A comparative analysis of machine learning and statistical methods for evaluating building performance: A systematic review and future benchmarking framework

The utilization of machine learning (ML) techniques is increasingly prevalent in the domain of building performance evaluation. This trend is primarily driven by ML's capacity to capture intricate relationships between building attributes and performance metrics, such as energy consumption and comfort levels. However, the comparative merits of ML techniques and traditional statistical methods, such as linear and logistic regression, which are typically more cost-effective and interpretable, remains uncertain. This study presents a systematic comparison between ML and statistical methods in the assessment of building performance, considering factors such as model complexity, interpretability, required expertise, performance disparities, and computational costs. Findings indicate that, in most scenarios, ML techniques outperform statistical methods. Nevertheless, there are notable instances where statistical methods can compete, highlighting the context-dependent nature of technique selection. Furthermore, this research introduces a novel Python-based framework with a user-friendly spreadsheet interface designed for the evaluation and benchmarking of ML and statistical methods in research settings. The developed framework can be easily customized for ML evaluation and benchmarking in diverse fields, including production, logistics, supply chain management, and others.