Performance of artificial neural networks in nearshore wave power prediction

被引:47
作者
Castro, A. [1 ]
Carballo, R. [1 ]
Iglesias, G. [2 ]
Rabunal, J. R. [3 ]
机构
[1] Univ Santiago Compostela, EPS, Lugo 27002, Spain
[2] Univ Plymouth, Sch Marine Sci & Engn, Plymouth PL4 8AA, Devon, England
[3] Univ A Coruna, Dept Informat & Commun Technol, La Coruna 15071, Spain
关键词
Wave energy; Artificial intelligence; Neural network; Numerical model; Wave propagation; SWAN; ENERGY CONVERTERS; MODEL; RESOURCE; WATER; BREAKWATERS; METHODOLOGY; VARIABLES; SPAIN; WIND; SITE;
D O I
10.1016/j.asoc.2014.06.031
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper the assessment of the wave energy potential in nearshore coastal areas is investigated by means of artificial neural networks (ANNs). The performance of the ANNs is compared with in situ measurements and spectral numerical modelling (the conventional tool for wave energy assessment). For this purpose, 13 years of records of two buoys, one offshore and one inshore, with an hourly frequency are used to develop an ANN model for predicting the nearshore wave power. The best suited architecture was selected after assessing the performance of 480 ANN models involving twelve different architectures. The results predicted by the ANN model were compared with the measured data and those obtained by means of the SWAN (Simulating Waves Nearshore) spectral model. The quality in the predictions of the ANN model shows that this type of artificial intelligence models constitutes a powerful tool to forecast the wave energy potential at particular coastal site with great accuracy, and one that overcomes some of the disadvantages of the conventional tools for nearshore wave power prediction. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码:194 / 201
页数:8
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