The rapid proliferation of renewable energy sources, such as solar and wind power, underscores the critical need for intelligent energy management systems to address challenges associated with intermittent generation and grid stability. This study investigates advanced methods and models for forecasting electricity generation from renewable energy sources, focusing on enhancing prediction accuracy to optimize energy management in island-mode systems. Using real-world datasets from solar power plants, four distinct approaches – cluster regression approximation, group argument accounting, decision tree synthesis, and forest-based ensemble methods – are evaluated. The results demonstrate the efficacy of machine learning techniques, with the Random Forest model emerging as the most effective due to its ability to handle nonlinear dependencies and noise while maintaining computational efficiency. Additionally, the Polynomial Deep Neural Network approach excels in capturing complex interactions between variables, achieving the highest accuracy at a higher computational cost. These findings highlight the potential of integrating adaptive AI-driven models for improving renewable energy forecasting and operational resilience in decentralized systems. This research provides a foundation for advancing intelligent energy systems, ensuring stability, and optimizing resource utilization in modern renewable-based grids.