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An application of the Fast Fourier Transform to the short-term prediction of sea wave behaviour

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  • Halliday, J. Ross
  • Dorrell, David G.
  • Wood, Alan R.

Abstract

This paper examines the appropriateness of the Fast Fourier Transform for decomposition and reconstruction of wave records taken at fixed locations and transposed to a different temporal and spatial point. In marine renewable energy, advanced control methods based on the future prediction of waves are being developed. These methods are based on the assumption that a forward looking prediction is available and over the years there has been a conjecture that the FFT may perform this role and that the prediction of wave behaviour at any point on the sea surface should be realizable. The validity of this statement is tested using numerical wave records.

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  • Halliday, J. Ross & Dorrell, David G. & Wood, Alan R., 2011. "An application of the Fast Fourier Transform to the short-term prediction of sea wave behaviour," Renewable Energy, Elsevier, vol. 36(6), pages 1685-1692.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:6:p:1685-1692
    DOI: 10.1016/j.renene.2010.11.035
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    Cited by:

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    2. Flores, Juan J. & Graff, Mario & Rodriguez, Hector, 2012. "Evolutive design of ARMA and ANN models for time series forecasting," Renewable Energy, Elsevier, vol. 44(C), pages 225-230.
    3. Wu, Jinming & Yao, Yingxue & Zhou, Liang & Göteman, Malin, 2018. "Real-time latching control strategies for the solo Duck wave energy converter in irregular waves," Applied Energy, Elsevier, vol. 222(C), pages 717-728.
    4. Liu, Yue & Zhang, Xiantao & Dong, Qing & Chen, Gang & Li, Xin, 2024. "Phase-resolved wave prediction with linear wave theory and physics-informed neural networks," Applied Energy, Elsevier, vol. 355(C).
    5. Graff, Mario & Peña, Rafael & Medina, Aurelio & Escalante, Hugo Jair, 2014. "Wind speed forecasting using a portfolio of forecasters," Renewable Energy, Elsevier, vol. 68(C), pages 550-559.
    6. Guo, Bingyong & Patton, Ron J. & Jin, Siya & Lan, Jianglin, 2018. "Numerical and experimental studies of excitation force approximation for wave energy conversion," Renewable Energy, Elsevier, vol. 125(C), pages 877-889.

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