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Baseline Energy Use Modeling and Characterization in Tertiary Buildings Using an Interpretable Bayesian Linear Regression Methodology

Author

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  • Benedetto Grillone

    (Building Energy and Environment Group, Centre Internacional de Mètodes Numèrics a l’Enginyeria, GAIA Building (TR14), Rambla Sant Nebridi 22, 08222 Terrassa, Spain)

  • Gerard Mor

    (Building Energy and Environment Group, Centre Internacional de Mètodes Numèrics a l’Enginyeria, CIMNE-Lleida, Pere de Cabrera 16, Office 2G, 25001 Lleida, Spain)

  • Stoyan Danov

    (Building Energy and Environment Group, Centre Internacional de Mètodes Numèrics a l’Enginyeria, GAIA Building (TR14), Rambla Sant Nebridi 22, 08222 Terrassa, Spain)

  • Jordi Cipriano

    (Building Energy and Environment Group, Centre Internacional de Mètodes Numèrics a l’Enginyeria, CIMNE-Lleida, Pere de Cabrera 16, Office 2G, 25001 Lleida, Spain
    Applied Physics Section of the Environmental Science Department, University of Lleida, Jaume II 69, 25001 Lleida, Spain)

  • Florencia Lazzari

    (Building Energy and Environment Group, Centre Internacional de Mètodes Numèrics a l’Enginyeria, CIMNE-Lleida, Pere de Cabrera 16, Office 2G, 25001 Lleida, Spain)

  • Andreas Sumper

    (Centre d’Innovació Tecnològica en Convertidors Estàtics i Accionaments (CITCEA-UPC), Departament d’Enginyeria Elèctrica, ETS d’Enginyeria Industrial de Barcelona, Universitat Politècnica de Catalunya, 08028 Barcelona, Spain)

Abstract

Interpretable and scalable data-driven methodologies providing high granularity baseline predictions of energy use in buildings are essential for the accurate measurement and verification of energy renovation projects and have the potential of unlocking considerable investments in energy efficiency worldwide. Bayesian methodologies have been demonstrated to hold great potential for energy baseline modelling, by providing richer and more valuable information using intuitive mathematics. This paper proposes a Bayesian linear regression methodology for hourly baseline energy consumption predictions in commercial buildings. The methodology also enables a detailed characterization of the analyzed buildings through the detection of typical electricity usage profiles and the estimation of the weather dependence. The effects of different Bayesian model specifications were tested, including the use of different prior distributions, predictor variables, posterior estimation techniques, and the implementation of multilevel regression. The approach was tested on an open dataset containing two years of electricity meter readings at an hourly frequency for 1578 non-residential buildings. The best performing model specifications were identified, among the ones tested. The results show that the methodology developed is able to provide accurate high granularity baseline predictions, while also being intuitive and explainable. The building consumption characterization provides actionable information that can be used by energy managers to improve the performance of the analyzed facilities.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5556-:d:629763
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    References listed on IDEAS

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    Cited by:

    1. Abdurahman Alrobaie & Moncef Krarti, 2022. "A Review of Data-Driven Approaches for Measurement and Verification Analysis of Building Energy Retrofits," Energies, MDPI, vol. 15(21), pages 1-30, October.
    2. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
    3. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.

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