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• W3075674906 endingPage "110114" @default.
• W3075674906 startingPage "110114" @default.
• W3075674906 abstract "Abstract The prediction of global solar radiation for the regions is of great importance in terms of giving directions of solar energy conversion systems (design, modeling, and operation), selection of proper regions, and even future investment policies of the decision-makers. With this viewpoint, the objective of this paper is to predict daily global solar radiation data of four provinces (Kirklareli, Tokat, Nevsehir and Karaman) which have different solar radiation distribution in Turkey. In the study, four different machine learning algorithms (support vector machine (SVM), artificial neural network (ANN), kernel and nearest-neighbor (k-NN), and deep learning (DL)) are used. In the training of these algorithms, daily minimum and maximum ambient temperature, cloud cover, daily extraterrestrial solar radiation, day length and solar radiation of these provinces are used. The data is supplied from the Turkish State Meteorological Service and cover the last two years (01.01.2018–31.12.2019). To decide on the success of these algorithms, seven different statistical metrics (R2, RMSE, rRMSE, MBE, MABE, t-stat, and MAPE) are discussed in the study. The results shows that R2, MABE, and RMSE values of all algorithms are ranging from 0.855 to 0.936, from 1.870 to 2.328 MJ/m2, from 2.273 to 2.820 MJ/m2, respectively. At all cases, k-NN exhibited the worst result in terms of R2, RMSE, and MABE metrics. Of all the models, DL was the only model that exceeded the t-critic value. In conclusion, the present paper is reporting that all machine learning algorithms tested in this study can be used in the prediction of daily global solar radiation data with a high accuracy; however, the ANN algorithm is the best fitting algorithm among all algorithms. Then it is followed by DL, SVM and k-NN, respectively." @default.
• W3075674906 created "2020-08-24" @default.
• W3075674906 creator A5016070843 @default.
• W3075674906 creator A5060698736 @default.
• W3075674906 creator A5084774480 @default.
• W3075674906 date "2021-01-01" @default.
• W3075674906 modified "2023-10-17" @default.
• W3075674906 title "Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison" @default.
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• W3075674906 doi "https://doi.org/10.1016/j.rser.2020.110114" @default.
• W3075674906 hasPublicationYear "2021" @default.
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