Assessment of solar potential over a location of interest is introduced as an important step for the successful planning of solar energy systems (photovoltaic or thermal). Due to the absence of meteorological stations and sophisticated solar sensors, solar data may be unavailable for every point of interest. Hence, empirical and intelligence methods are developed to estimate solar irradiance data. In this study, the idea of artificial intelligence methods is employed to predict the daily global solar radiation. The developed models are: group method of data handling (GMDH) type neural network, multilayer feed-forward neural network (MLFFNN), adaptive neuro-fuzzy inference system (ANFIS), ANFIS optimized with particle swarm optimization algorithm (ANFIS-PSO), ANFIS optimized with genetic algorithm (ANFIS-GA) and ANFIS optimized with ant colony (ANFIS-ACO). The data are collected from 12 stations in different climate zones of Iran. The input variables of the models are including month, day, average air temperature, maximum air temperature, minimum air temperature, air pressure, relative humidity, wind speed, top of atmosphere insolation, latitude and longitude. The results demonstrated that although the developed models can successfully predict the global horizontal irradiance, the GMDH model outperforms the other developed models. The values of root mean square error (RMSE), determination coefficient (R²) and mean square error (MSE) for the GMDH model were 0.2466 (kWh/m²/day), 0.9886 and 0.0608 (kWh/m²/day), respectively.

引入对感兴趣位置的太阳能潜力的评估是成功规划太阳能系统（光伏或热能系统）的重要步骤。由于缺少气象站和先进的太阳能传感器，因此可能无法针对每个兴趣点获得太阳能数据。因此，开发了经验和智能方法来估计太阳辐照度数据。在这项研究中，采用人工智能方法的思想来预测每日的全球太阳辐射量。开发的模型是：数据处理（GMDH）型神经网络的分组方法，多层前馈神经网络（MLFFNN），自适应神经模糊推理系统（ANFIS），使用粒子群优化算法（ANFIS-PSO）优化的ANFIS，通过遗传算法（ANFIS-GA）优化的ANFIS和通过蚁群（ANFIS-ACO）优化的ANFIS。数据是从伊朗不同气候区的12个站点收集的。模型的输入变量包括月，日，平均气温，最高气温，最低气温，气压，相对湿度，风速，日照高度，纬度和经度。结果表明，尽管开发的模型可以成功预测整体水平辐照度，但GMDH模型优于其他开发的模型。均方根误差（RMSE）值，确定系数（R 最低气温，气压，相对湿度，风速，最高日照度，纬度和经度。结果表明，尽管开发的模型可以成功预测整体水平辐照度，但GMDH模型优于其他开发的模型。均方根误差（RMSE）值，确定系数（R 最低气温，气压，相对湿度，风速，最高日照度，纬度和经度。结果表明，尽管开发的模型可以成功预测整体水平辐照度，但GMDH模型优于其他开发的模型。均方根误差（RMSE）值，确定系数（R²）和GMDH模型的均方误差（MSE）分别为0.2466（kWh / m ² /day)、0.9886和0.0608（kWh / m ² / day）。