ORIGINAL PAPER Short-term wind speed predictions with machine learning techniques M. A. Ghorbani 1 • R. Khatibi 2 • M. H. FazeliFard 1 • L. Naghipour 3 • O. Makarynskyy 4 Received: 22 September 2013 / Accepted: 3 August 2015 Ó Springer-Verlag Wien 2015 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 effi- ciency 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. 1 Introduction Autoregressive types of regression models are used to formulate forecasting models by using the information contained within their recorded values. The study employs wind speed time series at the hourly interval and investi- gates four techniques: two machine learning techniques (artificial neural networks and genetic expression pro- gramming) and two techniques for comparison: a multiple linear regression and the simple Persistence methods. The study has potential applications to energy sources from the wind and, as stated by Burton et al. (2001) and Li and Shi (2010), this source of energy is particularly attractive for being clean, renewable, economically competitive and environmentally friendly. The wind energy systems depend on wind speed and a host of factors as discussed by Tandjaoui et al. (2013), including (i) low production capacity when deployed in a sheltered area and (ii) Responsible Editor: C. Simmer. & M. A. Ghorbani Ghorbani@tabrizu.ac.ir; m_ali_ghorbani@ymail.com R. Khatibi rahman.khatibi@gmail.com L. Naghipour Naghipour.L@tabrizu.ac.ir O. Makarynskyy makarynsky@live.com 1 Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran 2 GTEV-ReX - Research and Mathematical Modelling, Swindon, UK 3 Department of Water Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran 4 METOcean Dynamic Solutions, 19 Pelion Street, Bardon 4065, Australia 123 Meteorol Atmos Phys DOI 10.1007/s00703-015-0398-9