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.


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    Titel :

    A Laboratory Test of an Offline-trained Multi-Agent Reinforcement Learning Algorithm for Heating Systems


    Beteiligte:
    Blad, Christian (Autor:in) / Bøgh, Simon (Autor:in) / Kallesøe, Carsten (Autor:in) / Raftery, Paul (Autor:in)

    Erscheinungsdatum :

    2023-05-01


    Anmerkungen:

    Blad , C , Bøgh , S , Kallesøe , C & Raftery , P 2023 , ' A Laboratory Test of an Offline-trained Multi-Agent Reinforcement Learning Algorithm for Heating Systems ' , Applied Energy , vol. 337 , 120807 . https://doi.org/10.1016/j.apenergy.2023.120807



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



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