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Data-driven Offline Reinforcement Learning for HVAC-systems

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  • Blad, Christian
  • Bøgh, Simon
  • Kallesøe, Carsten Skovmose

Abstract

This paper presents a novel framework for Offline Reinforcement Learning (RL) with online fine tuning for Heating Ventilation and Air-conditioning (HVAC) systems. The framework presents a method to do pre-training in a black box model environment, where the black box models are built on data acquired under a traditional control policy. The paper focuses on the application of Underfloor Heating (UFH) with an air-to-water-based heat pump. However, the framework should also generalize to other HVAC control applications. Because Black box methods are used is there little to no commissioning time when applying this framework to other buildings/simulations beyond the one presented in this study. This paper explores and deploys Artificial Neural Network (ANN) based methods to design efficient controllers. Two ANN methods are tested and presented in this paper; a Multilayer Perceptron (MLP) method and a Long Short Term Memory (LSTM) based method. It is found that the LSTM-based method reduces the prediction error by 45% when compared with a MLP model. Additionally, different network architectures are tested. It is found that by creating a new model for each time step, performance can be improved additionally 19%. By using these models in the framework presented in this paper, it is shown that a Multi-Agent RL algorithm can be deployed without ever performing worse than an industrial controller. Furthermore, it is shown that if building data from a Building Management System (BMS) is available, an RL agent can be deployed which performs close to optimally from the first day of deployment. An optimal control policy reduces the cost of heating by 19.4 % when compared to a traditional control policy in the simulation presented in this paper.

Suggested Citation

  • Blad, Christian & Bøgh, Simon & Kallesøe, Carsten Skovmose, 2022. "Data-driven Offline Reinforcement Learning for HVAC-systems," Energy, Elsevier, vol. 261(PB).
    • Handle: RePEc:eee:energy:v:261:y:2022:i:pb:s0360544222021739
    DOI: 10.1016/j.energy.2022.125290
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    References listed on IDEAS

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    1. Afram, Abdul & Janabi-Sharifi, Farrokh, 2015. "Gray-box modeling and validation of residential HVAC system for control system design," Applied Energy, Elsevier, vol. 137(C), pages 134-150.
    2. Kazmi, Hussain & Suykens, Johan & Balint, Attila & Driesen, Johan, 2019. "Multi-agent reinforcement learning for modeling and control of thermostatically controlled loads," Applied Energy, Elsevier, vol. 238(C), pages 1022-1035.
    3. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    4. Christian Blad & Simon Bøgh & Carsten Kallesøe, 2021. "A Multi-Agent Reinforcement Learning Approach to Price and Comfort Optimization in HVAC-Systems," Energies, MDPI, vol. 14(22), pages 1-20, November.
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    Cited by:

    1. Chen, Minghao & Xie, Zhiyuan & Sun, Yi & Zheng, Shunlin, 2023. "The predictive management in campus heating system based on deep reinforcement learning and probabilistic heat demands forecasting," Applied Energy, Elsevier, vol. 350(C).
    2. Zhuang, Dian & Gan, Vincent J.L. & Duygu Tekler, Zeynep & Chong, Adrian & Tian, Shuai & Shi, Xing, 2023. "Data-driven predictive control for smart HVAC system in IoT-integrated buildings with time-series forecasting and reinforcement learning," Applied Energy, Elsevier, vol. 338(C).
    3. Blad, C. & Bøgh, S. & Kallesøe, C. & Raftery, Paul, 2023. "A laboratory test of an Offline-trained Multi-Agent Reinforcement Learning Algorithm for Heating Systems," Applied Energy, Elsevier, vol. 337(C).

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