Wind Speed Prediction of Target Station from Reference Stations Data

BİLGİLİ M., ŞAHİN B.

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, cilt.35, sa.5, ss.455-466, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 35 Sayı: 5
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1080/15567036.2010.512906
  • Dergi Adı: ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.455-466
  • Anahtar Kelimeler: artificial neural network, missing data, prediction, target station, wind speed, ARTIFICIAL NEURAL-NETWORKS, TIME-SERIES, ELECTRICITY CONSUMPTION, TURKEY, ENERGY, POWER, RAINFALL, CLASSIFICATION, TEMPERATURE, SIMULATION
  • Çukurova Üniversitesi Adresli: Evet

Özet

The aim of the present study is to apply an artificial neural network method for daily, weekly, and monthly wind speed predictions in some parts of the Aegean and Marmara region of Turkey that demonstrate acceptable cross-correlations. The wind data taken with an interval of one hour were measured by the General Directorate of Electrical Power Resources Survey Administration at four different measuring stations, namely, Gokceada, Foca, Gelibolu, and Bababurnu. The wind speeds of three different stations were used as input neurons, while the wind speed of the target station was used as an output neuron in the artificial neural network architecture. The results obtained with this model were compared with the measured data. Errors obtained in this model are within acceptable limits. Results show that the artificial neural network method can successfully predict the daily, weekly, and monthly wind speed of any target station using the measured data of surrounding stations.