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Uses of sunshine duration to estimate the global solar radiation over eight meteorological stations in Egypt

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  • Tadros, M.T.Y.

Abstract

The climatological Ångstrom regression coefficients have been determined by three methods and used to predict the global solar radiation over eight meteorological stations. Each of the three methods depends on the correlation between two ratios. The first ratio is between the long period of monthly average sunshine duration s̄ and the corresponding maximum of daily sunshine duration (day length) N, and the second ratio is between the measured monthly average daily global solar radiation H̄ and the corresponding monthly mean daily extraterrestrial solar radiation on the horizontal surface H̄0. A comparison between the measured data and the estimated values has been done. The t-statistics is used as a statistical indicator to choose the coefficients of the best method that gives a percentage of error less than 10%.

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  • Tadros, M.T.Y., 2000. "Uses of sunshine duration to estimate the global solar radiation over eight meteorological stations in Egypt," Renewable Energy, Elsevier, vol. 21(2), pages 231-246.
    • Handle: RePEc:eee:renene:v:21:y:2000:i:2:p:231-246
    DOI: 10.1016/S0960-1481(00)00009-4
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    Cited by:

    1. Li, Huashan & Lian, Yongwang & Wang, Xianlong & Ma, Weibin & Zhao, Liang, 2011. "Solar constant values for estimating solar radiation," Energy, Elsevier, vol. 36(3), pages 1785-1789.
    2. 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.
    3. Robaa, S.M., 2008. "Evaluation of sunshine duration from cloud data in Egypt," Energy, Elsevier, vol. 33(5), pages 785-795.
    4. El Ouderni, Ahmed Ridha & Maatallah, Taher & El Alimi, Souheil & Ben Nassrallah, Sassi, 2013. "Experimental assessment of the solar energy potential in the gulf of Tunis, Tunisia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 20(C), pages 155-168.
    5. Jiang, Yingni, 2009. "Computation of monthly mean daily global solar radiation in China using artificial neural networks and comparison with other empirical models," Energy, Elsevier, vol. 34(9), pages 1276-1283.
    6. Armstrong, S. & Hurley, W.G., 2010. "A new methodology to optimise solar energy extraction under cloudy conditions," Renewable Energy, Elsevier, vol. 35(4), pages 780-787.

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