The prediction of global solar radiation for the regions is of great importance in terms of giving directions of solar energy conversion systems (design, modeling, and operation), selection of proper regions, and even future investment policies of the decision-makers. With this viewpoint, the objective of this paper is to predict daily global solar radiation data of four provinces (Kırklareli, Tokat, Nevşehir and Karaman) which have different solar radiation distribution in Turkey. In the study, four different machine learning algorithms (support vector machine (SVM), artificial neural network (ANN), kernel and nearest-neighbor (k-NN), and deep learning (DL)) are used. In the training of these algorithms, daily minimum and maximum ambient temperature, cloud cover, daily extraterrestrial solar radiation, day length and solar radiation of these provinces are used. The data is supplied from the Turkish State Meteorological Service and cover the last two years (01.01.2018–31.12.2019). To decide on the success of these algorithms, seven different statistical metrics (R², RMSE, rRMSE, MBE, MABE, t-stat, and MAPE) are discussed in the study. The results shows that R², MABE, and RMSE values of all algorithms are ranging from 0.855 to 0.936, from 1.870 to 2.328 MJ/m², from 2.273 to 2.820 MJ/m², respectively. At all cases, k-NN exhibited the worst result in terms of R², RMSE, and MABE metrics. Of all the models, DL was the only model that exceeded the t-critic value. In conclusion, the present paper is reporting that all machine learning algorithms tested in this study can be used in the prediction of daily global solar radiation data with a high accuracy; however, the ANN algorithm is the best fitting algorithm among all algorithms. Then it is followed by DL, SVM and k-NN, respectively.

就提供太阳能转换系统的方向（设计，建模和操作），选择合适的区域，甚至决策者的未来投资政策而言，对这些地区的全球太阳辐射的预测非常重要。基于此观点，本文的目的是预测土耳其太阳辐射分布不同的四个省（克尔克拉雷利，托卡特，内夫谢希尔和卡拉曼）的每日全球太阳辐射数据。在这项研究中，使用了四种不同的机器学习算法（支持向量机（SVM），人工神经网络（ANN），内核和最近邻居（k-NN）以及深度学习（DL））。在这些算法的训练中，每天的最低和最高环境温度，云量，每日的地外太阳辐射，使用这些省份的日长和太阳辐射。数据由土耳其国家气象局提供，涵盖了过去两年（01.01.2018–31.12.2019）。要决定这些算法的成功与否，请使用七个不同的统计指标（R²，研究中讨论了RMSE，rRMSE，MBE，MABE，t-stat和MAPE）。结果表明，所有算法的R ²，MABE和RMSE值分别在0.855至0.936，从1.870至2.328 MJ / m ²，从2.273至2.820 MJ / m ^{2的范围内}。在所有情况下，就R ²，RMSE和MABE度量而言，k-NN的结果最差。在所有模型中，DL是唯一超过t临界值的模型。总而言之，本文报道了在这项研究中测试的所有机器学习算法都可以用于高精度地预测每日全球太阳辐射数据。然而，在所有算法中，ANN算法都是最佳拟合算法。然后分别是DL，SVM和k-NN。