| 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.
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