A review of machine learning in building load prediction. (1st March 2021)

• Record Type: Journal Article
• Authors: Zhang, Liang; Wen, Jin; Li, Yanfei; Chen, Jianli; Ye, Yunyang; Fu, Yangyang; Livingood, William
• Is Part Of: Applied energy. Volume 285(2021)
• Publication Date: 2021-03-01
• Subjects: Building energy system -- Building load prediction -- Building energy forecasting -- Machine learning -- Feature engineering -- Data engineering; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.116452
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: This paper reviews building load prediction with machine learning techniques. Review and technical papers are searched by Sub-keyword Synonym Searching method. Technical papers are reviewed in terms of application, algorithms, and data. Primary limitations and gaps are identified; future trends are predicted. A guidance for future technical paper on building load prediction is proposed. 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. Firstly, we review the applications of building load prediction model (task T ). Then, we review the modeling algorithms that improve machine learning performance and accuracy (performance P ). Throughout the papers, we also review the literature from the data perspective for modeling (experience E ), including data engineering from the sensor level to data level, pre-processing, feature extraction and selection. Finally, we conclude with a discussion of well-studied and relatively unexplored fields for future research reference. We also identify the gaps in current machine learning application and predict for future trends and development.