Forecasting solar energy production: A comparative study of machine learning algorithms

The use of solar energy has been rapidly expanding as a clean and renewable energy source, with the installation of photovoltaic panels on homes, businesses, and large-scale solar farms. The increasing demand for sustainable energy sources has pushed the growth of the solar industry, as well as advancements in technology, making solar panels more efficient and cost-effective. The implementation of solar energy not only reduces our reliance on non-renewable fossil fuels but also helps to mitigate the effects of climate change by reducing carbon emissions. This paper presents a complete and comparative study of solar energy production forecasting in Morocco using six machine learning (ML) algorithms : Support Vector Regression (SVR), Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), Generalized Additive Model (GAM) and Extreme Gradient Boosting (XGBOOST), based on Solar Power Plant daily data installed in Benguerir city of Morocco between January and December 2022. The models were trained, tested, and then evaluated. In order to assess the models performance four metrics were used in this study, namely root mean squared error (RMSE), mean absolute error (MAE), mean absolute scaled error (MASE)and R-squared (R2). The performance of the models reveals ANN to be the most effective predictive model for energy forecasting in similar cases with the lowest value of RMSE, MSAE and the highest value of R-squared, which are accepted as one of the most important performance criteria by the ANN model. The findings of this study not only validate the effectiveness of the ANN algorithm but also offer the appropriate parameters for achieving the best results in predicting solar energy production. By identifying the optimal configuration of the ANN algorithm, we provide valuable insights that can be directly applied in real-world applications, thereby enhancing the optimization of solar energy systems and contributing to a sustainable future, particularly the integration of these results in an edge device for the predictive maintenance of photovoltaic power plants.