Modeling and Analysis of Solar Power Generation Forecasting using Machine Learning Technique

Abstract

Accurate forecasting of solar power generation is critical for efficient grid operation, energy management, and large-scale integration of renewable energy sources. The intermittent and nonlinear nature of solar energy, influenced by meteorological and environmental factors, makes prediction a challenging task. This research paper presents a comprehensive modeling and analysis of solar power generation forecasting using machine learning (ML) techniques. Various ML models, including Linear Regression, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Random Forest (RF), and Long Short-Term Memory (LSTM) networks, are investigated and compared. Historical solar power output and meteorological data such as solar irradiance, temperature, humidity, and wind speed are utilized for model training and validation. Performance evaluation is conducted using statistical metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R²). The results demonstrate that advanced ML and deep learning models significantly outperform conventional statistical approaches, offering improved accuracy and robustness. The study highlights the potential of machine learning-based forecasting systems in enhancing the reliability and efficiency of solar power integration into modern power grids.