Journal article
Challenges and opportunities of machine learning control in building operations
Building simulation, v 16(6), pp 831-852
01 Jun 2023
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Machine learning control (MLC) is a highly flexible and adaptable method that enables the design, modeling, tuning, and maintenance of building controllers to be more accurate, automated, flexible, and adaptable. The research topic of MLC in building energy systems is developing rapidly, but to our knowledge, no review has been published that specifically and systematically focuses on MLC for building energy systems. This paper provides a systematic review of MLC in building energy systems. We review technical papers in two major categories of applications of machine learning in building control: (1) building system and component modeling for control, and (2) control process learning. We identify MLC topics that have been well-studied and those that need further research in the field of building operation control. We also identify the gaps between the present and future application of MLC and predict future trends and opportunities.
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Details
- Title
- Challenges and opportunities of machine learning control in building operations
- Creators
- Liang Zhang - University of ArizonaZhelun Chen - Drexel UniversityXiangyu Zhang - Natl Renewable Energy Lab, 15013 Denver W Pkwy, Golden, CO USAAmanda Pertzborn - NIST, 100 Bur Dr, Gaithersburg, MD USAXin Jin - Natl Renewable Energy Lab, 15013 Denver W Pkwy, Golden, CO USA
- Publication Details
- Building simulation, v 16(6), pp 831-852
- Publisher
- Springer Nature
- Number of pages
- 22
- Grant note
- DE-AC36-08GO28308 / U.S. Department of Energy (DOE); United States Department of Energy (DOE) U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Building Technologies Office; 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; School of Engineering
- Web of Science ID
- WOS:000948874900001
- Scopus ID
- 2-s2.0-85149934487
- Other Identifier
- 991022207066504721
UN Sustainable Development Goals (SDGs)
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Source: SDGs in the Output
InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Construction & Building Technology
- Thermodynamics