Solar energy is one of the clean and healthy energies. Due to the high cost of required equipment to convert solar energy into the desired form, the economic facets must be addressed and the equipment should be installed in areas with higher accessible solar energy. However, due to the complex and time-consuming process of calculating solar radiation, it seems necessary to develop more simple models with higher estimation capability. Therefore, the present study investigated the prediction of solar radiation on the horizon using neural network methods, ANFIS and RSM, in Sarpol-e-Zahab Township, Kermanshah, Iran. In this respect, the meteorological data of this township were collected. Then, the key parameters were selected by performing sensitivity analysis, and models were designed and optimized using ANFIS, ANN, and RSM methods. Moreover, respective correlation coefficients and mean square errors of each method were obtained (ANFIS (0.993 and 0.0005), ANN (0.996 and 0.00029), and RSM (0.996 and 0.00027), respectively). Also, the neural network and response surface methodology were superior to the ANFIS Model in terms of performance, simplicity, and speed. In short, the performance of the response surface methodology was slightly better than that of the neural network.

中文翻译：

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使用神经网络方法，ANFIS和RSM预测地平线上的太阳辐射（案例研究：伊朗Sarpol-e-Zahab镇）

太阳能是清洁健康的能源之一。由于将太阳能转换为所需形式所需的设备成本很高，因此必须解决经济方面的问题，并且应将设备安装在具有较高可及性的太阳能区域。但是，由于计算太阳辐射的过程复杂且耗时，因此似乎有必要开发具有更高估算能力的更简单的模型。因此，本研究使用神经网络方法ANFIS和RSM在伊朗克尔曼沙的Sarpol-e-Zahab镇调查了地平线上太阳辐射的预测。在这方面，收集了该乡镇的气象数据。然后，通过进行敏感性分析选择关键参数，并使用ANFIS，ANN和RSM方法设计和优化模型。此外，获得每种方法各自的相关系数和均方误差（分别为ANFIS（0.993和0.0005），ANN（0.996和0.00029）和RSM（0.996和0.00027））。而且，神经网络和响应面方法在性能，简单性和速度方面均优于ANFIS模型。简而言之，响应面方法的性能略好于神经网络。