Actions for Uncertainty Estimation Improves Energy Measurement and Verification Procedures [electronic resource].

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Uncertainty Estimation Improves Energy Measurement and Verification Procedures [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy. Office of Science, 2014.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy
Physical Description
230 : digital, PDF file
Additional Creators
Lawrence Berkeley National Laboratory, United States. Department of Energy. Office of Science, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
Implementing energy conservation measures in buildings can reduce energy costs and environmental impacts, but such measures cost money to implement so intelligent investment strategies require the ability to quantify the energy savings by comparing actual energy used to how much energy would have been used in absence of the conservation measures (known as the baseline energy use). Methods exist for predicting baseline energy use, but a limitation of most statistical methods reported in the literature is inadequate quantification of the uncertainty in baseline energy use predictions. However, estimation of uncertainty is essential for weighing the risks of investing in retrofits. Most commercial buildings have, or soon will have, electricity meters capable of providing data at short time intervals. These data provide new opportunities to quantify uncertainty in baseline predictions, and to do so after shorter measurement durations than are traditionally used. In this paper, we show that uncertainty estimation provides greater measurement and verification (M&V) information and helps to overcome some of the difficulties with deciding how much data is needed to develop baseline models and to confirm energy savings. We also show that cross-validation is an effective method for computing uncertainty. In so doing, we extend a simple regression-based method of predicting energy use using short-interval meter data. We demonstrate the methods by predicting energy use in 17 real commercial buildings. We discuss the benefits of uncertainty estimates which can provide actionable decision making information for investing in energy conservation measures.
Report Numbers
E 1.99:lbnl-6907e
lbnl-6907e
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Note
Published through SciTech Connect.
05/14/2014.
"lbnl-6907e"
Applied Energy 130 ISSN 0306-2619 FT
Walter, Travis; Price, Phillip N.; Sohn, Michael D.
Funding Information
DE-AC02-05CH11231
View MARC record | catkey: 23762219