Solar Energy Prediction Using Weather Forecasts: A Comparative Evaluation of Machine Learning Models

Abstract

This study advances solar energy forecasting by developing and evaluating a robust framework that integrates high-resolution weather data with machine learning models. The unpredictable nature of solar energy generation, primarily due to intermittent weather conditions, poses a significant challenge for its efficient integration into power grids. To address this, our research leverages critical meteorological parameters, including Global Horizontal Irradiance (GHI), ambient temperature, relative humidity, cloud cover, and precipitation, to predict photovoltaic (PV) energy output. We perform a comprehensive comparative evaluation of five distinct machine learning algorithms: Linear Regression, Decision Tree, Lasso Regression, Gradient Boosting, and XGBoost, to determine the most effective model for this task. The models were trained on a comprehensive historical dataset comprising 196,776 records, which incorporates both meteorological inputs and corresponding solar energy measurements. The results of our evaluation reveal that XGBoost achieves a superior predictive accuracy of 94%, enabling reliable long-term forecasts and enhanced grid management. The framework demonstrates that leveraging meteorological data significantly boosts prediction performance, thereby supporting sustainable and reliable energy integration. Our findings underscore the efficacy of ensemble learning methods, particularly XGBoost, in capturing the complex, non-linear dependencies inherent in solar energy data. Keywords: Solar energy forecasting, photovoltaic output prediction, machine learning, meteorological parameters, global horizontal irradiance (GHI), gradient boosting, XGBoost, renewable energy integration, Long-term forecasting, ensemble learning

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