In this research, we used daily incoming solar radiation data of Isfahan ozone station during 1365-1387 to find an estimation model of Incoming solar radiation using sunshine ours. The results have been compared to the other common models using RMSE and MBE indices. The results show that the regression amount of our model is 0.91 and 0.6 and 0.7 for the models Frere and McCulloc respectively. Also, our model has a very precise results for May and Augest months in which the error is less than -2 %.

The mean daily global solar radiation flux is influenced by astronomical, climatological, geographical, geometrical, meteorological, and physical parameters. This paper deals with the study of the effects of influencing parameters on the mean daily global solar radiation flux, and also with the computation of the solar radiation flux at the surface of the earth in locations without solar radiation measurements. The reference–real data were borrowed from the Iranian Meteorological Organization. The analysis of data showed that the mean daily solar radiation flux on a horizontal surface is related to parameters such as: mean daily extraterrestrial solar radiation, average daily ratio of sunshine duration, mean daily relative humidity, mean daily maximum air temperature, mean daily maximum dew point temperature, mean daily atmospheric pressure, and sine of the solar declination angle. Multiple regression and correlation analysis were applied to predict the mean daily global solar radiation flux on a horizontal surface. The models were validated when compared with the reference–measured data of global solar radiation flux. The results showed that the models estimate the global solar radiation flux within a narrow relative error band. The values of mean bias errors and root mean square errors were within acceptable margins. The predicted values of global solar radiation flux by this approach can be used for the design and performance estimation in solar applications. The model can be used in areas where meteorological stations do not exist and information on solar radiation flux cannot be obtained experimentally.

One way to forecast solar radiation when the measurement data are not available is estimation by using meteorological parameters. In this study, meteorological data, including maximum and minimum temperatures, wind speed, sunshine hours, degree of cloudiness, precipitation, pressure, and humidity were collected in the sixth stations of Mashhad, Isfahan, Ramsar, Zahedan, Urmia, and Shiraz. Meteorological parameters affecting solar radiation were determined using Gamma test at each station. The results showed that in all stations the maximum temperature and sunshine hours, in 5 stations degree of cloudiness, and in 4 station pressure and wind speed were the parameters affecting the solar radiation. The most important influencing parameters were different at each station so that in four stations the wind speed and in three stations the sunshine hours were ranked as first and second. The results showed that the maximum temperature and the degree of cloudiness were also effective on solar radiation but they possessed less importance compared to the latter parameters. After determining the effective meteorological parameters at each station, solar radiation was estimated using support vector machine (SVM) and three experimental methods of Angstrom, Hargreaves, and Abdullah. Coefficients of experimental methods was calibrated using data training. In stations under study the accuracy of Angstrom and Abdullah were not certain and at some stations Abdullah and in others the Angstrom methods estimated radiation more accurately. Hargreaves method estimated the solar radiation with less accuracy compared to the two other experimental methods. SVM estimated the solar radiation in the test phase at Isfahan, Mashhad, Urmia, Ramsar, Shiraz, and Zahedan stations by RMSE errors of 1.38, 1.28, 1.36, 1.51, 1.21, and 1.58 MJm-2d-1 and MEF errors of 3.59, 5.50, 4.18, 7.96, 3.26, and 5.17 percent, respectively. SVM estimated the solar radiation with greater accuracy than empirical methods in all stations by using artificial intelligence.

This study aimed to compare global solar radiation on the horizontal area between two models of Chabane Foued and M.Capderou. The model of Chabane has been interested in pollution factors such as TL (turbidity), BE (Angstraon), and the chemical components of the air such as WV, O3, CH4, CO, CO2, and the especial part the new pollution factor such as hbeam, kbeam, and kdiffuse, which all influenced onto solar radiation, and the model of Capderou has been used the atmospheric disturbance to calculate the direct and diffuse components of radiation received on a plane, while the constituents of the atmosphere (absorption and diffusion) can be expressed by disturbance factors, which is very necessary to determine irradiation In the clear sky. The results reveal a significant difference between the two models with approximated curves. The difference between the models probably returns to the nature of the geographic site which the authors used and injected into the models.

The value of radiation energy is considered to be the main component of the design of clean energy devices. Due to the fact that this amount is not measured or in some cases the radiation station may not be available in Iran, so it is necessary to estimate this component. In this study, Kerman and Yazd were selected as the regions with the maximum potential of solar energy. Then Artificial Neural Network (ANN) and Support Vector Machine (SVM) capabilities were evaluated in solar energy estimation. For this purpose, the daily data of 25 years (1992-2017) including maximum temperature, mean temperature, relative humidity, sunshine hours and solar radiation were collected at the synoptic stations of these regions. To evaluate the performance of models, common evaluation statistics were used. The results showed that at the Yazd station, in the ANN method, the lowest values of RMSE, MAE and the highest values of IA and R2 were 2. 381, 1. 760, 0. 869, and 0. 962, respectively. These values at Kerman Station are equal to 2. 708, 2. 050, 0. 945 and 0. 810, respectively. In the SVM method, the lowest values of RMSE, MAE and the highest values of IA and R2 at the Yazd station were 2. 028, 1. 540, 0. 901 and 0. 973, respectively, and at Kerman station were 2. 407, 1. 896, 0. 956 and 0. 846, respectively. Overall, the efficiency of the SVM method in both regions was more accurate than the ANN method.

Solar energy is one of the most promising renewable energy sources. The values of the global solar radiation (GSR) are the most important parameter for the solar energy applications. The main objective of the present study is to apply Genetic Algorithm (GA) to estimate Global Solar Radiation over central arid deserts of Iran. To achieve this, the measured weather data for six stations, spread over the study area, were used to develop comprehensive models to estimate GSR. Monthly maximum, minimum, and mean air temperature, mean relative humidity, total sunshine hours, maximum possible sunshine duration and extra-terrestrial solar radiation were used as inputs variables and the measured global solar radiation (GSR) was used as output. The obtained results confirm the ability of the developed models to predict solar radiation values precisely.

Accurate calculation of the amount of solar radiation in an area has an effective role in the climatology and agriculture of the region, estimating the rate of evapotranspiration, site selection of solar power plant and using photovoltaic systems. Point measurements at ground stations using pyranometers are the most accurate method of estimating solar radiation, in which measurements are extended to a continuous surface using spatial interpolation methods. The main purpose of this study is to increase the accuracy of solar radiation zoning in Iran using Cokriging method. For this purpose, the amount of solar radiation was first calculated using the Digital Elevation Model (DEM) and the Solar Radiation spatial toolbox in ArcGIS software. Then the correlation coefficient (R) between the obtained values from the software with the values of solar radiation measured at ground stations was calculated. According to R = 0. 713 between these two data, by Cokriging method, these two data were combined and the continuous surface of solar radiation for the whole of the country was calculated. The results showed that the calculation of solar radiation using Area Solar GIS tool is not accurate enough compared to ground data, but the combination of the two data, while affecting the topography in the calculation of solar radiation, increases the interpolation accuracy by 11%. Therefore, although existing models may not be accurate enough to estimate solar radiation on a national scale compared to terrestrial data, they can be used to improve the accuracy of terrestrial data zoning. According to the final map, most regions of the country, except the northern and northwestern regions, receive solar radiation above the global average (340 w/m2).