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Prediction of mean monthly wind speed and optimization of wind power by artificial neural networks using geographical and atmospheric variables: case of Aegean Region of Turkey

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Abstract

Although there are many locations suitable to construct new wind turbines, wind speeds in those areas are not always available, which makes it difficult to plan and develop a proper wind energy conversion system. This paper proposes an approach to determine the wind speeds corresponding to locations without any past wind speed data. First, monthly mean wind speed was modeled as a function of geographical variables (latitude, longitude and elevation), atmospheric variables (ambient temperature, atmospheric pressure, percent relative humidity), and the month of the year for a case location (Aegean Region of Turkey) by artificial neural networks (ANNs) trained by the data supplied by 55 wind speed measuring stations throughout the region (660 data points). Then, the prediction ability of the ANN model was tested: The wind speed data of each station was excluded from the database, and an ANN model trained by the data of the rest of the wind stations was used to forecast the excluded data. Finally, a grid search algorithm was applied to the entire region to search for the optimum location for the maximum average annual wind speed which was found to be 10.6 m/s. A generic wind turbine was considered at this location and a power of 1.79 MW was achieved.

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Acknowledgements

The authors would like to thank Turkish General Directorate of Meteorology for providing wind speed data.

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  1. Department of Energy Systems Engineering, Istanbul Bilgi University, 34060, Eyup, Istanbul, Turkey

    Deniz Ulkat & M. Erdem Günay

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  1. Deniz Ulkat
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  2. M. Erdem Günay
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Correspondence to M. Erdem Günay.

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Ulkat, D., Günay, M.E. Prediction of mean monthly wind speed and optimization of wind power by artificial neural networks using geographical and atmospheric variables: case of Aegean Region of Turkey. Neural Comput & Applic 30, 3037–3048 (2018). https://doi.org/10.1007/s00521-017-2895-x

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