Wave prediction using wave rider position measurements and NARX network in wave energy conversion

•Wave prediction for control of wave energy converters.•Wave prediction using artificial neural networks.•Prediction of significant wave height in the future using real data. Several control methods of wave energy converters (WECs) need prediction in the future of wave surface elevation. Prediction...

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Bibliographic Details
Published in:Applied ocean research 2019-01, Vol.82, p.10-21
Main Authors: Desouky, Mohammed A.A., Abdelkhalik, Ossama
Format: Article
Language:English
Subjects:
Buoys
Control methods
Elevation
Energy conversion
Wave energy
Wave energy conversion
Wave power
Wave power devices
Wave predicting
Wave prediction
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Summary:•Wave prediction for control of wave energy converters.•Wave prediction using artificial neural networks.•Prediction of significant wave height in the future using real data. Several control methods of wave energy converters (WECs) need prediction in the future of wave surface elevation. Prediction of wave surface elevation can be performed using measurements of surface elevation at a location ahead of the controlled WEC in the upcoming wave. Artificial neural network (ANN) is a robust data-learning tool, and is proposed in this study to predict the surface elevation at the WEC location using measurements of wave elevation at ahead located sensor (a wave rider buoy). The nonlinear autoregressive with exogenous input network (NARX NN) is utilized in this study as the prediction method. Simulations show promising results for predicting the wave surface elevation. Challenges of using real measurements data are also discussed in this paper.
ISSN:0141-1187
1879-1549
DOI:10.1016/j.apor.2018.10.016