Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/72263
Title: Genetic algorithm applied to real-time short-term wave prediction for wave generator system in the Canary Islands
Authors: Hernández, C.
Méndez, M. 
Aguasca Colomo, Ricardo 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Forecasting methods
Genetic algorithms
Wave energy
Yule-Walker method
Issue Date: 2020
Journal: Lecture Notes in Computer Science 
Conference: 17th International Conference on Computer Aided Systems Theory, EUROCAST 2019 
Abstract: In island territories, as is the case of the Canary Islands, renewable energies mean greater energy independence, in these cases wave and wind energy favour this independence, all the more so when the generation of these types of energy is optimised. The increase in wave energy extracted from the waves requires knowledge of the future wave incident on the energy converters. A prediction system is presented using Genetic Algorithm to optimize the parameters that govern an autoregressive model, model necessary for the prediction of the incident wave. The comparison of the Yule-Walker equations with that of the Genetic Algorithm will provide us with a knowledge of the prediction technique that offers the best results, for the sake of its application. All this under the restriction of limited execution times, less than the periods of the waves to be predicted, and a demanding precision through distant prediction horizons, with reduced training datasets.
URI: http://hdl.handle.net/10553/72263
ISBN: 978-3-030-45092-2
ISSN: 0302-9743
DOI: 10.1007/978-3-030-45093-9_51
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [ISSN 0302-9743], v. 12013 LNCS, p. 421-428, (Enero 2020)
Appears in Collections:Actas de congresos
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