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Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems

Author

Listed:
  • Wassila Tercha

    (Electrification of Industrial Enterprises Laboratory, University of Boumerdes, Boumerdes 35000, Algeria)

  • Sid Ahmed Tadjer

    (Electrification of Industrial Enterprises Laboratory, University of Boumerdes, Boumerdes 35000, Algeria)

  • Fathia Chekired

    (Unité de Développement des Équipements Solaires, UDES, Centre de Développement des Energies Renouvelables, CDER, Tipaza 42004, Algeria)

  • Laurent Canale

    (CNRS, LAPLACE Laboratory, UMR 5213, 31062 Toulouse, France)

Abstract

The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research into renewable energy sources. The accurate prediction of temperature and solar irradiance is essential for optimizing the performance and grid integration of PV systems. Machine learning (ML) has become an effective tool for improving the accuracy of these predictions. This comprehensive review explores the pioneer techniques and methodologies employed in the field of ML-based forecasting of temperature and solar irradiance for PV systems. This article presents a comparative study between various algorithms and techniques commonly used for temperature and solar radiation forecasting. These include regression models such as decision trees, random forest, XGBoost, and support vector machines (SVM). The beginning of this article highlights the importance of accurate weather forecasts for the operation of PV systems and the challenges associated with traditional meteorological models. Next, fundamental concepts of machine learning are explored, highlighting the benefits of improved accuracy in estimating the PV power generation for grid integration.

Suggested Citation

  • Wassila Tercha & Sid Ahmed Tadjer & Fathia Chekired & Laurent Canale, 2024. "Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems," Energies, MDPI, vol. 17(5), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1124-:d:1346759
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    References listed on IDEAS

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    Cited by:

    1. Saman Abolghasemi Moghaddam & Nuno Simões & Michael Brett & Manuel Gameiro da Silva & Joana Prata, 2025. "Dynamic Behavior of a Glazing System and Its Impact on Thermal Comfort: Short-Term In Situ Assessment and Machine Learning-Based Predictive Modeling," Energies, MDPI, vol. 18(17), pages 1-22, September.
    2. Latif Bukari Rashid & Shahzada Zaman Shuja & Shafiqur Rehman, 2025. "Machine Learning Forecasting of Direct Solar Radiation: A Multi-Model Evaluation with Trigonometric Cyclical Encoding," Forecasting, MDPI, vol. 7(4), pages 1-25, October.
    3. Tomás Gavilánez & Néstor Zamora & Josué Navarrete & Nino Vega & Gabriela Vergara, 2025. "AI-Based Virtual Assistant for Solar Radiation Prediction and Improvement of Sustainable Energy Systems," Sustainability, MDPI, vol. 17(19), pages 1-20, October.
    4. Mehmet Das & Erhan Arslan & Sule Kaya & Bilal Alatas & Ebru Akpinar & Burcu Özsoy, 2024. "Performance Evaluation of Photovoltaic Panels in Extreme Environments: A Machine Learning Approach on Horseshoe Island, Antarctica," Sustainability, MDPI, vol. 17(1), pages 1-34, December.
    5. Kaysal, Kübra & Hocaoğlu, Fatih Onur, 2026. "A novel three-segment solar radiation forecasting model," Renewable Energy, Elsevier, vol. 256(PB).
    6. Grothe, Oliver & Kächele, Fabian & Wälde, Mira, 2025. "High-resolution working layouts and time series for renewable energy generation in Europe," Renewable Energy, Elsevier, vol. 239(C).
    7. Aissa Meflah & Fathia Chekired & Nadia Drir & Laurent Canale, 2024. "Accurate Method for Solar Power Generation Estimation for Different PV (Photovoltaic Panels) Technologies," Resources, MDPI, vol. 13(12), pages 1-18, November.

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