Forecasting photovoltaic (PV) power accurately is essential for enhancing grid reliability and optimizing energy management in renewable power systems. This study proposes an optimized ensemble-based regression framework for improving PV power predictions. Three machine learning models (LSTM, CNN-LSTM, and SVR) are utilized to generate baseline forecasts. To enhance predictive performance, particle swarm optimization (PSO) is utilized for hyperparameter tuning, ensuring optimal model configurations. Furthermore, a weighted ensemble strategy is introduced, where simple voting and grid search-based ensemble voting are compared to refine final predictions. Experimental results demonstrate that the optimized grid search ensemble model achieves superior forecasting accuracy, with MSE of 163.02 kW 2 , RMSE of 12.77 kW, nRMSE of 1.55%, rRMSE of 6.31%, MAE of 5.26 kW, and R 2 of 0.9976. The scalability and robustness of the model are tested by utilizing data from different regions and a variable cloud analysis respectively, yielding superior results. These findings highlight the critical role of hyperparameter optimization and ensemble weighting in enhancing solar PV power forecasting, offering a robust framework for grid operators and energy planners to improve decision-making in solar-integrated power systems. • This work employs LSTM, CNN-LSTM, and SVR models for initial PV power forecast. • The PSO algorithm is employed to optimize hyperparameters of the forecasting models. • A simple voting regressor approach is implemented to enhance the initial forecasts. • Grid search is implemented to improve the simple voting regressor forecasts. • SHapley Additive exPlanations is adopted to explain model predictions.
Aduama et al. (Sat,) studied this question.