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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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    5. Qin, Wenmin & Wang, Lunche & Lin, Aiwen & Zhang, Ming & Xia, Xiangao & Hu, Bo & Niu, Zigeng, 2018. "Comparison of deterministic and data-driven models for solar radiation estimation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 579-594.
    6. Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    7. Feng, Lan & Lin, Aiwen & Wang, Lunche & Qin, Wenmin & Gong, Wei, 2018. "Evaluation of sunshine-based models for predicting diffuse solar radiation in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 168-182.
    8. Fan, Junliang & Chen, Baiquan & Wu, Lifeng & Zhang, Fucang & Lu, Xianghui & Xiang, Youzhen, 2018. "Evaluation and development of temperature-based empirical models for estimating daily global solar radiation in humid regions," Energy, Elsevier, vol. 144(C), pages 903-914.
    9. 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.
    10. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    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.
    12. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2020. "Application of functional deep belief network for estimating daily global solar radiation: A case study in China," Energy, Elsevier, vol. 191(C).

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