Dynamic Forecasting of Solar Power Using Random Forest Regression

One of the biggest obstacles to solar power generation's integration into the electrical grid is its unpredictability. As grid balancing authorities work to keep supply and demand in balance, this discrepancy presents challenges. A viable way to enhance solar power forecasting—which is crucial for maximizing energy dispatch and grid stability—is through machine learning. This study investigates the prediction of solar power generation using a particular machine learning method known as Random Forests. Solar power production from 21 solar panels in Germany and historical meteorological data are used in the experiment. Decision Tree Regression (DT) and Multiple Linear Regression (MLR), two further regression approaches, are then used to compare the experiment's results. Using standard performance metrics including Coefficient of Determination (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), we assessed each model's capacity to predict solar power generation. With an average R2 of 88.31%, average RMSE of 6.25%, and average MAE of 2.96%, the Random Forest model outperformed the other models, according to the comparative findings. Random Forests therefore have potential as a potent method for maximizing solar energy integration into the electrical grid.