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Solar energy is one of the most prominent renewable energy sources today and it can contribute to the energy market with accurate solar radiation estimation. This study proposes a new deep learning-based solar radiation forecasting model using solar radiation data (W/m2) obtained from a ground-based Meteorology measurement station in Hakkari, Turkey, and satellite images taken simultaneously from the European Meteorological Satellites Utilization Organization (EUMETSAT). The model based on convolutional neural networks (CNN) framework adequately extracts the features inherent in multilayer satellite images used as input and obtains more accurate prediction results than traditional models. The prediction performance of the fully-trained CNN model was evaluated using different error metrics such as mean square error (MSE), mean absolute error (MAE), root mean squared error (RMSE), normalized root mean squared error (nRMSE), and coefficient of determination (R2). The results show that the proposed model can accurately predict solar radiation without the need for any measuring device, by making successful meteorological inferences from satellite images with 0.015% MSE, 8.21% nRMSE and 99.8% R2 ratios. Furthermore, the proposed model can be used to verify and calibrate global solar radiation measured from the ground within other locations around the world with similar geographic and climatological characteristics.

This study focuses on the important issue of predicting electricity load for efficient energy management. To achieve this goal, different statistical methods were compared, and results over time were analyzed using various ratios and layers for training and testing. This study uses an artificial neural network (ANN) model with advanced prediction techniques such as genetic algorithms (GA) and adaptive neuro-fuzzy inference systems (ANFIS). This article stands out with a comprehensive compilation of many features and methodologies previously presented in other studies. This study uses a long-term pattern in the prediction process and achieves the lowest relative error values by using hourly divided annual data for testing and training. Data samples were applied to different algorithms, and we examined their effects on load predictions to understand the relationship between various factors and electrical load. This study shows that the ANN–GA model has good accuracy and low error rates for load predictions compared to other models, resulting in the best performance for our system.