Challenges and Opportunities of Machine Learning Control in Building Operations

Liang Zhang, Zhelun Chen, Xiangyu Zhang, Amanda Pertzborn, Xin Jin

Research output: Contribution to journalArticlepeer-review

9 Scopus Citations

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.

Original languageAmerican English
Pages (from-to)831-852
Number of pages22
JournalBuilding Simulation
Volume16
Issue number6
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023, Tsinghua University Press.

NREL Publication Number

  • NREL/JA-5500-79545

Keywords

  • building energy system
  • building operation control
  • machine learning
  • reinforcement learning

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