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Energies 2018, 11(2), 380;

Bayesian Energy Measurement and Verification Analysis

Centre for New Energy Systems, University of Pretoria, Pretoria 0002, South Africa
Department of Industrial and Systems Engineering, University of Pretoria, Pretoria 0002, South Africa
Author to whom correspondence should be addressed.
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)
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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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Carstens, H.; Xia, X.; Yadavalli, S. Bayesian Energy Measurement and Verification Analysis. Energies 2018, 11, 380.

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