Principles, research status, and prospects of feature engineering for data-driven building energy prediction: A comprehensive review

Abstract

With the rapid growth in the volume of relevant and available data, feature engineering is emerging as a popular research subject in data-driven building energy prediction owing to its essential role in improving data quality. Many studies have examined the feasibility of applying feature engineering methods to data-driven building energy prediction. However, a systematic review of this area's research status, characteristics, and limitations is lacking. Therefore, this study analyzes the current status of research and directions of future work in feature engineering for building energy prediction. In this article, we first discuss the concept of feature engineering and its main methods, including the construction, selection, and extraction of features. We, then, summarize the status and characteristics of feature engineering research in the building energy domain using a comprehensive study of 172 relevant articles. We also discuss critical issues in feature engineering in data-driven building energy prediction, including why feature engineering has recently received increasing attention, whether it is useful in this domain, and effective ways to apply it. Finally, we identify promising research directions in the area based on its current state and limitations. The results here provide researchers and the industry with a better understanding of the state of the art and future research trends in feature engineering for data-driven building energy prediction.