Random Forest Regression for Solar Radiation Forecasting with Meteorological Inputs

Accurate solar radiation estimates are essential for the proper planning and operation of solar energy installations. This study examines the effectiveness of the Random Forest (RF) algorithm in predicting solar radiation for the near future, utilizing historical weather data that includes humidity, wind speed, temperature, and cloud cover. The results indicate that the Random Forest model effectively delineates the intricate, nonlinear relationships between solar radiation and meteorological variables, yielding forecasts that are both dependable and resilient. The research highlights the potential of employing ensemble learning techniques, such as Random Forest, to enhance the accuracy of solar energy predictions and support the integration of renewable resources into the grid.