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Evaluation and development of empirical models for estimating daily solar radiation

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  • Jahani, Babak
  • Dinpashoh, Y.
  • Raisi Nafchi, Atefeh

Abstract

In this study, accuracy and suitability of eleven models from three different categories for estimating daily solar radiation in Iran were evaluated. For this purpose, daily meteorological data of 23 stations in Iran which record solar radiation was used. The models consist of temperature-, sunshine-based and complex models. Furthermore, four new models, namely; P1, P2, P3 and P4 were developed in the present study. The models P1 and P2 estimate daily solar radiation based on daily range of temperature and, P3 and P4 models contribute more meteorological data. The models were evaluated based on, root mean squared error (RMSE), mean bias error (MBE), coefficient of determination (R2) and modeling efficiency (ME) criteria. The results indicated that despite almost all the studied models were able to estimate daily solar radiation with such a good accuracy, the sunshine user models (either single variable or complex) could perform better than the non-sunshine user models. Furthermore, the accuracy of the sunshine based models was improved when daily range of dry and wet bulb air temperature parameters were included in the models. In addition, P1 and P2 models were the most accurate temperature based models. In terms of complex models, the newly proposed models P3 and P4, and the existing model C4 were the top ranked models. In case of the single variable models also the sunshine based models N1 and N2 were the best performing models.

Suggested Citation

  • Jahani, Babak & Dinpashoh, Y. & Raisi Nafchi, Atefeh, 2017. "Evaluation and development of empirical models for estimating daily solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 878-891.
    • Handle: RePEc:eee:rensus:v:73:y:2017:i:c:p:878-891
    DOI: 10.1016/j.rser.2017.01.124
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    1. Shamshirband, Shahaboddin & Mohammadi, Kasra & Khorasanizadeh, Hossein & Yee, Por Lip & Lee, Malrey & Petković, Dalibor & Zalnezhad, Erfan, 2016. "Estimating the diffuse solar radiation using a coupled support vector machine–wavelet transform model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 428-435.
    2. Bahel, V. & Srinivasan, R. & Bakhsh, H., 1987. "Statistical comparison of correlations for estimation of global horizontal solar radiation," Energy, Elsevier, vol. 12(12), pages 1309-1316.
    3. Khorasanizadeh, H. & Mohammadi, K., 2013. "Introducing the best model for predicting the monthly mean global solar radiation over six major cities of Iran," Energy, Elsevier, vol. 51(C), pages 257-266.
    4. Besharat, Fariba & Dehghan, Ali A. & Faghih, Ahmad R., 2013. "Empirical models for estimating global solar radiation: A review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 798-821.
    5. Muzathik, A.M. & Ibrahim, M.Z. & Samo, K.B. & Wan Nik, W.B., 2011. "Estimation of global solar irradiation on horizontal and inclined surfaces based on the horizontal measurements," Energy, Elsevier, vol. 36(2), pages 812-818.
    6. Kumar, Rajesh & Aggarwal, R.K. & Sharma, J.D., 2015. "Comparison of regression and artificial neural network models for estimation of global solar radiations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1294-1299.
    7. Almorox, J. & Hontoria, C. & Benito, M., 2011. "Models for obtaining daily global solar radiation with measured air temperature data in Madrid (Spain)," Applied Energy, Elsevier, vol. 88(5), pages 1703-1709, May.
    8. Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2015. "Review and statistical analysis of different global solar radiation sunshine models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1869-1880.
    9. Hassan, Gasser E. & Youssef, M. Elsayed & Mohamed, Zahraa E. & Ali, Mohamed A. & Hanafy, Ahmed A., 2016. "New Temperature-based Models for Predicting Global Solar Radiation," Applied Energy, Elsevier, vol. 179(C), pages 437-450.
    10. Bahel, V. & Bakhsh, H. & Srinivasan, R., 1987. "A correlation for estimation of global solar radiation," Energy, Elsevier, vol. 12(2), pages 131-135.
    11. Dos Santos, Cícero Manoel & De Souza, José Leonaldo & Ferreira Junior, Ricardo Araujo & Tiba, Chigueru & de Melo, Rinaldo Oliveira & Lyra, Gustavo Bastos & Teodoro, Iêdo & Lyra, Guilherme Bastos & Lem, 2014. "On modeling global solar irradiation using air temperature for Alagoas State, Northeastern Brazil," Energy, Elsevier, vol. 71(C), pages 388-398.
    12. Mecibah, Mohamed Salah & Boukelia, Taqiy Eddine & Tahtah, Reda & Gairaa, Kacem, 2014. "Introducing the best model for estimation the monthly mean daily global solar radiation on a horizontal surface (Case study: Algeria)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 194-202.
    13. El Mghouchi, Y. & El Bouardi, A. & Sadouk, A. & Fellak, I. & Ajzoul, T., 2016. "Comparison of three solar radiation models and their validation under all sky conditions – case study: Tetuan city in northern of Morocco," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1432-1444.
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