Wind Power Production Forecasting Using Ant Colony Optimization and Extreme Learning Machines

  • Maria Carrillo
  • Javier Del Ser
  • Miren Nekane Bilbao
  • Cristina Perfecto
  • David Camacho
Part of the Studies in Computational Intelligence book series (SCI, volume 737)


Nowadays the energy generation strategy of almost every nation around the world relies on a strong contribution from renewable energy sources. In certain countries the relevance taken by wind energy is particularly high within its national production share, mainly due to its large-scale wind flow patterns. This noted potentiality of wind energy has so far attracted public and private funds to support the development of advanced wind energy technologies. However, the proliferation of wind farms makes it challenging to achieve a proper electricity balance of the grid, a problem that becomes further involved due to the fluctuations of wind generation that occur at different time scales. Therefore, acquiring a predictive insight on the variability of this renewable energy source becomes essential in order to optimally inject the produced wind energy into the electricity grid. To this end the present work elaborates on a hybrid predictive model for wind power production forecasting based on meteorological data collected at different locations over the area where a wind farm is located. The proposed method hybridizes Extreme Learning Machines with a feature selection wrapper that models the discovery of the optimum subset of predictors as a metric-based search for the optimum path through a solution graph efficiently tackled via Ant Colony Optimization. Results obtained by our approach for two real wind farms in Zamora and Galicia (Spain) are presented and discussed, from which we conclude that the proposed hybrid model is able to efficiently reduce the number of input features and enhance the overall model performance.


Wind production Supervised learning Feature selection Ant Colony Optimization Extreme Learning Machines 



This work has been co-funded by the following research projects: EphemeCH (TIN2014-56494-C4-4-P) by the Spanish Ministry of Economy and Competitivity, CIBERDINE (S2013/ICE-3095), both under the European Regional Development Fund FEDER, by Airbus Defence & Space (FUAM-076914 and FUAM-076915), and by the Basque Government under its ELKARTEK program (KK-2016/00096, BID3ABI project).


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Maria Carrillo
    • 1
  • Javier Del Ser
    • 1
    • 2
    • 3
  • Miren Nekane Bilbao
    • 1
  • Cristina Perfecto
    • 1
  • David Camacho
    • 4
  1. 1.University of the Basque Country UPV/EHUBilbaoSpain
  2. 2.TECNALIADerioSpain
  3. 3.Basque Center for Applied Mathematics (BCAM)BilbaoSpain
  4. 4.Universidad Autónoma de MadridMadridSpain

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