Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles

Abstract

Abstract Implementing machine-learning models in real applications is crucial to achieving intelligent building control and high energy efficiency. Over the past few decades, numerous studies have attempted to explore the application of machine-learning models to building energy efficiency. However, these studies have focused on analyzing the technical feasibility and superiority of machine learning algorithms for fitting building energy-related data and have not considered methods of implementing machine learning technology in building energy efficiency applications. Therefore, this review aims to summarize the current practical issues involved in applying machine-learning models to building energy efficiency by systematically analyzing existing research findings and limitations. The paper first reviews the application status of machine learning-based building energy efficiency research by analyzing the model implementation process and summarizing the main uses of the technology in the overall building energy management life cycle. The paper then elaborates on the causes of, influences on, and potential solutions for practical issues found in the implementation and promotion of machine learning-based building energy efficiency measures. Finally, this paper discusses valuable future machine learning-based building energy efficiency research directions with regard to technology opportunity discovery, data governance, feature engineering, generalizability test, technology diffusion, and knowledge sharing. This paper will provide building researchers and practitioners with a better understanding of the current application statuses of and potential research directions for machine learning models in building energy efficiency.