Energy Benchmarking in Healthcare Facilities: A Comparative Study
Publication: Journal of Construction Engineering and Management
Volume 147, Issue 11
Abstract
Benchmarking the energy efficiency of buildings is important for optimizing the efficient use of energy and reducing carbon footprint. Healthcare facilities have especially high energy usage, but the historical difficulty of collecting energy data from a relatively large number of healthcare facilities has made it challenging to develop the appropriate benchmarking system. In this paper, we seek to foster the task of benchmarking the energy efficiency of healthcare facilities using three different methods: multiple linear regression (MLR), generalized additive model (GAM), and energy performance index (EPI). The analysis was applied using a unique dataset that contained information on energy consumption and various building features for 22 large-size public hospitals managed by the Shanghai hospital development center (SHDC). Findings suggest that different benchmarking methods yield substantially different energy performance ranking results. Furthermore, a comparative analysis of the three benchmarking methods was conducted in terms of goodness-of-fit, consistency, and robustness. The results show that MLR tends to be the most consistent and robust benchmarking model, while GAM appears to have the best goodness-of-fit. The proposed methodology can assist hospital managers identify potential improvements for more efficient use of energy in healthcare facilities.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This study is supported by the National Natural Science Foundation of China (No. 72001160), China Scholarship Council (No. 202006260314), the Support Program for Young and Middle-Tech Leading Talents of Tongji University. Specifically, the first author was supported by the Support Program for Young and Middle-Tech Leading Talents of Tongji University; the second author was supported by China Scholarship Council (No. 202006260314); the fifth author was supported by the National Natural Science Foundation of China (No. 72001160).
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Journal of Construction Engineering and Management
Volume 147 • Issue 11 • November 2021
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© 2021 American Society of Civil Engineers.
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Received: Jan 8, 2021
Accepted: Jul 15, 2021
Published online: Sep 14, 2021
Published in print: Nov 1, 2021
Discussion open until: Feb 14, 2022
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