The selection of a suitable gridding method for the interpolation of the input dataset has always been a big challenge. In this study, principal component analysis (PCA) was employed to select a suitable interpolator for the mapping of solar irradiation. For this purpose, sunshine duration data obtained from 50 meteorological stations was used to predict solar irradiation based on the Angstrom–Prescott model. A total of nine gridding methods were utilized to interpolate monthly mean daily global solar irradiation (GHI). Results showed that the GHI values across the Pakistan ranged between 4.50 to 6.00 kWh/m²/day. In the SPSS software package (version 23), PCA was performed and Kriging was considered as the most suitable interpolation method among all interpolators used in this analysis. To validate PCA results, interpolated GHI maps were compared with the solar atlas developed by the World Bank’s Energy Sector Management Assistance Program (ESMAP). The estimated value of performance indicators showed that the Kriging had the comparatively lowest value of mean absolute error (MAE) and root-mean-square error (RMSE). The outcomes of this study will be useful in the analysis and planning of solar energy system at the local and regional level.

为输入数据集的内插选择合适的网格化方法一直是一个很大的挑战。在这项研究中，主要成分分析（PCA）用于选择合适的插值器来绘制太阳辐射图。为此，根据Angstrom-Prescott模型，使用了从50个气象站获得的日照时长数据来预测太阳辐射。总共使用了九种网格化方法来对月平均日总日照量（GHI）进行插值。结果表明，巴基斯坦的GHI值介于4.50至6.00 kWh / m ^2之间/天。在SPSS软件包（版本23）中，执行了PCA，在此分析中使用的所有插值器中，克里格被认为是最合适的插值方法。为了验证PCA结果，将内插的GHI地图与世界银行能源部门管理援助计划（ESMAP）开发的太阳图集进行了比较。性能指标的估计值表明，克里格法具有最低的平均绝对误差（MAE）和均方根误差（RMSE）。这项研究的结果将有助于在地方和区域层面对太阳能系统进行分析和规划。