A Comparison of Two Solar Radiation Models Using Artificial Neural Networks and Remote Sensing in Turkey

YILDIZ B. Y., Sahin M., ŞENKAL O., Pestemalci V., EMRAHOĞLU N.

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, cilt.35, sa.3, ss.209-217, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 35 Sayı: 3
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1080/15567036.2011.650276
  • Dergi Adı: ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.209-217
  • Anahtar Kelimeler: land surface temperature, remote sensing, scale conjugate gradiant, solar radiation, Turkey, LAND-SURFACE-TEMPERATURE
  • Çukurova Üniversitesi Adresli: Evet

Özet

This study introduces artificial neural networks for the estimation of solar radiation using model 1 (latitude, longitude, altitude, month, and meteorological land surface temperature) and model 2 (latitude, longitude, altitude, month, and satellite land surface temperature) data in Turkey. Price method was used for the estimation of land surface temperature values. Scale conjugate gradiant learning algorithms and logistic sigmoid transfer function were used in the network. R 2 with model 1 and model 2 values have been found to be 96.93 and 97.24% (training stations), 80.41 and 82.37% (testing stations), respectively. These results are sufficient to predict the solar radiation.