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General formula for the estimation of global solar radiation on earth’s surface around the globe

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  • Korachagaon, Iranna
  • Bapat, V.N.

Abstract

The data such as global solar radiation, air temperature, relative humidity, wind and moisture, was collected from 875 stations around the globe. Of which data from 210 stations fairly spread on the earth surface was used to develop the formula for estimating the monthly average daily global radiation on a horizontal surface. In this study, using air temperature, relative humidity, wind, moisture and few derived parameters as independent variables, the most accurate equations have been obtained. The results show that the general formula developed could be used for the estimation of solar radiation with the local site parameters. Thus developed models have been validated with remaining 665 data sites. Finally two candidate models have been proposed. These models are capable of covering 50% of the land area on earth surface between latitude ±30°, enabling estimation accuracy to 93% of sites, with an estimation error (RMSE) limiting to 15%. Thus it is envisaged that, the proposed equations (models) can be used to estimate the monthly average daily global solar radiation in area where the radiation data is missing or not available. This helps in assessing the solar energy potential over necessitated area.

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  • Korachagaon, Iranna & Bapat, V.N., 2012. "General formula for the estimation of global solar radiation on earth’s surface around the globe," Renewable Energy, Elsevier, vol. 41(C), pages 394-400.
    • Handle: RePEc:eee:renene:v:41:y:2012:i:c:p:394-400
    DOI: 10.1016/j.renene.2011.11.002
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    Cited by:

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    2. 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).
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    4. 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.
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    9. Rivero, M. & Orozco, S. & Sellschopp, F.S. & Loera-Palomo, R., 2017. "A new methodology to extend the validity of the Hargreaves-Samani model to estimate global solar radiation in different climates: Case study Mexico," Renewable Energy, Elsevier, vol. 114(PB), pages 1340-1352.
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