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Deep Reinforcement Learning for the Optimal Angle Control of Tracking Bifacial Photovoltaic Systems

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  • Shuto Tsuchida

    (Department of Science of Technology Innovation, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka 940-2188, Niigata, Japan)

  • Hirofumi Nonaka

    (Faculty of Business Administration, Aichi Institute of Technology, 1247 Yachigusa, Yakusa cho, Toyota 470-0392, Aichi, Japan)

  • Noboru Yamada

    (Department of Mechanical Engineering, Nagaoka University of Technology, 1603-1 Kamitomioka, Nagaoka 940-2188, Niigata, Japan)

Abstract

An optimal tilt-angle control based on artificial intelligence (AI control) for tracking bifacial photovoltaic (BPV) systems is developed in this study, and its effectiveness and characteristics are examined by simulating a virtual system over five years. Using deep reinforcement learning (deep RL), the algorithm autonomously learns the control strategy in real time from when the system starts to operate. Even with limited deep RL input variables, such as global horizontal irradiance, time, tilt angle, and power, the proposed AI control successfully learns and achieves a 4.0–9.2% higher electrical-energy yield in high-albedo cases (0.5 and 0.8) as compared to traditional sun-tracking control; however, the energy yield of AI control is slightly lower in low-albedo cases (0.2). AI control also demonstrates a superior performance when there are seasonal changes in albedo. Moreover, AI control is robust against long-term system degradation by manipulating the database used for reward setting.

Suggested Citation

  • Shuto Tsuchida & Hirofumi Nonaka & Noboru Yamada, 2022. "Deep Reinforcement Learning for the Optimal Angle Control of Tracking Bifacial Photovoltaic Systems," Energies, MDPI, vol. 15(21), pages 1-14, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8083-:d:958972
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    References listed on IDEAS

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    1. AL-Rousan, Nadia & Isa, Nor Ashidi Mat & Desa, Mohd Khairunaz Mat, 2018. "Advances in solar photovoltaic tracking systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2548-2569.
    2. Hafez, A.Z. & Yousef, A.M. & Harag, N.M., 2018. "Solar tracking systems: Technologies and trackers drive types – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 754-782.
    3. Guo, Siyu & Walsh, Timothy Michael & Peters, Marius, 2013. "Vertically mounted bifacial photovoltaic modules: A global analysis," Energy, Elsevier, vol. 61(C), pages 447-454.
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    Cited by:

    1. Ali Javaid & Umer Javaid & Muhammad Sajid & Muhammad Rashid & Emad Uddin & Yasar Ayaz & Adeel Waqas, 2022. "Forecasting Hydrogen Production from Wind Energy in a Suburban Environment Using Machine Learning," Energies, MDPI, vol. 15(23), pages 1-13, November.

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