A study for estimating solar resources in Turkey using artificial neural networks

SÖZEN A. , Ozalp M., ARCAKLIOĞLU E., Kanit E.

ENERGY SOURCES, cilt.26, sa.14, ss.1369-1378, 2004 (SCI İndekslerine Giren Dergi) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 26 Konu: 14
  • Basım Tarihi: 2004
  • Doi Numarası: 10.1080/00908310490441935
  • Sayfa Sayıları: ss.1369-1378


Turkey has sufficient solar radiation and radiation period for solar thermal applications since it lies in a sunny belt between 36degrees and 42degreesN latitudes. The yearly average solar radiation is 3.6 kWh/m(2) day, and the total yearly radiation period is similar to2610 h. This study investigates the estimation of solar resources in Turkey using artificial neural networks (ANNs). Scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM) learning algorithms and logistic sigmoid transfer function were used in the network. In order to train the neural network, meteorological data for last three years (2000-2002) from 17 stations (namely cities) spread over Turkey were used as training (11 stations) and testing (6 stations) data. These cities selected can give a general idea about Turkey. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, and mean temperature) is used in input layer of network. Solar radiation is in output layer. The maximum mean absolute percentage error was found to be less than 6.7% and R-2 values to be about 99.8937% for the testing stations. However, these values were found to be 2.41% and 99.99658% for the training stations. The results indicate that the ANN model seems promising for evaluating solar resource possibilities at the places where there are no monitoring stations in Turkey. The results on the testing stations indicate a relatively good agreement between the observed and the predicted values.