A Data-Driven Approach Towards the Application of Reinforcement Learning Based HVAC Control

• Published in: Journal of Nigerian Society of Physical Sciences Vol. 5; no. 1; p. 1244
• Main Authors: Falk, Constantin, El Ghayed, Tarek, Van de Sand, Ron, Reiff-Stephan, Jörg
• Format: Journal Article
• Language: English
• Published: Nigerian Society of Physical Sciences 01.02.2023
• ISSN: 2714-2817; 2714-4704
• DOI: 10.46481/jnsps.2023.1244

Refrigeration applications consume a significant share of total electricity demand, with a high indirect impact on global warming through greenhouse gas emissions. Modern technology can help reduce the high power consumption and optimize the cooling control. This paper presents a case study of machine-learning for controlling a commercial refrigeration system. In particular, an approach to reinforcement learning is implemented, trained and validated utilizing a model of a real chiller plant. The reinforcement-learning controller learns to operate the plant based on its interactions with the modeled environment. The validation demonstrates the functionality of the approach, saving around 7% of the energy demand of the reference control. Limitations of the approach were identified in the discretization of the real environment and further model-based simplifications and should be addressed in future research.