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

Benedetto Grillone, Gerard Mor, Stoyan Danov, Jordi Cipriano, Florencia Lazzari and Andreas Sumper
Additional contact information
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
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

Energies, 2021, vol. 14, issue 17, 1-30

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.

Keywords: Bayesian; baseline; energy; efficiency; probabilistic; uncertainty; buildings; savings (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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