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Bayesian Energy Measurement and Verification Analysis

Author

Listed:
  • 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)

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.

Suggested Citation

  • Herman Carstens & Xiaohua Xia & Sarma Yadavalli, 2018. "Bayesian Energy Measurement and Verification Analysis," Energies, MDPI, vol. 11(2), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:2:p:380-:d:130436
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    References listed on IDEAS

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    2. Granderson, Jessica & Touzani, Samir & Custodio, Claudine & Sohn, Michael D. & Jump, David & Fernandes, Samuel, 2016. "Accuracy of automated measurement and verification (M&V) techniques for energy savings in commercial buildings," Applied Energy, Elsevier, vol. 173(C), pages 296-308.
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    5. Carstens, Herman & Xia, Xiaohua & Yadavalli, Sarma, 2017. "Low-cost energy meter calibration method for measurement and verification," Applied Energy, Elsevier, vol. 188(C), pages 563-575.
    6. repec:dau:papers:123456789/1908 is not listed on IDEAS
    7. Jeff Leek & Blakeley B. McShane & Andrew Gelman & David Colquhoun & Michèle B. Nuijten & Steven N. Goodman, 2017. "Five ways to fix statistics," Nature, Nature, vol. 551(7682), pages 557-559, November.
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    Citations

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    Cited by:

    1. Jonathan Roth & Jayashree Chadalawada & Rishee K. Jain & Clayton Miller, 2021. "Uncertainty Matters: Bayesian Probabilistic Forecasting for Residential Smart Meter Prediction, Segmentation, and Behavioral Measurement and Verification," Energies, MDPI, vol. 14(5), pages 1-22, March.
    2. Hanna, Richard & Gross, Robert, 2021. "How do energy systems model and scenario studies explicitly represent socio-economic, political and technological disruption and discontinuity? Implications for policy and practitioners," Energy Policy, Elsevier, vol. 149(C).
    3. 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).
    4. 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.
    5. Suwon Song & Chun Gun Park, 2019. "Alternative Algorithm for Automatically Driving Best-Fit Building Energy Baseline Models Using a Data—Driven Grid Search," Sustainability, MDPI, vol. 11(24), pages 1-11, December.
    6. 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.
    7. Benedetto Grillone & Gerard Mor & Stoyan Danov & Jordi Cipriano & Florencia Lazzari & Andreas Sumper, 2021. "Baseline Energy Use Modeling and Characterization in Tertiary Buildings Using an Interpretable Bayesian Linear Regression Methodology," Energies, MDPI, vol. 14(17), pages 1-30, September.
    8. Simon Rouchier, 2022. "Bayesian Workflow and Hidden Markov Energy-Signature Model for Measurement and Verification," Energies, MDPI, vol. 15(10), pages 1-19, May.
    9. Fonseca, Jimeno A. & Nevat, Ido & Peters, Gareth W., 2020. "Quantifying the uncertain effects of climate change on building energy consumption across the United States," Applied Energy, Elsevier, vol. 277(C).
    10. Hou, D. & Hassan, I.G. & Wang, L., 2021. "Review on building energy model calibration by Bayesian inference," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).

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