Solar photovoltaic power generation using machine learning considering weather conditions: A case study of Biret, Mauritania

The increasing adoption of renewable energy sources positions solar energy as a vital solution, especially in remote areas. Forecasting energy production based on climate variables is essential to align supply with demand and improve the management of photovoltaic systems. Artificial Intelligence (A...

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Veröffentlicht in:Engineering applications of artificial intelligence Jg. 162; S. 112621
Hauptverfasser: Rchid, Abdellahi Moulaye, Attia, Moussa, Mohamed Mahmoud, Mohamed Elmamy, Aoulmi, Zoubir, Mahmoud, Abdelkader Ould
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 26.12.2025
Schlagworte:
Artificial intelligence
Gradient boosting algorithm
Hyperparameter optimization
Machine learning
Photovoltaic systems
Photovoltaic systems
Hyperparameter optimization
Gradient boosting algorithm
Artificial intelligence
Machine learning
ISSN:0952-1976
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Zusammenfassung:The increasing adoption of renewable energy sources positions solar energy as a vital solution, especially in remote areas. Forecasting energy production based on climate variables is essential to align supply with demand and improve the management of photovoltaic systems. Artificial Intelligence (AI) algorithms are necessary to predict electrical capacity and power, providing accurate estimations. This research investigates the accuracy of forecasts using data from the village of Biret, Mauritania, over one month. It evaluates several machine learning models, including Gradient Boosting, Random Forest, Adaptive Boosting (AdaBoost), Support Vector Regression (SVR), and Elastic Net. The study applies advanced machine learning techniques, such as Gradient Boosting and Random Forest, to enhance the accuracy of solar energy predictions. Grid Search for Hyperparameter Optimization (GridSearchCV) improved performance by reducing the mean square error (MSE). The optimization allowed Gradient Boosting to achieve the coefficient of determination (R2) value of 0.99, with Random Forest reaching 0.97 and AdaBoost reaching 0.88. The results indicate that the full models, especially Gradient Boosting and AdaBoost, achieve high accuracy, facilitating better matching of electricity production to actual needs.
ISSN:0952-1976
DOI:10.1016/j.engappai.2025.112621