Forecasting Solar Radiation Based on Meteorological Data Using Machine Learning Techniques: A Case Study of Isparta Province.

Solar energy systems which is one of the renewable energy sources takes more interest and gains prevalence day by day. A significant problem in solar energy systems as in other many renewable energy sources is the instability of the energy that the system will provide. Forecasting of the energy to be obtained is very important in this respect. In this study, solar radiation has been forecasted using meteorological data taken from the General Directorate of Meteorology for Isparta province. Random Forest (RF), k-Nearest Neighbor (k-NN), Artificial Neural Network (ANN) and Deep Learning (DL) methods have been used for forecasting. In addition, the results of dummy variable usage for time data have been examined with these different methods. According to the findings obtained, it is seen that the dummy variable usage increases performance for ANN and DL methods but decreases performance for RF and k-NN methods. Best results have been obtained with ANN and DL for the forecasting of the solar radiation. [ABSTRACT FROM AUTHOR]