Estimation of Global Solar Radiation Using Artificial Neural Networks Models Compared with Empirical Models at Three Stations of Shiraz, Karaj and Ramsar

Global solar radiation (Rs(on a horizontal surface in the estimation of evapotranspiration of plants and hydrology studies is an important factor. Average daily global solar radiation on a horizontal surface was estimated by artificial neural networks (ANNs) and five empirical models including FAO (No. 56)، Hargreaves-Samani، Mahmood-Hubard، Bahel and Annandale. The weather data was selected from Karaj، Shiraz، and Ramsar weather stations، which have arid، semi arid and very humid climates (based on De Martonne classification). Daily solar radiation was measured at the three sites selected. The ANN، with actual duration of sunshine and maximum possible duration of sunshine as input parameters، generated daily solar radiation estimates with highest level of accuracy among all models tested. Rs estimates by ANNs with only temperature indices as input and by Hargreaves-Samani، Annandale and Mahmood-Hubard، which are all temperature oriented models، had lower accuracy at all three sites. In contrast، ANNs with actual duration of sunshine and maximum possible sunshine hours as inputs in Karaj، Shiraz and Ramsar station with root mean square error (RMSE) of 2. 08، 1. 85 and 2. 05 Mj m-2 day-1 respectively were the best models. After ANNs، FAO-56 model which is based on sunshine hours produced results closer to the measured values. Rs estimates by ANNs with only temperature indices as input and by Hargreaves-Samani، Annandale and Mahmood-Hubard which are all temperature oriented models، had lower accuracy at all the three sites. These models are not appropriate for estimating daily global solar radiation.