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Modeling daily global solar radiation using only temperature data: Past, development, and future

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  • Qiu, Rangjian
  • Li, Longan
  • Wu, Lifeng
  • Agathokleous, Evgenios
  • Liu, Chunwei
  • Zhang, Baozhong
  • Luo, Yufeng
  • Sun, Shanlei

Abstract

Accurate determination on global solar radiation (Rs) is essential in many disciplines and sectors. This study comprehensively reviewed 78 existing models and developed 4 new models (N1-1∼N1-4) for estimating daily Rs using only temperature data. The best model was then applied to forecast daily Rs using temperature data in weather forecast. All models were calibrated and tested using data collected from 105 radiation stations scattered across China. Daily data of temperature were also collected from 703 additional meteorological stations to classify diurnal temperature range (ΔT) zones in China. The results showed that China could be divided into five ΔT zones. The newly developed model N1-4 was the most accurate among all models for various ΔT zones, with values of coefficient of determination, index of agreement, mean bias error, mean absolute bias error, root mean square error, and relative root mean square error in the ranges of 0.660–0.737, 0.888–0.919, −0.023–0.100 MJ m−2 d−1, 2.691–3.195 MJ m−2 d−1, 3.725–4.231 MJ m−2 d−1, and 0.224–0.352, respectively. The generalized coefficients of the N1-4 model were also derived and further used for estimating daily Rs in areas with or without Rs data when there are only temperature data. The model N1-4 was successfully applied to predict daily Rs in varying ΔT zones with forecast temperature data for a lead time of 1–8 days. This new model is recommended for forecasting daily Rs in zones with high ΔT for a long lead time and in zones with low ΔT for a short lead time.

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  • Qiu, Rangjian & Li, Longan & Wu, Lifeng & Agathokleous, Evgenios & Liu, Chunwei & Zhang, Baozhong & Luo, Yufeng & Sun, Shanlei, 2022. "Modeling daily global solar radiation using only temperature data: Past, development, and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    • Handle: RePEc:eee:rensus:v:163:y:2022:i:c:s1364032122004154
    DOI: 10.1016/j.rser.2022.112511
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