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Estimating monthly average daily diffuse solar radiation with multiple predictors: A case study

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  • Li, Huashan
  • Ma, Weibin
  • Wang, Xianlong
  • Lian, Yongwang

Abstract

Solar radiation measurements are not easily available, especially for the diffuse solar radiation. In this study, two models for estimating the diffuse solar radiation are proposed based on multiple predictors including the clearness index, relative sunshine duration, ambient temperature and relative humidity. One of them aims to increase the estimation accuracy, and the other aims to estimate the diffuse solar radiation direct from other meteorological elements in the absence of the global solar radiation. For a case study, the performance of the proposed models is validated by comparing with eight existing models selected from literature against the measured data at Guangzhou station in China. Through the analysis based on statistical error tests, results show that the two models can estimate the monthly average daily diffuse solar radiation with good accuracy.

Suggested Citation

  • Li, Huashan & Ma, Weibin & Wang, Xianlong & Lian, Yongwang, 2011. "Estimating monthly average daily diffuse solar radiation with multiple predictors: A case study," Renewable Energy, Elsevier, vol. 36(7), pages 1944-1948.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:7:p:1944-1948
    DOI: 10.1016/j.renene.2011.01.006
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    References listed on IDEAS

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    1. Munawwar, Saima & Muneer, Tariq, 2007. "Statistical approach to the proposition and validation of daily diffuse irradiation models," Applied Energy, Elsevier, vol. 84(4), pages 455-475, April.
    2. Ertekin, Can & Yaldız, Osman, 1999. "Estimation of monthly average daily global radiation on horizontal surface for Antalya (Turkey)," Renewable Energy, Elsevier, vol. 17(1), pages 95-102.
    3. Jiang, Yingni, 2009. "Estimation of monthly mean daily diffuse radiation in China," Applied Energy, Elsevier, vol. 86(9), pages 1458-1464, September.
    4. Trabea, A.A. & Shaltout, M.A.Mosalam, 2000. "Correlation of global solar radiation with meteorological parameters over Egypt," Renewable Energy, Elsevier, vol. 21(2), pages 297-308.
    5. Pandey, Chanchal Kumar & Katiyar, A.K., 2009. "A comparative study to estimate daily diffuse solar radiation over India," Energy, Elsevier, vol. 34(11), pages 1792-1796.
    6. Janjai, S. & Praditwong, P. & Moonin, C., 1996. "A new model for computing monthly average daily diffuse radiation for Bangkok," Renewable Energy, Elsevier, vol. 9(1), pages 1283-1286.
    7. El-Sebaii, A.A. & Al-Hazmi, F.S. & Al-Ghamdi, A.A. & Yaghmour, S.J., 2010. "Global, direct and diffuse solar radiation on horizontal and tilted surfaces in Jeddah, Saudi Arabia," Applied Energy, Elsevier, vol. 87(2), pages 568-576, February.
    8. Boland, John & Ridley, Barbara & Brown, Bruce, 2008. "Models of diffuse solar radiation," Renewable Energy, Elsevier, vol. 33(4), pages 575-584.
    9. Ridley, Barbara & Boland, John & Lauret, Philippe, 2010. "Modelling of diffuse solar fraction with multiple predictors," Renewable Energy, Elsevier, vol. 35(2), pages 478-483.
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