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Estimation of daily global solar radiation using deep learning model

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  • Kaba, Kazım
  • Sarıgül, Mehmet
  • Avcı, Mutlu
  • Kandırmaz, H. Mustafa

Abstract

Solar radiation (SR) is an important data for various applications such as climate, energy and engineering. Because of this, determination and estimation of temporal and spatial variability of SR has critical importance in order to make plans and organizations for the present and the future. In this study, a deep learning method is employed for estimating the SR over 30 stations located in Turkey. The astronomical factor, extraterrestrial radiation and climatic variables, sunshine duration, cloud cover, minimum temperature and maximum temperature were used as input attributes and the output was obtained as SR. The datasets of 34 stations, spanning the dates from 2001 to 2007, were used for training and testing the model, respectively, and simulated values were compared with ground-truth values. The overall coefficient of determination, root mean square error and mean absolute error were calculated as 0.980, 0.78 MJm−2day−1 and 0.61 MJm−2day−1, respectively. Consequently, DL model has yielded very precise and comparable results for estimating daily global SR. These results are generally better than or they are comparable to many previous studies reported in literature, so one can conclude that the method can be a good alternative and be successfully applied to similar regions.

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  • Kaba, Kazım & Sarıgül, Mehmet & Avcı, Mutlu & Kandırmaz, H. Mustafa, 2018. "Estimation of daily global solar radiation using deep learning model," Energy, Elsevier, vol. 162(C), pages 126-135.
    • Handle: RePEc:eee:energy:v:162:y:2018:i:c:p:126-135
    DOI: 10.1016/j.energy.2018.07.202
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