Several hourly, daily and monthly global solar radiation prediction models have been designed, to overcome the weakness of the previous models. Many previous models have used a long-term global solar radiation of Seoul to predict the consecutive years. Unfortunately, many countries do not have an enough history to build such prediction models, in addition many researchers suggested that seasonal autoregressive integrated moving average (SARIMA) is better than nonlinear autoregressive exogenous (NARX) neural network in predicting global solar radiation. Therefore, this research comes to fill the gaps in previous work, develop prediction model based on short-term global solar radiation, and test the best model between NARX and SARIMA by using global solar radiation of Seoul. The methodology divided the developed models into two parts including train phase and test phase. Train phase used dataset between 2007 and 2013, where test phase used dataset between 2014 and 2015. Afterward, the developed models are validated and tested using determination coefficient (R²) and different error function and the results are compared to two previous model that used long-term dataset namely ANFIS model and SARIMA. The results showed that the determination coefficient (R²) and RMSE of NARX model based on hourly data are 0.95 and 0.23 MJ/m², respectively, besides the best daily and monthly average solar radiation predictors are obtained when NARX and hourly data are used. The results revealed that using hour, day, month and year as independent variables and less history with NARX model is efficient to predict two consecutive years.

已经设计了几个小时，每日和每月的全球太阳辐射预测模型，以克服先前模型的缺点。以前的许多模型都使用首尔的长期全球太阳辐射来预测连续几年。不幸的是，许多国家没有足够的历史来建立这样的预测模型，此外，许多研究人员提出，在预测全球太阳辐射方面，季节性自回归综合移动平均值（SARIMA）优于非线性自回归外生（NARX）神经网络。因此，这项研究填补了先前的工作空白，开发了基于短期全球太阳辐射的预测模型，并利用首尔的全球太阳辐射测试了NARX和SARIMA之间的最佳模型。该方法将开发的模型分为两个部分，包括训练阶段和测试阶段。训练阶段使用2007年至2013年之间的数据集，而测试阶段使用2014年至2015年之间的数据集。之后，使用确定系数对开发的模型进行验证和测试（R ²）和不同的误差函数，并将结果与​​使用长期数据集的两个先前模型（ANFIS模型和SARIMA）进行比较。结果表明，基于小时数据的NARX模型的确定系数（R ²）和RMSE分别为0.95和0.23 MJ / m ²，此外，当使用NARX和小时数据时，可以获得最佳的日和月平均太阳辐射预报。结果表明，使用小时，日，月和年作为自变量，使用NARX模型的历史记录较少，可以有效地预测连续两年。