|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
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|
|Appears in Collections:||Artículos|
checked on Sep 19, 2021
WEB OF SCIENCETM
checked on Jan 10, 2021
checked on Jul 10, 2021
Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.