Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

Abstract

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil, gas, and uranium are rapidly running out. Research into renewable energy sources such as solar energy is being pursued to counter this. Solar energy is one of the most promising renewable energy sources, as it has the potential to meet the world’s energy needs indefinitely. This study aims to develop and evaluate artificial intelligence (AI) models for predicting hourly global irradiation. The hyperparameters were optimized using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton training algorithm and STATISTICA software. Data from two stations in Algeria with different climatic zones were used to develop the model. Various error measurements were used to determine the accuracy of the prediction models, including the correlation coefficient, the mean absolute error, and the root mean square error (RMSE). The optimal support vector machine (SVM) model showed exceptional efficiency during the training phase, with a high correlation coefficient (R = 0.99) and a low mean absolute error (MAE = 26.5741 Wh/m2), as well as an RMSE of 38.7045 Wh/m2 across all phases. Overall, this study highlights the importance of accurate prediction models in the renewable energy, which can contribute to better energy management and planning.