Challenges and opportunities of machine learning control in building operations

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.

Automated summary

Machine learning control (MLC) is a flexible method that enhances the accuracy, automation, flexibility, and adaptability of building controllers. This paper provides a systematic review of MLC in building energy systems, focusing on two major application categories: building system modeling and control, and control process learning. The review identifies well-studied MLC topics and areas that require further research, and predicts future trends and opportunities for MLC in building control.