Optimized random forest model for prediction solar irradianceand photovoltaic total power energy by using JAYA algorithm

This research deals with the solar photovoltaic power generation prediction through a machine learning–based architecture combining the Random Forest algorithm with the JAYA metaheuristic optimization. As the demand for renewable energy has been increasing, the establishment of correct and expeditive forecasting models is vital for the system operators, along with the energy managers, for effective planning, efficacy, and assured cost-effectiveness. The data, obtained for Chapel Lane substation in the United Kingdom (2019–2022) at 15 minute integration, went through the pre-processing, such as feature creation and normalizations. Random Forest was utilized for the extraction of the most pertinent parameters for the requirement of prediction, whereas the JAYA algorithm has been used for hyperparameter optimization for the better performance of the designed models. The performance accuracy of the models has been observed through established performance indicators, i.e., the coefficient of determination (R²), mean squared error (MSE), and mean absolute error (MAE). Through the results, the intention is demonstrated by projecting the predicted respective outputs with fewer faults, indicating its potential for solar energy prognostication along with the power management activities.