The precise estimation of solar radiation is of great importance in solar energy applications with respect to installation and capacity. In estimate modelling on selected target locations, various computer-based and experimental methods and techniques are employed. In the present study, the Multilayer Feed-Forward Neural Network (MFFNN), - Nearest Neighbors ( - NN), a Library for Support Vector Machines (LibSVM), and M5 rules algorithms, which are among the Machine Learning (ML) algorithms, were used to estimate the hourly average solar radiation of two geographic locations on the same latitude. The input variables that had the most impact on solar radiation were identified and grouped as a result of 29 different applications that were developed by using 6 different feature selection methods with Waikato Environment for Knowledge Analysis (WEKA) software. Estimation models were developed by using the selected data groups and all input variables for each target location. The results show that the estimations developed with the feature selection method were more successful for target locations, and the radiation potentials were similar. The performance of the estimation models was evaluated by comparing each model with different statistical indicators and with previous studies. According to the RMSE, MAE, , and SMAPE statistical scales, the results of the most successful estimation models that were developed with MFFNN were 0.0508-0.0536, 0.0341-0.0352, 0.9488-0.9656, and 7.77%-7.79%, respectively.

中文翻译：

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使用机器学习算法有效估计每小时全球太阳辐射

太阳辐射的精确估计在太阳能应用中就安装和容量而言非常重要。在选定目标位置的估计建模中，采用了各种基于计算机和实验的方法和技术。在本研究中，多层前馈神经网络 (MFFNN)、最近邻 (-NN)、支持向量机库 (LibSVM) 和 M5 规则算法，属于机器学习 (ML) 算法，用于估计同一纬度上两个地理位置的每小时平均太阳辐射。通过使用 6 种不同的特征选择方法和怀卡托知识分析环境 (WEKA) 软件开发的 29 种不同应用程序，对太阳辐射影响最大的输入变量被识别和分组。估计模型是通过使用选定的数据组和每个目标位置的所有输入变量来开发的。结果表明，使用特征选择方法开发的估计对于目标位置更成功，并且辐射电位相似。通过将每个模型与不同的统计指标和以前的研究进行比较来评估估计模型的性能。根据 RMSE、MAE、... 和 SMAPE 统计量表，使用 MFFNN 开发的最成功的估计模型的结果为 0.0508-0.0536，