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A new correlation between clearness index and relative sunshine

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  • Coppolino, S.

Abstract

A new correlation between the clearness index Kt and the relative sunshine s/S is proposed for computing the monthly mean daily global radiation G(MJ m−2 day−1) at Italian locations. This equation is: Kt = 0.67(s/S)0.45 (sin hn)0.05 0.15 ⩽ s/S ⩽ 0.90, where Kt = G/Go; Go(MJ m−2 day−1) and hn (degrees) are the monthly mean daily extraterrestrial radiation and the noon altitude of the sun on the 15th of the month, respectively; s and S (hrs) are the monthly mean daily values of the real and theoretical sunshine duration, respectively. This formula is tested using data from 34 Italian stations displaced at various latitudes or elevations above sea level or climatic and geographical situations (inland or coastal site of the Mediterranean, Tyrrhenian, Adriatic or Ligurian Sea). The test results generally show low deviations between the measured and computed values of G with the exception of Pian Rosà, whose elevation is 3488 m. For 32 locations the maximum per cent standard error of estimate, Φ(%), is less than 10. Moreover, a comparison among the values of G computed for each tested location by the above formula, and two other correlations with measured data, points out that the per cent standard error of estimate Φ(%) referring to the present equation is better than ones obtained for the other considered correlations for 16 analysed locations, whereas it is almost equal for eight other locations. Consequently the correlation herein proposed can be considered suitable and useful to predict, with a high degree of accuracy, the monthly mean daily global radiation, even at Italian locations lacking in actinometric stations but provided with measured data of sunshine duration, provided the elevation of the place is less than approximately 2500 m.

Suggested Citation

  • Coppolino, S., 1994. "A new correlation between clearness index and relative sunshine," Renewable Energy, Elsevier, vol. 4(4), pages 417-423.
  • Handle: RePEc:eee:renene:v:4:y:1994:i:4:p:417-423
    DOI: 10.1016/0960-1481(94)90049-3
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    Cited by:

    1. Chen, Ji-Long & He, Lei & Yang, Hong & Ma, Maohua & Chen, Qiao & Wu, Sheng-Jun & Xiao, Zuo-lin, 2019. "Empirical models for estimating monthly global solar radiation: A most comprehensive review and comparative case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 91-111.
    2. Dorvlo, Atsu S.S & Ampratwum, David B, 1999. "Technical note Modelling of weather data for Oman," Renewable Energy, Elsevier, vol. 17(3), pages 421-428.
    3. 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.
    4. Ampratwum, David B. & Dorvlo, Atsu S. S., 1999. "Estimation of solar radiation from the number of sunshine hours," Applied Energy, Elsevier, vol. 63(3), pages 161-167, July.
    5. Halawa, Edward & GhaffarianHoseini, AmirHosein & Hin Wa Li, Danny, 2014. "Empirical correlations as a means for estimating monthly average daily global radiation: A critical overview," Renewable Energy, Elsevier, vol. 72(C), pages 149-153.
    6. Anton Vernet & Alexandre Fabregat, 2023. "Evaluation of Empirical Daily Solar Radiation Models for the Northeast Coast of the Iberian Peninsula," Energies, MDPI, vol. 16(6), pages 1-18, March.
    7. Dorvlo, Atsu S.S. & Ampratwum, David B., 2000. "Harmonic analysis of global irradiation," Renewable Energy, Elsevier, vol. 20(4), pages 435-443.
    8. Doorga, Jay R.S. & Rughooputh, Soonil D.D.V. & Boojhawon, Ravindra, 2019. "Modelling the global solar radiation climate of Mauritius using regression techniques," Renewable Energy, Elsevier, vol. 131(C), pages 861-878.
    9. Alnaser, W.E. & Merzaa, M.K., 2005. "Empirical correlations for the climate data in the Kingdom of Bahrain," Energy, Elsevier, vol. 30(15), pages 2818-2830.
    10. Dorvlo, Atsu S. S. & Jervase, Joseph A. & Al-Lawati, Ali, 2002. "Solar radiation estimation using artificial neural networks," Applied Energy, Elsevier, vol. 71(4), pages 307-319, April.

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