Journal
IEEE JOURNAL OF OCEANIC ENGINEERING
Volume 45, Issue 2, Pages 462-471Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JOE.2018.2875575
Keywords
Forecasting; Predictive models; Sea measurements; Autoregressive processes; Time series analysis; Surface waves; Sea surface; Autoregressive model (AR); filtering; forecasting; Gaussian process; time series; wave energy
Funding
- Science Foundation Ireland [13/IA/1886]
- Marine Renewable Ireland Centre [12/RC/2302]
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For wave energy converter control applications, autoregressive (AR) models have been proposed as a simple wave forecasting method, solely based on measured or estimated values of the past wave elevation (or excitation force) signal. Using offline-filtered wave time series, AR models can achieve accurate forecasts several wave periods into the future. In this paper, the AR method is examined from the broader perspective of linear, Gaussian processes. In particular, assuming Gaussian waves and perfect knowledge of the wave spectrum, it is possible to derive a theoretically-optimal wave elevation predictor. It is shown that, in realistic situations, AR models can achieve a performance comparable to the theoretically optimal, spectrum-based predictor, both in simulated wave time series and using actual wave elevation records.
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