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A Review of Machine Learning Control in Building Operations

Published

Author(s)

Liang Zhang, Zhelun Chen, Xiangyu Zhang, Amanda Pertzborn

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.
Citation
Building Simulation

Keywords

machine learning, building operation control, building energy system, model predictive control, reinforcement learning, sensor technologies

Citation

Zhang, L. , Chen, Z. , Zhang, X. and Pertzborn, A. (2023), A Review of Machine Learning Control in Building Operations, Building Simulation, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932040 (Accessed June 13, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created March 14, 2023, Updated September 19, 2023