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Forecast Analysis of Renewable Solar Energy Production Using Meteorological Data with Machine Learning Methods
(Fatma Kutlu Gündoğdu, 2024) Macit Sezikli, Naciye; Alkan, Ümit; Zontul, Metin; Elabiad, Zeynep
Solar energy power plants play a significant role in meeting the demand for sustainable and clean energy. However, variable weather conditions, seasonal effects, and similar factors can result in the need for energy overproduction to be stored or lead to costs associated with energy deficiency. These situations can result in inefficiencies in solar energy production. The objective of this study is to predict energy production, increase efficiency, and develop more sustainable energy strategies by using machine learning methods with data obtained from meteorological and solar energy panels. This study aims to assess the results achieved by existing models and compare their successes. The Random Forest algorithm, which achieved the highest R2 score, also obtained significantly lower values for MSE, RMSE, and MAE. This indicates that the Random Forest algorithm performs the best among the algorithms used in this study. This ranking of success is followed by Decision Trees and KNearest neighbors.