Recent advances in data mining and machine learning for enhanced building energy management

Xinlei Zhou, Han Du, Shan Xue and Zhenjun Ma

Energy, 2024, vol. 307, issue C

Abstract: Due to the recent advancements in the Internet of Things and data science techniques, a wide range of studies have investigated the use of data mining (DM) and machine learning (ML) algorithms to enhance building energy management (BEM). However, different classes of DM and ML algorithms feature different mechanisms and capabilities, resulting in their distinct roles and performance in BEM. Appropriate integration of different categories of DM and ML algorithms in BEM is essential to promote their wide application and provide guidance for new topic areas. This study presents a literature review of the use of DM and ML techniques in key areas of BEM, including building performance evaluation, energy usage prediction, and demand flexibility optimization. The review categorizes DM and ML techniques into three main categories, including supervised DM, unsupervised DM, and reinforcement learning (RL). Unsupervised techniques are primarily used for building energy performance assessment, while supervised techniques are mainly employed for building performance benchmarking and energy usage prediction. RL has been utilized for optimal building control to improve efficiency, demand flexibility, and indoor thermal comfort. The strengths, shortcomings, and integration of these methods in terms of their applications in BEM are discussed, along with some suggestions for future research in this field.

Keywords: Data mining; Machine learning; Artificial intelligence; Building energy management; Energy flexibility (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544224024101
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:307:y:2024:i:c:s0360544224024101

DOI: 10.1016/j.energy.2024.132636

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().