Estimating and modeling monthly mean daily global solar radiation on horizontal surfaces using artificial neural networks

Abstract

In this study, an artificial neural network based model for prediction of solar energy potential in Kerman province in Iran has been developed. Meteorological data of 12 cities for period of 17 years (1997–2013) and solar radiation for five cities around and inside Kerman province from the Iranian Meteorological Office data center were used for the training and testing the network. Meteorological and geographical data were used as inputs to the network, while the solar radiation intensity was used as the output of the network. The results show that the correlation coefficients between the predictions and actual global solar radiation intensities for training and testing datasets were higher than 97%, suggesting a high reliability of the model for evaluating solar radiation in locations where solar radiation data are not available. The predicted solar radiation values are illustrated in the form of maps that were made by ArcGIS.