Estimation of solar radiation over Turkey using artificial neural network and satellite data

Abstract

This study introduces artificial neural networks (ANNs) for the estimation of solar radiation in Turkey (26-45 E and 36-42 N). Resilient propagation (RP), Scale conjugate gradient (SCG) learning algorithms and logistic sigmoid transfer function were used in the network. In order to train the neural network, meteorological data for the period from August 1997 to December 1997 for 12 cities (Antalya, Artvin, Edirne, Kayseri, Kütahya, Van, Adana, Ankara, İstanbul, Samsun, İzmir, Diyarbaki{dotless}r) spread over Turkey were used as training (nine stations) and testing (three stations) data. Meteorological and geographical data (latitude, longitude, altitude, month, mean diffuse radiation and mean beam radiation) are used in the input layer of the network. Solar radiation is the output. However, solar radiation has been estimated as monthly mean daily sum by using Meteosat-6 satellite C3 D data in the visible range over 12 cities in Turkey. Digital counts of satellite data were converted into radiances and these are used to calculate the albedos. Using the albedo, the cloud cover index of each pixel was constructed. Diffuse and direct component of horizontal irradiation were calculated as a function of optical air mass, turbidity factor and Rayleigh optical thickness for clear-sky. Using the relation between clear-sky index and cloud cover index, the solar irradiance for any pixel is calculated for Physical method. RMS between the estimated and ground values for monthly mean daily sum with ANN and Physical method values have been found as 2.32 MJ m-2 (54 W/m2) and 2.75 MJ m-2 (64 W/m2) (training cities), 3.94 MJ m-2 (91 W/m2) and 5.37 MJ m-2 (125 W/m2) (testing cities), respectively. © 2008 Elsevier Ltd. All rights reserved.