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A Practical Guide to Gaussian Process Regression for Energy Measurement and Verification within the Bayesian Framework

Department of Engineering Sciences, University of the Free State, P.O. Box 339, Bloemfontein 9300, South Africa
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Energies 2018, 11(4), 935; https://doi.org/10.3390/en11040935
Received: 25 January 2018 / Revised: 29 March 2018 / Accepted: 4 April 2018 / Published: 14 April 2018
(This article belongs to the Special Issue Bayesian Building Energy Modeling)
Measurement and Verification (M&V) aims to quantify savings achieved as part of energy efficiency and energy management projects. M&V depends heavily on metered energy data, modelling parameters and uncertainties that govern the energy system under consideration. M&V therefore requires a stringent handle on the inherent uncertainties in the calculated savings. The Bayesian framework of data analysis in the form of non-parametric, nonlinear Gaussian Process (GP) regression provides a mechanism by which these uncertainties can be quantified thoroughly, and is therefore an attractive alternative to the more traditional frequentist approach. It is important to select appropriate kernels to construct the prior when performing GP regression. This paper aims to construct a guideline for a practical GP regression within the energy M&V framework. It does not attempt to quantify energy losses or savings, but rather presents a case study that could act as a road map for energy managers and M&V professionals to apply the GP regression as a Bayesian alternative to base-line adjustment. Special attention will be given to the selection of appropriate kernels for the application of baseline adjustment and energy savings quantification in a model-independent manner. View Full-Text
Keywords: measurement and verification; bayesian framework; Gaussian Process; energy management; energy efficiency; energy audit; building energy modelling measurement and verification; bayesian framework; Gaussian Process; energy management; energy efficiency; energy audit; building energy modelling
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Maritz, J.; Lubbe, F.; Lagrange, L. A Practical Guide to Gaussian Process Regression for Energy Measurement and Verification within the Bayesian Framework. Energies 2018, 11, 935.

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