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|Title:||Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate||Authors:||Samadianfard, S
Global solar radiation
|Issue Date:||2019||Publisher:||Hong Kong Polytechnic University, Department of Civil and Structural Engineering||Source:||Engineering applications of computational fluid mechanics, 1 Jan. 2019, v. 13, no. 1, p. 142-157 How to cite?||Journal:||Engineering applications of computational fluid mechanics||Abstract:||Solar radiation, moisture and temperature are the most vital meteorological variables which affect plant growth. Due to the fact that the global solar radiation (GSR) is scarcely gauged at meteorological stations in developing countries, it is commonly estimated by data-driven techniques or by empirical equations. In this study, support vector regression (SVR), model trees (MT), gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS) and several empirical equations were applied to assess the relations between GSR and several meteorological variables including minimum temperature (T-min), maximum temperature (T-max), relative humidity (RH), sunshine hours (n), maximum sunshine hours (N), corrected clear-sky solar irradiation (ICSKY), day of year (DOY) and extra-terrestrial radiation (R-a). For this purpose, the daily GSR measured from the beginning of 2011 to the end of 2013 at Tabriz synoptic station, which is located in semi-arid regions of Iran, were used. A direct strong relationship was observed to exist between the GSR and n. For evaluating the performances of studied techniques, three different statistical indicators were used namely root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC). Additionally, a Taylor diagram was utilized to test the similarity between the observed and predicted GSR values. Results indicated that the SVR-6 with input parameters of R-a, RH, T-min, T-max, n/N had better accuracy in predicting GSR with RMSE of 1.656, MAE of 0.990, CC of 0.980 and WI of 0.990 than the other models. Moreover, MT-6 ranked as the second best model in the prediction of GSR values. As an interesting point, studied empirical equations had lower accuracies comparing with the SVR, GEP, MT and ANFIS methods. For instance, GSR values were computed by Angstrom and Prescott equation, as the best empirical equation, with RMSE of 1.786, MAE of 1.156, CC of 0.977 and WI of 0.988. Conclusively, results from the current study proved that the SVR provided reasonable trends for GSR modeling at Tabriz synoptic station. Furthermore, MT models with linear equations can be implemented with a high degree of simplicity and acceptable precision in GSR estimation.||URI:||http://hdl.handle.net/10397/80478||ISSN:||1994-2060||EISSN:||1997-003X||DOI:||10.1080/19942060.2018.1560364||Rights:||© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Samadianfard, S., Majnooni-Heris, A., Qasem, S. N., Kisi, O., Shamshirband, S., & Chau, K. W. (2019). Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate. Engineering Applications of Computational Fluid Mechanics, 13(1), 142-157 is available at https://dx.doi.org/10.1080/19942060.2018.1560364
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