = {Luo, Xuan and Hong, Tianzhen and Chen, Yixing and Piette, Mary Ann},
abstractNote = {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.},
doi = {10.1016/j.apenergy.2017.07.108},
journal = {Applied Energy},
number = C,
volume = 204,
place = {United States},
year = {2017},
month = {7}
}