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A comprehensive review of empirical models for estimating global solar radiation in Africa

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  • Samuel Chukwujindu, Nwokolo

Abstract

The accurate knowledge of global solar radiation is of vital requirement for surveys in agronomy, hydrology, ecology, sizing of the photovoltaic or thermal solar systems, solar architecture, molten salt power plant and supplying energy to natural processes like photosynthesis and estimates of their performances. However, measurement of global solar radiation is not available in most locations across Africa. During the past 36 years in order to estimate global solar radiation on the horizontal surface on both daily and monthly mean daily basis, numerous empirical models have been developed for several locations in Africa. As a result various input parameters have been utilized and different functional forms used. In this study aim at classifying and reviewing the empirical models employed for estimating global solar radiation in Africa. The empirical models so far utilized were classified into six main categories and presented based on the input parameters employed. The models were further reclassified into several main sub-classes (groups) and finally represented according to their developing year. On the whole, 732 empirical models and 65 functional forms were recorded in literature for estimating global solar radiation in Africa in this review. Thus, this review would provide solar energy researchers in terms of identifying the input parameters and functional forms widely employed up till now as well as recognizing their importance for estimating global solar radiation in several locations in Africa.

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  • Samuel Chukwujindu, Nwokolo, 2017. "A comprehensive review of empirical models for estimating global solar radiation in Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 955-995.
    • Handle: RePEc:eee:rensus:v:78:y:2017:i:c:p:955-995
    DOI: 10.1016/j.rser.2017.04.101
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    1. Khorasanizadeh, Hossein & Mohammadi, Kasra, 2016. "Diffuse solar radiation on a horizontal surface: Reviewing and categorizing the empirical models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 338-362.
    2. Said, R. & Mansor, M. & Abuain, T., 1998. "Estimation of global and diffuse radiation at Tripoli," Renewable Energy, Elsevier, vol. 14(1), pages 221-227.
    3. 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.
    4. Bahel, V. & Srinivasan, R. & Bakhsh, H., 1987. "Statistical comparison of correlations for estimation of global horizontal solar radiation," Energy, Elsevier, vol. 12(12), pages 1309-1316.
    5. Olatomiwa, Lanre & Mekhilef, Saad & Shamshirband, Shahaboddin & Petković, Dalibor, 2015. "Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1784-1791.
    6. Mohanty, Sthitapragyan & Patra, Prashanta Kumar & Sahoo, Sudhansu Sekhar, 2016. "Prediction and application of solar radiation with soft computing over traditional and conventional approach – A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 778-796.
    7. 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.
    8. 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.
    9. Kumar, Rajesh & Aggarwal, R.K. & Sharma, J.D., 2015. "Comparison of regression and artificial neural network models for estimation of global solar radiations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1294-1299.
    10. Ullah, K.R. & Saidur, R. & Ping, H.W. & Akikur, R.K. & Shuvo, N.H., 2013. "A review of solar thermal refrigeration and cooling methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 499-513.
    11. Adeyefa, Z.D. & Adedokun, J.A., 1991. "Pyrheliometric determination of atmospheric turbidity in the harmattan season over Ile-Ife, Nigeria," Renewable Energy, Elsevier, vol. 1(3), pages 555-566.
    12. Bosch, J.L. & López, G. & Batlles, F.J., 2008. "Daily solar irradiation estimation over a mountainous area using artificial neural networks," Renewable Energy, Elsevier, vol. 33(7), pages 1622-1628.
    13. Thirugnanasambandam, Mirunalini & Iniyan, S. & Goic, Ranko, 2010. "A review of solar thermal technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 312-322, January.
    14. Zhang, Jianyuan & Zhao, Li & Deng, Shuai & Xu, Weicong & Zhang, Ying, 2017. "A critical review of the models used to estimate solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 314-329.
    15. Bahel, V. & Bakhsh, H. & Srinivasan, R., 1987. "A correlation for estimation of global solar radiation," Energy, Elsevier, vol. 12(2), pages 131-135.
    16. Adaramola, Muyiwa S., 2012. "Estimating global solar radiation using common meteorological data in Akure, Nigeria," Renewable Energy, Elsevier, vol. 47(C), pages 38-44.
    17. Will, A. & Bustos, J. & Bocco, M. & Gotay, J. & Lamelas, C., 2013. "On the use of niching genetic algorithms for variable selection in solar radiation estimation," Renewable Energy, Elsevier, vol. 50(C), pages 168-176.
    Full references (including those not matched with items on IDEAS)

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    11. Bouchouicha, Kada & Hassan, Muhammed A. & Bailek, Nadjem & Aoun, Nouar, 2019. "Estimating the global solar irradiation and optimizing the error estimates under Algerian desert climate," Renewable Energy, Elsevier, vol. 139(C), pages 844-858.
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