Deep learning model for regional solar radiation estimation using satellite images

Abstract

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. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).