SOLAR RADIATION PREDICTION USING MACHINE LEARNING

Information Technology

Solar energy is an abundant and sustainable source of power, making it a critical component of the global transition towards clean energy solutions. Accurate prediction of solar radiation is essential for optimizing the performance of solar energy systems, such as photovoltaic panels and solar thermal plants. This study presents a comprehensive exploration of machilearning techniques for the prediction of solar radiation.Machine learning models have been developed and trained on historical solar radiation data, incorporating various meteorological parameters, geographical factors, and time-related features. The predictive accuracy of these models has been evaluated using real-world datasets from diverse geographic locations and climates.The results demonstrate the effectiveness of machine learning algorithms in accurately forecasting solar radiation levels. These predictions can empower energy stakeholders, grid operators, and solar energy system operators to make informed decisions regarding energy generation, distribution, and consumption. Additionally, the study highlights the significance of feature engineering, model selection, and hyper parameter tuning in enhancing prediction performance.The methodology involves data preprocessing, feature engineering, model selection, and evaluation using metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Additionally, model interpretability techniques, such as feature importance analysis, will be employed to enhance the transparency of predictions.

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