A review of machine learning in building load prediction

Liang Zhang, Jin Wen, Yanfei Li, Jianli Chen, Yunyang Ye, Yangyang Fu and William Livingood

Applied Energy, 2021, vol. 285, issue C, No S0306261921000209

Abstract: The surge of machine learning and increasing data accessibility in buildings provide great opportunities for applying machine learning to building energy system modeling and analysis. Building load prediction is one of the most critical components for many building control and analytics activities, as well as grid-interactive and energy efficiency building operation. While a large number of research papers exist on the topic of machine-learning-based building load prediction, a comprehensive review from the perspective of machine learning is missing. In this paper, we review the application of machine learning techniques in building load prediction under the organization and logic of the machine learning, which is to perform tasks T using Performance measure P and based on learning from Experience E.

Keywords: Building energy system; Building load prediction; Building energy forecasting; Machine learning; Feature engineering; Data engineering (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (73)

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DOI: 10.1016/j.apenergy.2021.116452

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