Smart solar forecasting: Predictive models for radiation and energy generation

Abstract

Efficient integration of solar energy systems depends on reliable forecasting of photovoltaic power output and solar radiation. Despite advances in predictive analytics, existing forecasting models often fail to adequately capture rapid weather fluctuations and long-term environmental variability, creating a critical research gap in intelligent and adaptive prediction frameworks. The objective of this study is to develop and evaluate a machine learning based platform that enhances the accuracy and reliability of solar power forecasting. The study adopts a data-driven methodology that integrates historical irradiance records, real-time meteorological inputs, and environmental parameters through advanced learning algorithms and IoT-enabled sensing technologies. The proposed platform demonstrates improved forecasting performance and stronger adaptability to dynamic weather conditions compared to conventional approaches. The findings further indicate that accurate predictions support grid stability, enhance supply and demand coordination, and reduce reliance on conventional energy backups. By incorporating cloud-based infrastructure and an interactive visualization interface, the system provides actionable insights for energy planning and operational decision-making. The study offers important implications for renewable energy management by supporting efficient resource optimization, advancing sustainable energy practices, and facilitating the transition toward a more resilient and intelligent power grid.

Keywords: Energy forecasting; machine learning; photovoltaic systems; renewable energy; solar radiation