Prediction of Solar Power Generation Using NWP and Machine Learning

For effective use of renewable energy sources, accurate forecasting of solar power output is crucial. This study investigates how machine learning techniques, such as Support Vector Machines (SVM), Neural Networks (NN), Linear Regression (LR), and Decision Trees and Numerical Weather Prediction (NWP) are used to forecast solar power generation based on climatic variables, historical power generation data, and other relevant aspects. The models' accuracy in predicting solar power generation could be determined by analyzing them using metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE). The results advance our understanding of the potential of machine learning approaches for precisely forecasting solar power generation, promoting effective energy management, and encouraging the integration of renewable energy sources into grid.