Solar Power Generation Prediction Using Random Forest Regression Model

Renewable energy sources include sunshine, wind, flowing water, internal heat, and biomass. Solar energy is a significant source of electricity generation due to its accessibility. Knowing the real generation and consumption of power is the first step of making a good electrical system. To save resources and reduce costs, power utilities are required to balance between produced power and customers’ consumption. Prediction is essential for the future operation of smart grids. To predict the generation, input features must be evaluated based on historical data on ISolarCloud. Supervised machine learning algorithm is used to create a predictive model. In this project, Random Forest Regression model have been chosen to predict the power generation from solar energy. By finding the best-fit algorithm, more investigation would be taken place for improvement in future work such as Correlation Coefficient (R), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Root Relative Squared Error (RRSE). This technique ensures excellent precision in energy forecasting with a very low error rate and the results.