Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/47432
Title: Modeling a robust wind-speed forecasting to apply to wind-energy production
Authors: Hernández-Travieso, José Gustavo
Travieso-González, Carlos M. 
Alonso-Hernández, Jesús B. 
Canino-Rodríguez, José Miguel 
Ravelo-García, Antonio G. 
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Neural-Networks
Power
Prediction
Modeling
Wind-Speed Prediction, et al
Issue Date: 2019
Publisher: 0941-0643
Journal: Neural Computing and Applications 
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.
URI: http://hdl.handle.net/10553/47432
ISSN: 0941-0643
DOI: 10.1007/s00521-018-3619-6
Source: Neural Computing and Applications[ISSN 0941-0643], vol. 31 (11), p. 7891-7905
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