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

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  • Bilgili, Mehmet
  • Sahin, Besir
  • Yasar, Abdulkadir

Abstract

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.

Suggested Citation

  • Bilgili, Mehmet & Sahin, Besir & Yasar, Abdulkadir, 2007. "Application of artificial neural networks for the wind speed prediction of target station using reference stations data," Renewable Energy, Elsevier, vol. 32(14), pages 2350-2360.
    • Handle: RePEc:eee:renene:v:32:y:2007:i:14:p:2350-2360
    DOI: 10.1016/j.renene.2006.12.001
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    References listed on IDEAS

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    1. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    2. Mohandes, M.A. & Halawani, T.O. & Rehman, S. & Hussain, Ahmed A., 2004. "Support vector machines for wind speed prediction," Renewable Energy, Elsevier, vol. 29(6), pages 939-947.
    3. Bilgili, M. & Şahin, B. & Kahraman, A., 2004. "Wind energy potential in Antakya and İskenderun regions, Turkey," Renewable Energy, Elsevier, vol. 29(10), pages 1733-1745.
    4. Çam, Ertugrul & Arcaklıoğlu, Erol & Çavuşoğlu, Abdullah & Akbıyık, Bilge, 2005. "A classification mechanism for determining average wind speed and power in several regions of Turkey using artificial neural networks," Renewable Energy, Elsevier, vol. 30(2), pages 227-239.
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