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Support vector regression based prediction of global solar radiation on a horizontal surface

机译：基于支持向量回归的水平面上全球太阳辐射的预测

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摘要

In this paper, the support vector regression (SVR) methodology was adopted to estimate the horizontal global solar radiation (HGSR) based upon sunshine hours (n) and maximum possible sunshine hours (N) as input parameters. The capability of two SVRs of radial basis function (rbf) and polynomial basis function (poly) was investigated and compared with the conventional sunshine duration-based empirical models. For this purpose, long-term measured data for a city situated in sunny part of Iran was utilized. Exploration was performed on both daily and monthly mean scales to accomplish a more complete analysis. Through a statistical comparative study, using 6 well-known statistical parameters, the results proved the superiority of developed SVR models over the empirical models. Also, SVR-rbf outperformed the SVR-poly in terms of accuracy. For SVR-rbf model on daily estimation, the mean absolute percentage error, mean absolute bias error, root mean square error, relative root mean square error and coefficient of determination were 10.4466%, 1.2524 MJ/m~2, 2.0046 MJ/m~2, 9.0343% and 0.9133, respectively. Also, on monthly mean estimation the values were 1.4078%, 0.2845 MJ/m~2, 0.45044 MJ/m~2, 2.2576% and 0.9949, respectively. The achieved results conclusively demonstrated that the SVR-rbf is highly qualified for HGSR estimation using n and N.
机译：本文采用支持向量回归（SVR）方法，以日照时数（n）和最大可能日照时数（N）为输入参数，估计水平全球太阳辐射（HGSR）。研究了两个具有径向基函数（rbf）和多项式基函数（poly）的SVR的功能，并将其与传统的基于日照时长的经验模型进行了比较。为此，利用了位于伊朗晴区的一个城市的长期测量数据。每天和每月的平均量表都进行了探索，以完成更完整的分析。通过统计比较研究，使用6个著名的统计参数，结果证明了开发的SVR模型优于经验模型。同样，SVR-rbf在准确性方面也优于SVR-poly。对于每日估计的SVR-rbf模型，平均绝对百分比误差，平均绝对偏差误差，均方根误差，相对均方根误差和确定系数为10.4466％，1.2524 MJ / m〜2、2.0046 MJ / m〜 2，分别为9.0343％和0.9133。此外，按月平均估算值分别为1.4078％，0.2845 MJ / m〜2、0.45044 MJ / m〜2、2.2576％和0.9949。所获得的结果最终证明SVR-rbf非常适合使用n和N进行HGSR估算。

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