Innovative approaches to solar energy forecasting: unveiling the power of hybrid models and machine learning algorithms for photovoltaic power optimization

As the world endeavors to shift toward sustainable energy solutions, the pivotal role of solar energy, specifically photovoltaics, becomes increasingly evident. This study investigates the critical task of accurately predicting photovoltaics power output, a fundamental aspect of maximizing economic benefits and ensuring stability in modern electric power systems. Three categories of models, including deterministic, statistical, and hybrid, are explored, with a focus on machine learning (ML) models such as AdaBoost and HGBoost. The results indicate that AdaBoost generally outperforms HGBoost in terms of accuracy metrics, such as R2, RMSE, and VAF, demonstrating its effectiveness in photovoltaics power prediction. However, the difference in performance, while notable, may not be substantial across all metrics, suggesting that the choice of the best model could depend on specific use cases and trade-offs between accuracy and computational efficiency. In addition, this study introduces hybrid models incorporating optimization algorithms such as fruit-fly optimizer, satin bowerbird optimizer, and particle swarm optimizer, with the integration of HGBoost and satin bowerbird optimizer emerging as the top-performing hybrid model (R2 = 0.9907), depicting enhanced accuracy and reduced error rates. The study concludes that combining ML with optimization algorithms significantly enhances PV power prediction accuracy, offering valuable insights for integrating renewable energy into modern energy systems.