Solar Irradiation and Wind Speed Forecasting Based on Regression Machine Learning Models

The future is envisaged to have renewable energy resources replacing conventional sources of energy like fossil fuels. In this direction wind and solar energy is emerging to be a vital source of green energy. Although these resources are a promising aspect in providing clean and cheap electrical energy, one demerit is that it is intermittent and therefore unpredictable. This intermittent nature poses a challenge in maintaining the balance between generation and demand of electrical energy thus adversely affecting the system control. Also, the electrical energy companies involved in selling by participating in the electricity pool market need highly accurate solar and wind energy predictions for maximizing their profit. These issues demand a tool for accurate prediction of generation. This paper proposes machine learning prediction models for wind and solar irradiation. For this, a case study is done considering weather data of Malviya National Institute of Technology in Jaipur used to train the regression models. The best-trained model is tested with unseen data and shown to have reasonably good accuracy in predicting wind speed and solar irradiation. A comparative study of regression model performances is done. It is shown that Gaussian Process Regression-based prediction for solar irradiation and the Support Vector Machine outperforms the trained model for the wind speed predictions.