Modeling a robust wind-speed forecasting to apply to wind-energy production

  • José Gustavo Hernández-Travieso
  • Carlos M. Travieso-González
  • Jesús B. Alonso-Hernández
  • José Miguel Canino-Rodríguez
  • Antonio G. Ravelo-García
Original Article


To obtain green energy, it is important to know, in advance, an estimation of the weather conditions. In case of wind energy, another important factor is to determine the right moment to stop the turbine in case of strong winds to avoid its damage. This research introduces a tool, not only to increase green energy generation from wind, reducing CO2 emissions, but also to prevent failures in turbines that is especially interesting for manufacturers. Using Artificial Neural Networks and data from meteorological stations located in Gran Canaria airport and Tenerife Sur airport (both in Canary Islands, Spain), a robust prediction system able to determine wind speed with a mean absolute error of 0.29 m per second is presented.


Modeling Wind-speed prediction Green energy Artificial neural networks 



This work has been supported by Endesa Foundation and the University of Las Palmas Foundation under Grant “Programa Innova Canarias 2020”.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.


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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Signal and Communications Department, Institute for Technological Development and Innovation in Communications (IDeTIC)University of Las Palmas de Gran CanariaLas Palmas de Gran CanariaSpain
  2. 2.Signal and Communications DepartmentUniversity of Las Palmas de Gran CanariaLas Palmas de Gran CanariaSpain

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