Application of artificial neural networks for the wind speed prediction of target station using reference stations data

Bilgili M., Sahin B., Yasar A.

RENEWABLE ENERGY, cilt.32, sa.14, ss.2350-2360, 2007 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 32 Sayı: 14
  • Basım Tarihi: 2007
  • Doi Numarası: 10.1016/j.renene.2006.12.001
  • Dergi Adı: RENEWABLE ENERGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2350-2360
  • Anahtar Kelimeler: artificial neural network, wind speed prediction, reference stations, REGIONS, POWER
  • Çukurova Üniversitesi Adresli: Evet

Özet

In this study, artificial neural networks (ANNs) were applied to predict the mean monthly wind speed of any target station using the mean monthly wind speeds of neighboring stations which are indicated as reference stations. Hourly wind speed data, collected by the Turkish State Meteorological Service (TSMS) at 8 measuring stations located in the eastern Mediterranean region of Turkey were used. The long-term wind data, containing hourly wind speeds, directions and related information, cover the period between 1992 and 2001. These data were divided into two sections. According to the correlation coefficients, reference and target stations were defined. The mean monthly wind speeds of reference stations were used and also corresponding months were specified in the input layer of the network. On the other hand, the mean monthly wind speed of the target station was utilized in the output layer of the network. Resilient propagation (RP) learning algorithm was applied in the present simulation. The hidden layers and output layer of the network consist of logistic sigmoid transfer function (logsig) and linear transfer function (purelin) as an activation function. Finally, the values determined by ANN model were compared with the actual data. The maximum mean absolute percentage error was found to be 14.13% for Antakya meteorological station and the best result was found to be 4.49% for Mersin meteorological station. (c) 2006 Elsevier Ltd. All rights reserved.