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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 by the authors.Energy models require accurate calibration to deliver reliable predictions. This study offers statistical guidance for a systematic treatment of uncertainty before and during model calibration. Statistical emulation and history matching are introduced. An energy model of a domestic property and a full year of observed data are used as a case study. Emulators, Bayesian surrogates of the energy model, are employed to provide statistical approximations of the energy model outputs and explore the input parameter space efficiently. The emulator’s predictions, alongside quantified uncertainties, are then used to rule out parameter configurations that cannot lead to a match with the observed data. The process is automated within an iterative procedure known as history matching (HM), in which simulated gas consumption and temperature data are simultaneously matched with observed values. The results show that only a small percentage of parameter configurations (0.3% when only gas consumption is matched, and 0.01% when both gas and temperature are matched) yielded outputs matching the observed data. This demonstrates HM’s effectiveness in pinpointing the precise region where model outputs align with observations. The proposed method is intended to offer analysts a robust solution to rapidly explore a model’s response across the entire input space, rule out regions where a match with observed data cannot be achieved, and account for uncertainty, enhancing the confidence in energy models and their viability as a decision support tool.
Author(s): Domingo D, Royapoor M, Du H, Boranian A, Walker S, Goldstein M
Publication type: Article
Publication status: Published
Journal: Energies
Year: 2024
Volume: 17
Issue: 16
Online publication date: 13/08/2024
Acceptance date: 31/07/2024
Date deposited: 09/09/2024
ISSN (electronic): 1996-1073
Publisher: Multidisciplinary Digital Publishing Institute (MDPI)
URL: https://doi.org/10.3390/en17164014
DOI: 10.3390/en17164014
Data Access Statement: The original data presented in the study are openly available in the Collections data repository [49], at https://collections.durham.ac.uk/files/r105741r794 (accessed on 9 August 2024)
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