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Machine learning in photovoltaic systems: A review

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  • Gaviria, Jorge Felipe
  • Narváez, Gabriel
  • Guillen, Camilo
  • Giraldo, Luis Felipe
  • Bressan, Michael

Abstract

This paper presents a review of up-to-date Machine Learning (ML) techniques applied to photovoltaic (PV) systems, with a special focus on deep learning. It examines the use of ML applied to control, islanding detection, management, fault detection and diagnosis, forecasting irradiance and power generation, sizing, and site adaptation in PV systems. The contribution of this work is three fold: first, we review more than 100 research articles, most of them from the last five years, that applied state-of-the-art ML techniques in PV systems; second, we review resources where researchers can find open data-sets, source code, and simulation environments that can be used to test ML algorithms; third, we provide a case study for each of one of the topics with open-source code and data to facilitate researchers interested in learning about these topics to introduce themselves to implementations of up-to-date ML techniques applied to PV systems. Also, we provide some directions, insights, and possibilities for future development.

Suggested Citation

  • Gaviria, Jorge Felipe & Narváez, Gabriel & Guillen, Camilo & Giraldo, Luis Felipe & Bressan, Michael, 2022. "Machine learning in photovoltaic systems: A review," Renewable Energy, Elsevier, vol. 196(C), pages 298-318.
    • Handle: RePEc:eee:renene:v:196:y:2022:i:c:p:298-318
    DOI: 10.1016/j.renene.2022.06.105
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    Cited by:

    1. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & David Celeita & George Anders, 2023. "Deep and Machine Learning Models to Forecast Photovoltaic Power Generation," Energies, MDPI, vol. 16(10), pages 1-24, May.
    2. Mao, Hongzhi & Chen, Xie & Luo, Yongqiang & Deng, Jie & Tian, Zhiyong & Yu, Jinghua & Xiao, Yimin & Fan, Jianhua, 2023. "Advances and prospects on estimating solar photovoltaic installation capacity and potential based on satellite and aerial images," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    3. Mahmoud Shahbazi & Niall Andrew Smith & Mousa Marzband & Habib Ur Rahman Habib, 2023. "A Reliability-Optimized Maximum Power Point Tracking Algorithm Utilizing Neural Networks for Long-Term Lifetime Prediction for Photovoltaic Power Converters," Energies, MDPI, vol. 16(16), pages 1-24, August.

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