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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Abstract

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.

Item Type:	Article
Keywords:	Wave energy; free surface elevation forecasting; autoregressive model; autoregressive moving average model; optimal control;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering Faculty of Science and Engineering > Research Institutes > Centre for Ocean Energy Research
Item ID:	12461
Depositing User:	Professor John Ringwood
Date Deposited:	19 Feb 2020 15:10
Journal or Publication Title:	Proceedings of the 12th European Wave and Tidal Energy Conference 27th Aug -1st Sept 2017
Publisher:	European Wave and Tidal Energy Conference 2017
Refereed:	Yes
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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