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

Wassila Tercha, Sid Ahmed Tadjer, Fathia Chekired and Laurent Canale ()
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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

Energies, 2024, vol. 17, issue 5, 1-20

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.

Keywords: forecasting; machine learning; photovoltaic; solar irradiance; temperature; regression models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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