A laboratory test of an Offline-trained Multi-Agent Reinforcement Learning Algorithm for Heating Systems

C. Blad, S. Bøgh, C. Kallesøe and Paul Raftery

Applied Energy, 2023, vol. 337, issue C, No S030626192300171X

Abstract: This paper presents a laboratory study of Offline-trained Reinforcement Learning (RL) control of a Heating Ventilation and Air-Conditioning (HVAC) system. We conducted the experiments on a radiant floor heating system consisting of two temperature zones located in Denmark. The buildings are subjected to real-world weather. A previous paper describes the algorithm we tested, which we summarize in this paper. First, we present a benchmarking test which we conducted during spring 2021 and winter 2021/2022. This data is used in the Offline RL framework to train and deploy the RL policy, which we then tested during winter 2021/2022 and spring 2022. An analysis of the data shows that the RL policy showed predictive control-like behavior, and reduced the oscillations of the system by a minimum of 40%. Additionally, we show that the RL policy is minimum 14% more cost-effective than the traditional control policy used in the benchmarking test.

Date: 2023
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DOI: 10.1016/j.apenergy.2023.120807

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