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Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on as a courtesy and upon agreement with the previous journal publisher.
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Math. Comput. Appl. 2011, 16(3), 556-564;

Analysis of Height Affect on Average Wind Speed by ANN

Celal Bayar University, Electrical Engineering Department, 45140, Muradiye, Manisa, Turkey
Ege University, Solar Energy Enstitute, Bornova, Izmir, Turkey
Author to whom correspondence should be addressed.
Published: 1 December 2011
PDF [158 KB, uploaded 18 March 2016]


The power generated by wind turbines depends on several factors. Two of them are the wind speed and the tower height of wind turbine. In this study, the annual average wind speed based on the tower height is predicted using Artificial Neural Networks (ANN) and comparisons made with conventional model approach. The backpropagation multi layer ANNs were used to estimate annual average wind speed for three locations in Turkey. The Model has been developed with the help of neural network methodology. It involves four input variables-wind speed of measured location, desired height on measured location, height above ground level of measured location and Hellmann coefficient and one output variables-annual average wind speed. The model accuracy is evaluated by comparing the conventional model results with the actual measured and calculated values.
Keywords: Artificial neural networks; Average wind speed forecasting; Hellmann coefficient Artificial neural networks; Average wind speed forecasting; Hellmann coefficient
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Ata, R.; Çetin, N.S. Analysis of Height Affect on Average Wind Speed by ANN. Math. Comput. Appl. 2011, 16, 556-564.

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