Evaluation of machine learning applications in building life cycle processes for energy efficiency: A systematic review

OpenAlex English

In recent years, machine learning has been increasingly applied to achieve energy efficiency in buildings. This study analyzes the utilization of machine learning across the building life cycle by reviewing literature on building energy efficiency. In this context, a systematic literature search was conducted using the Web of Science (WOS) search engine, and 868 publications were found. The publications were analyzed according to their year, subject scope, and qualification results, and 84 publications were selected. These publications were discussed under five categories: objective function and control variables, programs, simulations, machine learning, and optimization algorithms. The relationships between these categories and each phase of the building life cycle were examined. The findings suggest that machine learning can effectively optimize factors related to energy efficiency and building sustainability throughout the life cycle, and it is anticipated that interdisciplinary studies incorporating machine learning will experience exponential growth in the future. • A detailed review of papers on machine learning and building energy has been conducted. • Most studies have been conducted on the operation and design process. • Energy consumption was the primary objective in all processes except the operation. • Accuracy values of the machine learning algorithms vary depending on the objective and processes. • In general, high accuracy was achieved with ANN, SVM, and RF algorithms.