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Article

Bayesian Energy Measurement and Verification Analysis

1
Centre for New Energy Systems, University of Pretoria, Pretoria 0002, South Africa
2
Department of Industrial and Systems Engineering, University of Pretoria, Pretoria 0002, South Africa
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Author to whom correspondence should be addressed.
Energies 2018, 11(2), 380; https://doi.org/10.3390/en11020380
Received: 5 December 2017 / Revised: 12 January 2018 / Accepted: 18 January 2018 / Published: 6 February 2018
(This article belongs to the Special Issue Bayesian Building Energy Modeling)
Energy Measurement and Verification (M&V) aims to make inferences about the savings achieved in energy projects, given the data and other information at hand. Traditionally, a frequentist approach has been used to quantify these savings and their associated uncertainties. We demonstrate that the Bayesian paradigm is an intuitive, coherent, and powerful alternative framework within which M&V can be done. Its advantages and limitations are discussed, and two examples from the industry-standard International Performance Measurement and Verification Protocol (IPMVP) are solved using the framework. Bayesian analysis is shown to describe the problem more thoroughly and yield richer information and uncertainty quantification results than the standard methods while not sacrificing model simplicity. We also show that Bayesian methods can be more robust to outliers. Bayesian alternatives to standard M&V methods are listed, and examples from literature are cited. View Full-Text
Keywords: statistics; uncertainty; regression; sampling; outlier; probabilistic statistics; uncertainty; regression; sampling; outlier; probabilistic
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MDPI and ACS Style

Carstens, H.; Xia, X.; Yadavalli, S. Bayesian Energy Measurement and Verification Analysis. Energies 2018, 11, 380. https://doi.org/10.3390/en11020380

AMA Style

Carstens H, Xia X, Yadavalli S. Bayesian Energy Measurement and Verification Analysis. Energies. 2018; 11(2):380. https://doi.org/10.3390/en11020380

Chicago/Turabian Style

Carstens, Herman, Xiaohua Xia, and Sarma Yadavalli. 2018. "Bayesian Energy Measurement and Verification Analysis" Energies 11, no. 2: 380. https://doi.org/10.3390/en11020380

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