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A classification mechanism for determining average wind speed and power in several regions of Turkey using artificial neural networks

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  • Çam, Ertugrul
  • Arcaklıoğlu, Erol
  • Çavuşoğlu, Abdullah
  • Akbıyık, Bilge

Abstract

In this paper, average wind speed and wind power values are estimated using artificial neural networks (ANNs) in seven regions of Turkey. To start with, a network has been set up, and trained with the data set obtained from several stations—each station gather data from five different heights—from each region, one randomly selected height value of a station has been used as test data. Wind data readings corresponding to the last 50 years of relevant regions were obtained from the Turkish State Meteorological Service (TSMS). The software has been developed under Matlab 6.0. In the input layer, longitude, latitude, altitude, and height are used, while wind speeds and related power values correspond to output layer. Then we have used the networks to make predictions for varying heights, which are not incorporated to the system at the training stage. The network has successfully predicted the required output values for the test data and the mean error levels for regions differed between 3% and 6%. We believe that using ANNs average wind speed and wind power of a region can be predicted provided with lesser amount of sampling data, that the sampling mechanism is reliable and adequate.

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  • 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.
    • Handle: RePEc:eee:renene:v:30:y:2005:i:2:p:227-239
    DOI: 10.1016/j.renene.2004.05.008
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    Cited by:

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    2. Carolin Mabel, M. & Fernandez, E., 2008. "Analysis of wind power generation and prediction using ANN: A case study," Renewable Energy, Elsevier, vol. 33(5), pages 986-992.
    3. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    4. Ramasamy, P. & Chandel, S.S. & Yadav, Amit Kumar, 2015. "Wind speed prediction in the mountainous region of India using an artificial neural network model," Renewable Energy, Elsevier, vol. 80(C), pages 338-347.
    5. Yurdusev, M.A. & Ata, R. & Çetin, N.S., 2006. "Assessment of optimum tip speed ratio in wind turbines using artificial neural networks," Energy, Elsevier, vol. 31(12), pages 2153-2161.
    6. Manoj Verma & Harish Kumar Ghritlahre & Ghrithanchi Chandrakar, 2023. "Wind Speed Prediction of Central Region of Chhattisgarh (India) Using Artificial Neural Network and Multiple Linear Regression Technique: A Comparative Study," Annals of Data Science, Springer, vol. 10(4), pages 851-873, August.

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