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The importance of maximum air temperature in the parameterisation of solar radiation in Nigeria

Author

Listed:
  • Ododo, J.C.
  • Sulaiman, A.T.
  • Aidan, J.
  • Yuguda, M.M.
  • Ogbu, F.A.

Abstract

Using existing and new empirical model equations to analyse available data for nine stations located in different geographical and climatic zones in Nigeria, it is clearly demonstrated that maximum air temperature is an important climatological parameter which should be used in solar radiation modelling in Nigeria. It is also shown that seasonal variations in the values of the model parameters are significant.

Suggested Citation

  • Ododo, J.C. & Sulaiman, A.T. & Aidan, J. & Yuguda, M.M. & Ogbu, F.A., 1995. "The importance of maximum air temperature in the parameterisation of solar radiation in Nigeria," Renewable Energy, Elsevier, vol. 6(7), pages 751-763.
    • Handle: RePEc:eee:renene:v:6:y:1995:i:7:p:751-763
    DOI: 10.1016/0960-1481(94)00097-P
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    Cited by:

    1. 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.
    2. Yıldırım, H. Başak & Teke, Ahmet & Antonanzas-Torres, Fernando, 2018. "Evaluation of classical parametric models for estimating solar radiation in the Eastern Mediterranean region of Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2053-2065.
    3. Kaba, Kazım & Sarıgül, Mehmet & Avcı, Mutlu & Kandırmaz, H. Mustafa, 2018. "Estimation of daily global solar radiation using deep learning model," Energy, Elsevier, vol. 162(C), pages 126-135.
    4. Ajayi, Oluseyi O, 2013. "Sustainable energy development and environmental protection: Implication for selected states in West Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 532-539.
    5. Liu, Xiaoying & Xu, Yinlong & Zhong, Xiuli & Zhang, Wenying & Porter, John Roy & Liu, Wenli, 2012. "Assessing models for parameters of the Ångström–Prescott formula in China," Applied Energy, Elsevier, vol. 96(C), pages 327-338.
    6. Njau, Ernest C., 1997. "A new analytical model for temperature predictions," Renewable Energy, Elsevier, vol. 11(1), pages 61-68.
    7. 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.
    8. Njau, Ernest C., 1998. "Amplitude-modulating periodicities in global and regional heat/temperature variations," Renewable Energy, Elsevier, vol. 13(3), pages 295-303.
    9. Mohammadi, Kasra & Shamshirband, Shahaboddin & Kamsin, Amirrudin & Lai, P.C. & Mansor, Zulkefli, 2016. "Identifying the most significant input parameters for predicting global solar radiation using an ANFIS selection procedure," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 423-434.
    10. Njau, E.c., 1999. "Differential variations of maximum and minimum temperatures," Renewable Energy, Elsevier, vol. 18(2), pages 147-155.
    11. 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.
    12. Zang, Haixiang & Jiang, Xin & Cheng, LiLin & Zhang, Fengchun & Wei, Zhinong & Sun, Guoqiang, 2022. "Combined empirical and machine learning modeling method for estimation of daily global solar radiation for general meteorological observation stations," Renewable Energy, Elsevier, vol. 195(C), pages 795-808.
    13. Akarslan, Emre & Hocaoglu, Fatih Onur & Edizkan, Rifat, 2018. "Novel short term solar irradiance forecasting models," Renewable Energy, Elsevier, vol. 123(C), pages 58-66.
    14. Prieto, Jesús-Ignacio & García, David, 2022. "Global solar radiation models: A critical review from the point of view of homogeneity and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    15. Shamshirband, Shahaboddin & Mohammadi, Kasra & Yee, Por Lip & Petković, Dalibor & Mostafaeipour, Ali, 2015. "A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1031-1042.
    16. Xiang Yu, 2023. "Evaluating parallelized support vector regression and nearest neighbor regression with different input variations for estimating daily global solar radiation of the humid subtropical region in China," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 18, pages 95-110.
    17. Robaa, S.M., 2008. "Evaluation of sunshine duration from cloud data in Egypt," Energy, Elsevier, vol. 33(5), pages 785-795.
    18. Paniagua-Tineo, A. & Salcedo-Sanz, S. & Casanova-Mateo, C. & Ortiz-García, E.G. & Cony, M.A. & Hernández-Martín, E., 2011. "Prediction of daily maximum temperature using a support vector regression algorithm," Renewable Energy, Elsevier, vol. 36(11), pages 3054-3060.
    19. Yohanna, Jonathan K. & Itodo, Isaac N. & Umogbai, Victor I., 2011. "A model for determining the global solar radiation for Makurdi, Nigeria," Renewable Energy, Elsevier, vol. 36(7), pages 1989-1992.

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