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Empirical models for estimating monthly global solar radiation: A most comprehensive review and comparative case study in China

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  • Chen, Ji-Long
  • He, Lei
  • Yang, Hong
  • Ma, Maohua
  • Chen, Qiao
  • Wu, Sheng-Jun
  • Xiao, Zuo-lin

Abstract

Global solar radiation is a core component of scientific research and engineering application across a broad spectrum. However, its measurement is limited by a small number of stations due to the technical and financial restricts. Estimating solar radiation with the meteorological variables using empirical models is of benefit to obtain solar radiation data at global scale. Yet, there are various options of available empirical models to select the most suitable one. This study conducted a most comprehensive collection and review of empirical models employing the commonly measured meteorological variables and geographic factors. A total of 294 different types of empirical models were collected and classified into 37 groups according to input attributes. Such collection built an empirical model library providing an overall overview of the developed empirical models in literatures. Furthermore, the collected models were calibrated and evaluated at three meteorological stations in the Three Gorges Reservoir Area in China. This study suggests that these model-comparing processes can assist the governments, scientists and engineers in tailoring the most fitted model for specific applications and in particular areas.

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  • 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.
    • Handle: RePEc:eee:rensus:v:108:y:2019:i:c:p:91-111
    DOI: 10.1016/j.rser.2019.03.033
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    11. 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).
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    16. Jesús-Ignacio Prieto & David García & Ruth Santoro, 2022. "Comparative Analysis of Accuracy, Simplicity and Generality of Temperature-Based Global Solar Radiation Models: Application to the Solar Map of Asturias," Sustainability, MDPI, vol. 14(11), pages 1-29, May.

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