This research aimed to find a proper model to estimate daily solar radiation in terms of specific climate conditions of 51 weather stations over Iran. Twenty-one sunshine-based empirical models were calibrated and evaluated. Two different quality control methods were used to reject data error in daily solar radiation dataset collected from 2007 to 2017. The empirical constant coefficients were calculated using linear and nonlinear regression models. Statistical error comparisons between observation and models-based estimates were performed via R², RMSE, RRMSE, MAPE, MBE and MAE measures. Finally, following the models-based estimates, monthly solar radiation maps were created for the entire country using ordinary Kriging interpolation method. The results revealed that for the largest part of the regions, the highest correlation between Observation and Models-Based Estimates was detected while implementing regression-based models (models 4 and 12). However, the highest correlation between the values of the measured radiation and the models-based estimates over the entire country belonged to the linear model 15. Moreover, there was an opposite relationship between “a” and “b” coefficients in model 15. The results of models 4 and 12 show that these models can perform well for estimating solar radiation in the “C” and “F” climate zones, respectively.

这项研究旨在找到一个合适的模型，根据伊朗51个气象站的特定​​气候条件估算每日太阳辐射。校准和评估了二十一个基于阳光的经验模型。在2007年至2017年收集的日太阳辐射数据集中，使用两种不同的质量控制方法来拒绝数据误差。使用线性和非线性回归模型计算经验常数系数。观测值与基于模型的估计值之间的统计误差比较是通过R ^2进行的，RMSE，RRMSE，MAPE，MBE和MAE措施。最后，根据基于模型的估计，使用普通的克里格插值方法为整个国家创建了每月太阳辐射图。结果表明，在实施基于回归的模型（模型4和12）的过程中，对于大部分地区而言，观测值与基于模型的估计值之间的相关性最高。但是，在整个国家范围内，实测辐射值与基于模型的估计值之间的最高相关性属于线性模型15。此外，模型15中的“ a”和“ b”系数之间存在相反的关系。模型4和12的结果表明，这些模型分别可以很好地估计“ C”和“ F”气候区的太阳辐射。