Predicting Solar Radiation Based on Meteorological Data Using Machine Learning Techniques: A Case Study of Isparta release_ymgkcweoqffdzepnok2mx5udju

Abstract

<jats:p xml:lang="en">Solar energy systems which is one of renewable energy sources takes more interest and gains prevalence day by day. As in other many renewable energy sources, a significant problem in solar energy systems is the unstability of the energy that the system will provide. Prediction of the energy to be obtained is very important in this respect. In this study, solar radiation is predicted using meteorological data taken from the General Directorate of Meteorology for Isparta. For predictions, the random forest (RF), KNN (k-Nearest Neighbor), ANN (Artificial Neural Networks) and Deep Learning (DL) methods are used. In addition, the results of dummy variable usage for time data are 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 random forest and KNN methods. Best results are obtained for the prediction of the solar radiation with ANN and DL.
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