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Erschienen in: Neural Computing and Applications 11/2019

14.07.2018 | Original Article

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

verfasst von: José Gustavo Hernández-Travieso, Carlos M. Travieso-González, Jesús B. Alonso-Hernández, José Miguel Canino-Rodríguez, Antonio G. Ravelo-García

Erschienen in: Neural Computing and Applications | Ausgabe 11/2019

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Abstract

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.

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Metadaten
Titel
Modeling a robust wind-speed forecasting to apply to wind-energy production
verfasst von
José Gustavo Hernández-Travieso
Carlos M. Travieso-González
Jesús B. Alonso-Hernández
José Miguel Canino-Rodríguez
Antonio G. Ravelo-García
Publikationsdatum
14.07.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 11/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3619-6

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