Machine Learning Based Weather Forecasting for Enhanced Solar Energy Optimization

As solar energy systems become increasingly vital in addressing global sustainability goals, the ability to predict weather conditions accurately has gained critical importance. This study presents an intelligent forecasting framework that leverages various machine learning models, including Random Forest, Support Vector Machine, Gradient Boosting, and Long Short-Term Memory networks, to enhance solar energy optimization. By using historical and real-time meteorological data such as solar irradiance, temperature, humidity, wind speed, and cloud cover the models are trained to predict short-term weather fluctuations that impact photovoltaic performance. Comparative analysis reveals that deep learning models, particularly LSTM, offer higher accuracy in capturing temporal dynamics. The integration of these predictions into solar energy management systems improves scheduling, energy storage, and grid stability. This research underscores the potential of AI-driven forecasting in promoting efficient and reliable renewable energy systems.