Bayesian Energy Measurement and Verification Analysis

Herman Carstens, Xiaohua Xia and Sarma Yadavalli
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Herman Carstens: Centre for New Energy Systems, University of Pretoria, Pretoria 0002, South Africa
Xiaohua Xia: Centre for New Energy Systems, University of Pretoria, Pretoria 0002, South Africa
Sarma Yadavalli: Department of Industrial and Systems Engineering, University of Pretoria, Pretoria 0002, South Africa

Energies, 2018, vol. 11, issue 2, 1-20

Abstract: 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.

Keywords: statistics; uncertainty; regression; sampling; outlier; probabilistic (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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