Machine learning-based solar power prediction for major Indian metro cities - Komal Kumar Napa; Rajkumar Govindarajan; J. Senthil Murugan; Billa Manindhar (Institute of Advanced Engineering and Science, 2026)
The growing reliance on renewable energy has intensified the need for accurate solar power forecasting to support efficient grid operation and energy planning. However, reliable prediction remains challenging due to the strong dependence of solar power output on dynamic meteorological conditions. This study proposes a data-driven machine learning (ML) framework for high-precision solar power prediction across several major Indian metro cities. Using hourly weather and power generation data for the year 2023, a random forest regressor was developed to model complex non linear relationships between environmental variables and solar energy output. The proposed model achieved exceptional predictive performance, with an R² score of 0.9999 and a mean absolute error (MAE) of 0.15 kW, significantly outperforming conventional regression approaches. Feature contribution analysis revealed solar radiation as the dominant factor influencing power generation, while cloud cover and elevated temperatures exhibited negative effects. The key contribution of this work lies in demonstrating the robustness and generalizability of ensemble learning for urban-scale solar forecasting under diverse climatic conditions. The findings provide actionable insights for policymakers, grid operators, and energy planners to optimize solar integration and resource management.