Machine learning models for renewable energy forecasting

It is considered that the use of renewable energy sources will replace fossil fuels due to global climate change and accompanying decisions taken by states. In this study, unlike the renewable energy production estimation studies in the literature, a model was created by taking the socioeconomic, environmental and energy time series data of the countries. In the study, Turkey, which did not promise a numerical reduction in greenhouse gas emissions unlike other developing countries but has an increasing energy production from renewable energy sources, was chosen. In the study, the data between 1990 and 2020 were used to receive more realistic results by considering the interval before and after the Kyoto protocol. Artificial neural networks and support vector regression among machine learning methods were used to predict the model. As a result of the study, support vector regression had a 92% and artificial neural networks had a successful predictive power of 89.9% according to the coefficient of determination (R 2 ). In the study, the root mean square error value was 0.071 for artificial neural networks and 0.045 for support vector regression; the mean squared error value was 0.005 for artificial neural networks and 0.002 for support vector regression, which was close to the ideal values. Both methods were statistically successful. It is predicted that the model designed because of these successful results obtained in the study would guide the creation of energy policies and contribute to scientific studies.

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