Developing of the new models in solar radiation estimation with curve fitting based on moving least-squares approximation

Ayse Gul Kaplan and Yusuf Alper Kaplan

Renewable Energy, 2020, vol. 146, issue C, 2462-2471

Abstract: Recently, serious progress has been made in solar energy applications in developed and developing countries. Solar radiation on a horizontal surface is the basic parameter required for the design of solar energy systems and for evaluating the system performance. Therefore, solar radiation the exact determination of the amount of land in different latitudes on the earth's surface is of great importance in many solar energy applications. In this study, Angstorm coefficients were determined by Moving Least Squares Approximation (MLSA). Three different models were obtained by using the moving least squares method. In this study, new empirical models were developed for determining the monthly average daily global solar radiation on a horizontal surface for Antalya. The developed models were compared with the models in the literature by using different error analysis methods. The statistical compatibility of the investigated models was tested and the model closest to the measurements was determined. Although, this study concluded that the suggested methods are applicable to estimate the monthly average daily diffuse radiation on a horizontal surface for selected region, it has been observed that the performance of these models varies according to years and the error analysis test used. If the results are generally evaluated the developed linear model showed the best performance to estimate the global solar radiation on a horizontal surface for Antalya. Also, among the models used in the literature, the Togrul model showed the best performance for selected region.

Keywords: Solar energy; Solar radiation models; Statistical tests; Solar radiation in Antalya; Turkey (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:146:y:2020:i:c:p:2462-2471

DOI: 10.1016/j.renene.2019.08.095

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