AI-Based Solar Irradiance Forecasting for Optimal Grid Integration

Abstract

The increasing penetration of photovoltaic (PV) generation introduces substantial variability and uncertainty into modern power grids, making accurate solar irradiance forecasting essential for reliable grid operation, economic dispatch, and energy management. This paper presents an AI-based framework for short-term solar irradiance forecasting designed to support optimal integration of PV power into the electrical grid. The proposed framework combines meteorological variables, historical irradiance measurements, temporal characteristics, and PV operating conditions within a data-driven forecasting architecture. Appropriate preprocessing, feature engineering, temporal modelling, and performance evaluation are incorporated to improve forecasting robustness under rapidly changing weather conditions. The framework can employ advanced machine-learning and deep-learning models to capture nonlinear relationships between atmospheric conditions and solar irradiance. Forecast outputs are further integrated into grid-management decisions involving power scheduling, reserve allocation, voltage management, and reduction of renewable-energy curtailment. The proposed approach provides a systematic pathway for transforming solar-resource forecasts into actionable grid-integration strategies. The study demonstrates the potential of AI-driven forecasting to reduce renewable-generation uncertainty and enhance the reliability, flexibility, and operational efficiency of future solar-rich power systems