*1.3. The Proposed Study*

Based on the aforementioned literature review, we found that data from PV panels and/or meteorological data are utilized to predict solar radiations. The highest achievable results were found by deep learning techniques [28,31,36–44]. Therefore, we designed our experiment based on shallow and deep learning models. The motivation behind the proposed study was the irregularity of energy delivery in Duzce city in Turkey, which may exist in similar cities around the world. We utilized both PV historical data, which was collected from the city of Duzce in Turkey for the period between 2014 to 2018, as well as the daily meteorological data for the same period. In the proposed study, we compared between a deep ANN and an LSTM model in terms of predicting the solar radiation in the city of Duzce in Turkey on daily basis. We performed hyperparameter optimization at predefined hyperparameter values for both the networks, ANN and LSTM. Selecting a deep learning architecture to perform an accurate prediction of the solar radiation amount is crucial for the system operators to reduce costs and uncertainties [17,41–44]. The main contributions of the proposed work can be summarized as: (i) conducting a comparison between the performance of the most common deep learning models in the literature, (ii) building an LSTM to accurately predict the solar radiation at the city of Duzce in Turkey with the potential to be generalized to more cities around the world, and (iii) conducting a comparison between our results in terms of the coefficient of determination (R2), root mean squared error (RMSE), mean biased error (MBE), and mean absolute error (MAE).
