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A reinforcement learning approach to home energy management for modulating heat pumps and photovoltaic systems

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  • Langer, Lissy
  • Volling, Thomas

Abstract

Buildings are one of the main drivers of global energy consumption and CO2 emissions. Efficient energy management systems will have to integrate renewable energy sources with heating and/or cooling to mitigate climate change. In this study, we analyze the potential of deep reinforcement learning (DRL) to control a smart home with a modulating air-to-water heat pump, a photovoltaic system, a battery energy, and a thermal storage system for floor heating and hot water supply. We transform a mixed-integer linear program (MILP) into a DRL implementation. In our numerical analysis, we compare our results based on the deep deterministic policy gradient (DDPG) algorithm to the theoretical upper bound of the model predictive control (MPC) result under full information, as well as a practice-oriented rule-based benchmark. We show that our proposed DRL implementation outperforms the rule-based approach and achieves a self-sufficiency of 75% with only limited comfort violations. Analyzing different DRL formulations, we conclude that domain knowledge is key to formalizing an efficient decision problem with stable results. Our input data and models, developed using the Julia programming language, are available open source.

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  • Langer, Lissy & Volling, Thomas, 2022. "A reinforcement learning approach to home energy management for modulating heat pumps and photovoltaic systems," Applied Energy, Elsevier, vol. 327(C).
    • Handle: RePEc:eee:appene:v:327:y:2022:i:c:s0306261922012776
    DOI: 10.1016/j.apenergy.2022.120020
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    References listed on IDEAS

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    1. Vázquez-Canteli, José R. & Nagy, Zoltán, 2019. "Reinforcement learning for demand response: A review of algorithms and modeling techniques," Applied Energy, Elsevier, vol. 235(C), pages 1072-1089.
    2. Langer, Lissy & Volling, Thomas, 2020. "An optimal home energy management system for modulating heat pumps and photovoltaic systems," Applied Energy, Elsevier, vol. 278(C).
    3. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    4. Wang, Zhe & Hong, Tianzhen, 2020. "Reinforcement learning for building controls: The opportunities and challenges," Applied Energy, Elsevier, vol. 269(C).
    5. Fischer, David & Madani, Hatef, 2017. "On heat pumps in smart grids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 342-357.
    6. Lissy Langer, 2020. "An Optimal Peer-to-Peer Market Considering Modulating Heat Pumps and Photovoltaic Systems under the German Levy Regime," Energies, MDPI, vol. 13(20), pages 1-25, October.
    7. Dengiz, Thomas & Jochem, Patrick & Fichtner, Wolf, 2019. "Demand response with heuristic control strategies for modulating heat pumps," Applied Energy, Elsevier, vol. 238(C), pages 1346-1360.
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    Cited by:

    1. Mohammed Qais & K. H. Loo & Hany M. Hasanien & Saad Alghuwainem, 2023. "Optimal Comfortable Load Schedule for Home Energy Management Including Photovoltaic and Battery Systems," Sustainability, MDPI, vol. 15(12), pages 1-15, June.
    2. Michael Bachseitz & Muhammad Sheryar & David Schmitt & Thorsten Summ & Christoph Trinkl & Wilfried Zörner, 2024. "PV-Optimized Heat Pump Control in Multi-Family Buildings Using a Reinforcement Learning Approach," Energies, MDPI, vol. 17(8), pages 1-16, April.
    3. Wenya Xu & Yanxue Li & Guanjie He & Yang Xu & Weijun Gao, 2023. "Performance Assessment and Comparative Analysis of Photovoltaic-Battery System Scheduling in an Existing Zero-Energy House Based on Reinforcement Learning Control," Energies, MDPI, vol. 16(13), pages 1-19, June.

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