Smart Solar Energy Forecasting Using Stacking and Voting Regression Techniques

Solar energy has emerged as one of the most promising renewable energy sources for sustainable power generation. However, the intermittent nature of solar radiation and changing weather conditions make accurate energy forecasting a challenging task. Reliable solar energy prediction is essential for efficient grid management, energy storage planning, and optimal utilization of renewable resources. This research presents a Smart Solar Energy Forecasting framework using Stacking Regression and Voting Regression Techniques to improve prediction accuracy and reliability.The proposed system utilizes historical solar power generation data along with meteorological parameters such as temperature, humidity, wind speed, and solar irradiance. Data preprocessing techniques including cleaning, normalization, and feature selection are applied to enhance data quality. Multiple machine learning algorithms are trained and evaluated, including Linear Regression, Decision Tree Regression, Support Vector Regression (SVR), Voting Regressor, and Stacking Regressor. Ensemble learning approaches combine the strengths of multiple predictive models to generate highly accurate forecasts. The Voting Regressor aggregates predictions from different base learners, while the Stacking Regressor employs a meta-learning strategy to optimize final predictions. Experimental results demonstrate that ensemble methods outperform individual regression models in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R² Score.

Smart Solar Energy Forecasting, Solar Power Prediction, Machine Learning, Ensemble Learning

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