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

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 subjecte...

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Vydané v:Applied energy Ročník 337; s. 120807
Hlavní autori: Blad, C., Bøgh, S., Kallesøe, C., Raftery, Paul
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.05.2023
Predmet:
algorithms
cost effectiveness
Denmark
energy
issues and policy
laboratory experimentation
spring
temperature
weather
Denmark
ISSN:0306-2619, 1872-9118
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Shrnutí: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. •Real-world data from laboratory test with underfloor heating.•Offline Multi-Agent Reinforcement Learning can eliminate poor behavior during training while converging.•Long Short Term Memory layers are an effective method for obtaining training models for Reinforcement Learning.•Heating costs are reduced by approximately 15% and comfort is maintained.
Bibliografia:ObjectType-Article-1
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content type line 23
ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2023.120807