Neural Computing and Applications

, Volume 32, Issue 2, pp 391–402 | Cite as

Wind power ramp event detection with a hybrid neuro-evolutionary approach

  • L. Cornejo-Bueno
  • C. Camacho-Gómez
  • A. Aybar-Ruiz
  • L. Prieto
  • A. Barea-Ropero
  • S. Salcedo-SanzEmail author
IWANN2017: Learning algorithms with real world applications


In this paper, a hybrid system for wind power ramp events (WPREs) detection is proposed. The system is based on modeling the detection problem as a binary classification problem from atmospheric reanalysis data inputs. Specifically, a hybrid neuro-evolutionary algorithm is proposed, which combines artificial neural networks such as extreme learning machine (ELM), with evolutionary algorithms to optimize the trained models and carry out a feature selection on the input variables. The phenomenon under study occurs with a low probability, and for this reason the classification problem is quite unbalanced. Therefore, is necessary to resort to techniques focused on providing a balance in the classes, such as the synthetic minority over-sampling technique approach, the model applied in this work. The final model obtained is evaluated by a test set using both ELM and support vector machine algorithms, and its accuracy performance is analyzed. The proposed approach has been tested in a real problem of WPREs detection in three wind farms located in different areas of Spain, in order to see the spatial generalization of the method.


Wind power ramp events Prediction Neuro-evolutionary algorithms Unbalanced classification problems 



Area under the curve


Autoregressive moving average


Artificial neural networks


Extreme learning machine


European Centre for Medium-Range Weather Forecasts


Nearest K-neighbors


Machine learning


Multi-layer perceptrons


Numerical Weather Models


Receiver operating characteristic


Swinging door algorithm


Synthetic minority over-sampling technique


Support vector machine


Support vector regression


Wind power ramp events



This work has been partially supported by Comunidad de Madrid, under Project Number S2013/ICE-2933, by projects TIN2014-54583-C2-2-R and TIN2017-85887-C2-2-P of the Spanish Ministerial Commission of Science and Technology (MICYT). The authors acknowledge support by DAMA network TIM2015-70308-REDT.

Compliance with ethical standards

Conflict of interest statement

The authors declare no conflict of interest in this research work.


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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  • L. Cornejo-Bueno
    • 1
  • C. Camacho-Gómez
    • 1
  • A. Aybar-Ruiz
    • 1
  • L. Prieto
    • 2
  • A. Barea-Ropero
    • 1
  • S. Salcedo-Sanz
    • 1
    Email author
  1. 1.Department of Signal Processing and CommunicationsUniversidad de AlcaláAlcalá de Henares, MadridSpain
  2. 2.Department of Forecast SystemsIberdrolaMadridSpain

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