Journal article
A review of machine learning in building load prediction
Applied energy, v 285, 116452
01 Mar 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- A review of machine learning in building load prediction
- Creators
- Liang Zhang - National Laboratory of the RockiesJin Wen - Drexel UniversityYanfei Li - National Laboratory of the RockiesJianli Chen - University of UtahYunyang Ye - Pacific Northwest National LaboratoryYangyang Fu - Texas A&M UniversityWilliam Livingood - National Laboratory of the Rockies
- Publication Details
- Applied energy, v 285, 116452
- Publisher
- Elsevier
- Number of pages
- 22
- Grant note
- U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Building Technologies; United States Department of Energy (DOE) DE-AC3608GO28308 / U.S. Department of Energy (DOE); United States Department of Energy (DOE) National Renewable Energy Laboratory; United States Department of Energy (DOE)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Engineering Management; Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:000649545300034
- Scopus ID
- 2-s2.0-85099221457
- Other Identifier
- 991019169106504721
UN Sustainable Development Goals (SDGs)
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Highly Cited Paper
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Energy & Fuels
- Engineering, Chemical