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Correlationships between clearness index and relative sunshine duration for Sudan

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  • Elagib, N.a.
  • Alvi, S.H.
  • Mansell, M.G.

Abstract

The clearness index for 16 meteorological stations in Sudan has been expressed in terms of the fraction of bright sunshine duration, applying a variety of regression forms, namely linear, exponential, power and second-order polynomial. The significance and performance characteristics of the relations have been viewed using several statistical procedures. Although all of the four relationships are empirical and exhibit relative discrepancies, it has been found that they can reasonably be used to represent the underlying effect of relative duration of sunshine on clearness index for mean values. The estimates of the relationships are statistically significant at the 99.9% confidence level. For around 85% of the cases, the absolute error is fairly less than 4%. No estimate is found to fall beyond about ±10% accuracy level. Unless otherwise investigated under extreme conditions, especially S/So = 0 and 1, the predictive value of the relations remains questionable for values of S/So outside the ranges covered herein. From the bulk of the data available, the important finding is that solar radiation is plentiful in this vast country and could be regarded as an essential, renewable source of energy for various applications.

Suggested Citation

  • Elagib, N.a. & Alvi, S.H. & Mansell, M.G., 1999. "Correlationships between clearness index and relative sunshine duration for Sudan," Renewable Energy, Elsevier, vol. 17(4), pages 473-498.
    • Handle: RePEc:eee:renene:v:17:y:1999:i:4:p:473-498
    DOI: 10.1016/S0960-1481(98)00773-3
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    References listed on IDEAS

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    1. Topçu, S. & Dİlmaç, S. & Aslan, Z., 1995. "Study of hourly solar radiation data in Istanbul," Renewable Energy, Elsevier, vol. 6(2), pages 171-174.
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    1. Wan, Kevin K.W. & Tang, H.L. & Yang, Liu & Lam, Joseph C., 2008. "An analysis of thermal and solar zone radiation models using an Angstrom–Prescott equation and artificial neural networks," Energy, Elsevier, vol. 33(7), pages 1115-1127.
    2. Yao, Wanxiang & Zhang, Chunxiao & Hao, Haodong & Wang, Xiao & Li, Xianli, 2018. "A support vector machine approach to estimate global solar radiation with the influence of fog and haze," Renewable Energy, Elsevier, vol. 128(PA), pages 155-162.
    3. Ayvazoğluyüksel, Özge & Filik, Ümmühan Başaran, 2018. "Estimation methods of global solar radiation, cell temperature and solar power forecasting: A review and case study in Eskişehir," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 639-653.
    4. Li, Han & Li, Jinchao & Kong, Xiangfei & Long, Hao & Yang, Hua & Yao, Chengqiang, 2020. "A novel solar thermal system combining with active phase-change material heat storage wall (STS-APHSW): Dynamic model, validation and thermal performance," Energy, Elsevier, vol. 201(C).
    5. Yao, Wanxiang & Zhang, Kang & Cao, Weixue & Li, Xianli & Wang, Yan & Wang, Xiao, 2022. "Research on the correlation between solar radiation and sky luminance based on the principle of photothermal integration," Renewable Energy, Elsevier, vol. 194(C), pages 1326-1342.
    6. Li, Han & You, Shijun & Zhang, Huan & Zheng, Wandong & Zheng, Xuejing & Jia, Jie & Ye, Tianzhen & Zou, Lanjun, 2017. "Modelling of AQI related to building space heating energy demand based on big data analytics," Applied Energy, Elsevier, vol. 203(C), pages 57-71.
    7. Zhao, Qun & Yao, Wanxiang & Zhang, Chunxiao & Wang, Xiao & Wang, Yan, 2019. "Study on the influence of fog and haze on solar radiation based on scattering-weakening effect," Renewable Energy, Elsevier, vol. 134(C), pages 178-185.
    8. Lam, Joseph C. & Wan, Kevin K.W. & Lau, Chris C.S. & Yang, Liu, 2008. "Climatic influences on solar modelling in China," Renewable Energy, Elsevier, vol. 33(7), pages 1591-1604.
    9. Yao, Wanxiang & Song, Mengjia & Huang, Yu & Xu, Puyan & Li, Xianli & Su, Gang & Gao, Weijun, 2024. "A new anisotropic solar radiation model based on the principle of photothermal integration," Renewable Energy, Elsevier, vol. 226(C).
    10. Zhenyu Wang & Cuixia Tian & Qibing Zhu & Min Huang, 2018. "Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition," Energies, MDPI, vol. 11(1), pages 1-21, January.
    11. Makade, Rahul G. & Jamil, Basharat, 2018. "Statistical analysis of sunshine based global solar radiation (GSR) models for tropical wet and dry climatic Region in Nagpur, India: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 87(C), pages 22-43.

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