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Short-term wind speed predictions with machine learning techniques

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Abstract

Hourly wind speed forecasting is presented by a modeling study with possible applications to practical problems including farming wind energy, aircraft safety and airport operations. Modeling techniques employed in this paper for such short-term predictions are based on the machine learning techniques of artificial neural networks (ANNs) and genetic expression programming (GEP). Recorded values of wind speed were used, which comprised 8 years of collected data at the Kersey site, Colorado, USA. The January data over the first 7 years (2005–2011) were used for model training; and the January data for 2012 were used for model testing. A number of model structures were investigated for the validation of the robustness of these two techniques. The prediction results were compared with those of a multiple linear regression (MLR) method and with the Persistence method developed for the data. The model performances were evaluated using the correlation coefficient, root mean square error, Nash–Sutcliffe efficiency coefficient and Akaike information criterion. The results indicate that forecasting wind speed is feasible using past records of wind speed alone, but the maximum lead time for the data was found to be 14 h. The results show that different techniques would lead to different results, where the choice between them is not easy. Thus, decision making has to be informed of these modeling results and decisions should be arrived at on the basis of an understanding of inherent uncertainties. The results show that both GEP and ANN are equally credible selections and even MLR should not be dismissed, as it has its uses.

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Acknowledgments

The authors would like to acknowledge and thank the Colorado Climate Center for providing the data used in this study. The authors would also like to thank the two anonymous reviewers for their valuable suggestions, which resulted in a more technically sound and complete presentation of the conducted work.

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Authors and Affiliations

  1. Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran

    M. A. Ghorbani & M. H. FazeliFard

  2. GTEV-ReX - Research and Mathematical Modelling, Swindon, UK

    R. Khatibi

  3. Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

    L. Naghipour

  4. METOcean Dynamic Solutions, 19 Pelion Street, Bardon, 4065, Australia

    O. Makarynskyy

Authors
  1. M. A. Ghorbani
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  2. R. Khatibi
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  3. M. H. FazeliFard
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  4. L. Naghipour
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  5. O. Makarynskyy
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Corresponding author

Correspondence to M. A. Ghorbani.

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Responsible Editor: C. Simmer.

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Ghorbani, M.A., Khatibi, R., FazeliFard, M.H. et al. Short-term wind speed predictions with machine learning techniques. Meteorol Atmos Phys 128, 57–72 (2016). https://doi.org/10.1007/s00703-015-0398-9

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  • DOI: https://doi.org/10.1007/s00703-015-0398-9

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