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

Author

Listed:
  • Jacques Maritz

    (Department of Engineering Sciences, University of the Free State, P.O. Box 339, Bloemfontein 9300, South Africa)

  • Foster Lubbe

    (Department of Engineering Sciences, University of the Free State, P.O. Box 339, Bloemfontein 9300, South Africa)

  • Louis Lagrange

    (Department of Engineering Sciences, University of the Free State, P.O. Box 339, Bloemfontein 9300, South Africa)

Abstract

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.

Suggested Citation

  • Jacques Maritz & Foster Lubbe & Louis Lagrange, 2018. "A Practical Guide to Gaussian Process Regression for Energy Measurement and Verification within the Bayesian Framework," Energies, MDPI, vol. 11(4), pages 1-12, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:935-:d:141060
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    References listed on IDEAS

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    1. Herman Carstens & Xiaohua Xia & Sarma Yadavalli, 2018. "Bayesian Energy Measurement and Verification Analysis," Energies, MDPI, vol. 11(2), pages 1-20, February.
    2. Walter, Travis & Price, Phillip N. & Sohn, Michael D., 2014. "Uncertainty estimation improves energy measurement and verification procedures," Applied Energy, Elsevier, vol. 130(C), pages 230-236.
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    Cited by:

    1. Grillone, Benedetto & Danov, Stoyan & Sumper, Andreas & Cipriano, Jordi & Mor, Gerard, 2020. "A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    2. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    3. Foster Lubbe & Jacques Maritz & Thomas Harms, 2020. "Evaluating the Potential of Gaussian Process Regression for Solar Radiation Forecasting: A Case Study," Energies, MDPI, vol. 13(20), pages 1-18, October.
    4. Jun Yuan & Haowei Wang & Szu Hui Ng & Victor Nian, 2020. "Ship Emission Mitigation Strategies Choice Under Uncertainty," Energies, MDPI, vol. 13(9), pages 1-20, May.
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    6. Shakeel Ahmed, 2023. "A Software Framework for Predicting the Maize Yield Using Modified Multi-Layer Perceptron," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    7. Younness EL Fouih & Amine Allouhi & Jamil Abdelmajid & Tarik Kousksou & Youssef Mourad, 2020. "Post Energy Audit of Two Mosques as a Case Study of Intermittent Occupancy Buildings: Toward more Sustainable Mosques," Sustainability, MDPI, vol. 12(23), pages 1-22, December.

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