Prediction of Hourly Solar Radiation in Six Provinces in Turkey by Artificial Neural Networks

SOLMAZ Ö., Ozgoren M.

JOURNAL OF ENERGY ENGINEERING-ASCE, cilt.138, sa.4, ss.194-204, 2012 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 138 Sayı: 4
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1061/(asce)ey.1943-7897.0000080
  • Dergi Adı: JOURNAL OF ENERGY ENGINEERING-ASCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.194-204
  • Anahtar Kelimeler: Artificial neural network, Back-propagation, Hourly solar radiation, Solar energy, Turkey
  • Manisa Celal Bayar Üniversitesi Adresli: Hayır

Özet

The purpose of this study is to apply the method of artificial neural networks (ANNs) to predict the hourly solar radiation of six selected provinces in Turkey. Six neurons-receiving input signals of latitude, longitude, altitude, day of the year, hour of the day, and mean hourly atmospheric air temperature-were used in the input layer of the network. One neuron producing a corresponding output signal of hourly solar radiation was utilized in the output layer of the network. Two different models have been analyzed in the ANNs for training and testing. The results obtained from both models were compared by using different neurons, mean squared error (MSE), coefficient of determination (R-2), and mean absolute error (MAE). According to the results, the MSE value of training data in Model II was better than Model I. DOI: 10.1061/(ASCE)EY.1943-7897.0000080. (C) 2012 American Society of Civil Engineers.