An application of the Fast Fourier Transform to the short-term prediction of sea wave behaviour
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DOI: 10.1016/j.renene.2010.11.035
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Cited by:
- 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.
- Zhang, Jincheng & Zhao, Xiaowei & Jin, Siya & Greaves, Deborah, 2022. "Phase-resolved real-time ocean wave prediction with quantified uncertainty based on variational Bayesian machine learning," Applied Energy, Elsevier, vol. 324(C).
- 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.
- 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).
- 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.
- 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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Keywords
Fourier transform; Wave prediction; Dispersion relationship; Non-harmonic modelling;All these keywords.
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