Intelligent Weather Forecasting for Solar Energy Optimization Using Machine Learning Techniques

Abstract

The growing reliance on renewable energy sources has underscored the need for efficient and accurate solar energy forecasting systems. Solar power generation is heavily influenced by dynamic weather conditions, making real-time and predictive analysis essential for maximizing energy output and operational efficiency. This study presents an intelligent weather forecasting framework using machine learning (ML) techniques to optimize solar energy generation. Historical and real-time meteorological data such as solar irradiance, temperature, humidity, wind speed, and cloud cover are used to train and evaluate various ML models, including Random Forest, Support Vector Machines (SVM), Gradient Boosting, and Long Short-Term Memory (LSTM) networks. The system aims to forecast weather patterns with high accuracy and adjust solar energy management accordingly. Comparative analysis demonstrates that deep learning models, particularly LSTM, outperform traditional methods in temporal weather prediction. Integration of these predictive models into solar power systems results in improved scheduling, grid stability, and overall energy efficiency. The proposed framework highlights the potential of AI-driven solutions in enhancing the reliability and scalability of renewable energy infrastructures.