Systematic review on uncertainty quantification in machine learning-based building energy modeling

Abstract

Uncertainty quantification (UQ) has received increasing attention for improving the reliability, transparency, and robustness of machine learning (ML)-based building energy modeling (BEM). As ML methods are widely integrated into BEM applications, there is a growing need for a structured and comprehensive understanding of UQ implementations in ML-based BEM. This paper addresses this gap by presenting a thorough review of literatures at the intersection of ML, BEM, and UQ, guided by a systematic literature search using a Keyword Synonym Search strategy. First, three primary sources of uncertainty are identified in ML-based BEM, which are building system operations, building simulation tools, and ML models. The contributing factors of these sources are further categorized into aleatoric and epistemic uncertainty. The review then examines ten state-of-the-art UQ techniques, respectively ensemble modeling, prior network, Bayesian neural networks, Markov Chain Monte Carlo, variational inference, dropout networks, Bayes by Backprop, Bayesian active learning, variational autoencoders, and Gaussian process. Each UQ technique is reviewed in terms of its modeling principles and applications within ML-based BEM. The discussion offers critical insights into the necessity, practical implementation, comparative performance, and research gaps of UQ in ML-based BEM. Five research challenges are discussed, such as the underrepresentation of aleatoric uncertainty in building datasets and limited adoptions of advanced UQ techniques within ML-based BEM. Finally, this review concludes with recommendations for future research to support the development of uncertainty-aware ML-based BEMs.