Machine Learning-Based Prediction of Solar Power Generation Using Weather Data

Abstract

Solar energy has become one of the most important renewable energy resources due to the increasing global demand for clean and sustainable energy systems. However, the intermittent and weather-dependent nature of photovoltaic (PV) power generation creates significant challenges for reliable energy management and smart grid operation. Accurate solar power forecasting is therefore essential for improving energy scheduling, reducing operational uncertainty, and enhancing the integration of renewable energy into modern power systems. In recent years, Machine Learning techniques have gained considerable attention for solar power prediction because of their ability to model complex and nonlinear relationships between weather parameters and photovoltaic output. This paper presents a review of machine learning-based approaches for predicting solar power generation using weather data. Various machine learning techniques including Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting Machines (GBM), XGBoost, and Long Short-Term Memory (LSTM) networks are discussed and compared. The study also examines important weather parameters such as solar irradiance, temperature, humidity, wind speed, and atmospheric pressure that influence forecasting accuracy. Furthermore, commonly used evaluation metrics including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and coefficient of determination (R²-score) are analyzed. The reviewed literature demonstrates that machine learning models significantly improve forecasting accuracy compared to traditional statistical methods, while hybrid and ensemble learning approaches provide superior performance in renewable energy forecasting applications. Finally, the paper discusses current challenges and future research directions for developing more accurate and intelligent solar power forecasting systems.