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Operational optimization for off-grid renewable building energy system using deep reinforcement learning

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  • Gao, Yuan
  • Matsunami, Yuki
  • Miyata, Shohei
  • Akashi, Yasunori

Abstract

With the application of renewable energy in single office buildings, an increasing number of power grids require building systems coupled with renewable energy to realize off-grid operation. However, the uncertainty of renewable energy sources and the safety of the corresponding energy storage equipment have become major challenges for these systems. Reinforcement learning has made considerable progress in the field of building control as an advanced control algorithm; however, research on its application to the off-grid operation of renewable energy systems, particularly the specific reward function design is limited.

Suggested Citation

  • Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Operational optimization for off-grid renewable building energy system using deep reinforcement learning," Applied Energy, Elsevier, vol. 325(C).
    • Handle: RePEc:eee:appene:v:325:y:2022:i:c:s0306261922010625
    DOI: 10.1016/j.apenergy.2022.119783
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    1. McIlwaine, Neil & Foley, Aoife M. & Morrow, D. John & Al Kez, Dlzar & Zhang, Chongyu & Lu, Xi & Best, Robert J., 2021. "A state-of-the-art techno-economic review of distributed and embedded energy storage for energy systems," Energy, Elsevier, vol. 229(C).
    2. Wang, Zhe & Hong, Tianzhen, 2020. "Reinforcement learning for building controls: The opportunities and challenges," Applied Energy, Elsevier, vol. 269(C).
    3. Li, Yanxue & Zhang, Xiaoyi & Gao, Weijun & Xu, Wenya & Wang, Zixuan, 2022. "Operational performance and grid-support assessment of distributed flexibility practices among residential prosumers under high PV penetration," Energy, Elsevier, vol. 238(PB).
    4. Georgios D. Kontes & Georgios I. Giannakis & Víctor Sánchez & Pablo De Agustin-Camacho & Ander Romero-Amorrortu & Natalia Panagiotidou & Dimitrios V. Rovas & Simone Steiger & Christopher Mutschler & G, 2018. "Simulation-Based Evaluation and Optimization of Control Strategies in Buildings," Energies, MDPI, vol. 11(12), pages 1-23, December.
    5. Ullah, Zia & Elkadeem, M.R. & Kotb, Kotb M. & Taha, Ibrahim B.M. & Wang, Shaorong, 2021. "Multi-criteria decision-making model for optimal planning of on/off grid hybrid solar, wind, hydro, biomass clean electricity supply," Renewable Energy, Elsevier, vol. 179(C), pages 885-910.
    6. Zhang, Bo & Qiu, Rui & Liao, Qi & Liang, Yongtu & Ji, Haoran & Jing, Rui, 2022. "Design and operation optimization of city-level off-grid hydro–photovoltaic complementary system," Applied Energy, Elsevier, vol. 306(PB).
    7. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
    8. Spandagos, Constantinos & Ng, Tze Ling, 2017. "Equivalent full-load hours for assessing climate change impact on building cooling and heating energy consumption in large Asian cities," Applied Energy, Elsevier, vol. 189(C), pages 352-368.
    9. Ceusters, Glenn & Rodríguez, Román Cantú & García, Alberte Bouso & Franke, Rüdiger & Deconinck, Geert & Helsen, Lieve & Nowé, Ann & Messagie, Maarten & Camargo, Luis Ramirez, 2021. "Model-predictive control and reinforcement learning in multi-energy system case studies," Applied Energy, Elsevier, vol. 303(C).
    10. Blum, D.H. & Arendt, K. & Rivalin, L. & Piette, M.A. & Wetter, M. & Veje, C.T., 2019. "Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems," Applied Energy, Elsevier, vol. 236(C), pages 410-425.
    11. Zakaria, A. & Ismail, Firas B. & Lipu, M.S. Hossain & Hannan, M.A., 2020. "Uncertainty models for stochastic optimization in renewable energy applications," Renewable Energy, Elsevier, vol. 145(C), pages 1543-1571.
    12. 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.
    13. Zhang, Xiaoshun & Bao, Tao & Yu, Tao & Yang, Bo & Han, Chuanjia, 2017. "Deep transfer Q-learning with virtual leader-follower for supply-demand Stackelberg game of smart grid," Energy, Elsevier, vol. 133(C), pages 348-365.
    14. 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.
    15. Mokhtara, Charafeddine & Negrou, Belkhir & Settou, Noureddine & Settou, Belkhir & Samy, Mohamed Mahmoud, 2021. "Design optimization of off-grid Hybrid Renewable Energy Systems considering the effects of building energy performance and climate change: Case study of Algeria," Energy, Elsevier, vol. 219(C).
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    Cited by:

    1. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system," Applied Energy, Elsevier, vol. 326(C).
    2. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "Energy saving and indoor temperature control for an office building using tube-based robust model predictive control," Applied Energy, Elsevier, vol. 341(C).
    3. Lu, Yu & Xiang, Yue & Huang, Yuan & Yu, Bin & Weng, Liguo & Liu, Junyong, 2023. "Deep reinforcement learning based optimal scheduling of active distribution system considering distributed generation, energy storage and flexible load," Energy, Elsevier, vol. 271(C).
    4. Nebiyu Kedir & Phuong H. D. Nguyen & Citlaly Pérez & Pedro Ponce & Aminah Robinson Fayek, 2023. "Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation," Energies, MDPI, vol. 16(9), pages 1-38, April.
    5. 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.
    6. Ayas Shaqour & Aya Hagishima, 2022. "Systematic Review on Deep Reinforcement Learning-Based Energy Management for Different Building Types," Energies, MDPI, vol. 15(22), pages 1-27, November.
    7. Liang, Xinbin & Liu, Zhuoxuan & Wang, Jie & Jin, Xinqiao & Du, Zhimin, 2023. "Uncertainty quantification-based robust deep learning for building energy systems considering distribution shift problem," Applied Energy, Elsevier, vol. 337(C).
    8. Nakıp, Mert & Çopur, Onur & Biyik, Emrah & Güzeliş, Cüneyt, 2023. "Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network," Applied Energy, Elsevier, vol. 340(C).
    9. Keerthana Sivamayil & Elakkiya Rajasekar & Belqasem Aljafari & Srete Nikolovski & Subramaniyaswamy Vairavasundaram & Indragandhi Vairavasundaram, 2023. "A Systematic Study on Reinforcement Learning Based Applications," Energies, MDPI, vol. 16(3), pages 1-23, February.

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