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A Critical Comparison of AR and ARMA Models for Short-term Wave Forecasting |
Peña-Sanchez, Yerai; Ringwood, John
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In order to extract as much energy as possible from
ocean waves, an optimal control must be implemented in a wave
energy converter (WEC), which requires the knowledge of the
future incident waves (η). One of the most used methods to
predict the future η, is to use a linear combination of past η
values. Several models can be found in the literature, but only
two of these models are compared in this paper, the autoregressive
(AR) and autoregressive moving average (ARMA) models. Real
wave data from different locations is used to determine which
model is the best and in which scenario. This comparison
addresses the discrepancies between [1], where the ARMA model
is discarded for showing no improvement against the AR, and
[2], which states that the ARMA model does improve the AR.
The present paper shows that the two models achieve a similar
performance for all the different conditions analysed. Thus, due
to the simplicity and the lower computational requirement, the
AR model is chosen as the best model for prediction.
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Keyword(s):
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Wave energy; free surface elevation forecasting; autoregressive model; autoregressive moving average model; optimal control |
Publication Date:
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2017 |
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Type:
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Journal article |
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Peer-Reviewed:
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Yes |
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Institution:
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Maynooth University |
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Citation(s):
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Peña-Sanchez, Yerai and Ringwood, John (2017) A Critical Comparison of AR and ARMA Models for Short-term Wave Forecasting. Proceedings of the 12th European Wave and Tidal Energy Conference 27th Aug -1st Sept 2017 (961). pp. 1-6. ISSN 2309-1983 |
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Publisher(s):
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European Wave and Tidal Energy Conference 2017 |
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File Format(s):
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other |
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Related Link(s):
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http://mural.maynoothuniversity.ie/12461/1/JR-Critical-2017.pdf |
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First Indexed:
2020-04-02 06:01:22 Last Updated:
2020-04-02 06:01:22 |
