SOLAR ENERGY PREDICTION

Accurate prediction of solar energy production is essential for optimizing renewable energy utilization and supporting sustainable energy infrastructure. This project focuses on forecasting solar power output using machine learning-based regression techniques applied to a real-world solar generation dataset. Key features such as temperature, humidity, and solar irradiance are analyzed to build predictive models. Initially, traditional regression algorithms like Linear Regression, Support Vector Regression, and Decision Tree Regression are implemented to establish baseline performance. To improve predictive accuracy, advanced ensemble methods such as Stacking Regressor and Voting Regressor are applied. These models combine the strengths of multiple learners to enhance generalization and robustness. The project demonstrates the effectiveness of ensemble learning in solar energy forecasting and highlights its potential in improving power management systems in solar-rich regions. The outcomes aim to assist stakeholders in making data-driven decisions for efficient solar energy production and planning, ensuring energy sustainability, minimizing resource wastage, and promoting smart grid integration through intelligent forecasting models.

Keywords: Solar Energy Forecasting, Regression Analysis, Machine Learning, Stacking Regressor, Voting Regressor, Renewable Energy, Ensemble Learning, Power Prediction.