Solar Energy Forecasting Using Random Forest Regression

Abstract

Solar energy forecasting is crucial for efficient energy management and integration of renewable resources into the power grid. This study employs Random Forest Regression (RFR) as a predictive model to forecast solar energy production. RFR has gained popularity in the field of renewable energy forecasting due to its ability to handle complex relationships and high-dimensional data. We collected historical solar irradiance, weather, and solar power generation data to train and validate the RFR model. The results demonstrate the effectiveness of RFR in accurately predicting solar energy production, providing valuable insights for energy grid operators and renewable energy stakeholders. This research contributes to the advancement of reliable solar energy forecasting methods, which are essential for optimizing energy generation and consumption in a sustainable and environmentally friendly manner.The