AI-Based Solar Irradiance Forecasting for Optimal Grid Integration

Abstract

The increasing penetration of photovoltaic (PV) generation into modern power systems has introduced significant operational challenges due to the intermittent and stochastic nature of solar energy. Accurate solar irradiance forecasting has therefore become a critical requirement for maintaining grid stability, reducing reserve requirements, improving energy scheduling, and enhancing the reliability of renewable energy integration. Recent advances in Artificial Intelligence (AI) have enabled the development of highly accurate forecasting models capable of capturing complex nonlinear relationships among meteorological, geographical, and temporal variables. This paper presents a comprehensive study of AI-based solar irradiance forecasting for optimal grid integration. The proposed framework investigates the application of advanced machine learning and deep learning techniques, including Artificial Neural Networks, Long Short-Term Memory networks, Convolutional Neural Networks, hybrid architectures, and ensemble learning approaches for short-term and day-ahead forecasting. Furthermore, the study examines data preprocessing, feature engineering, model training, and performance evaluation using standard forecasting metrics. The impact of forecast accuracy on grid operation, energy management, demand-response coordination, and renewable energy penetration is also analyzed. The findings demonstrate that AI-driven forecasting significantly enhances prediction accuracy and supports efficient grid operation, thereby facilitating a more resilient, sustainable, and economically viable power system.