PREDICTION OF SIGNIFICANT WAVE HEIGHT BASED ON MULTIVARIABLE DSD-LSTM MODEL

Pang Junheng; Huang Weinan; Dong Sheng

doi:10.19912/j.0254-0096.tynxb.2023-0421

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (7) : 121-127. DOI: 10.19912/j.0254-0096.tynxb.2023-0421

PREDICTION OF SIGNIFICANT WAVE HEIGHT BASED ON MULTIVARIABLE DSD-LSTM MODEL

Pang Junheng, Huang Weinan, Dong Sheng

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Abstract

A new signal decomposition algorithm （DSD） is designed by using the ICEEMDAN and recursive quantification analysis method, which divides the original signal into deterministic and stochastic components. Considering the influence of wind speed and wind direction on wave height, a multi-variable DSD-LSTM model was established by combining DSD algorithm with Long and Short-Term Memory network （LSTM） to predict significant wave height. The proposed model significantly improved the prediction accuracy compared to the single LSTM model and has better prediction performance compared to the univariate hybrid model DSD-LSTM-u.

Key words

wave energy / wave height predictim / time series / signal processing / deep learning / long and short-memory network

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Pang Junheng, Huang Weinan, Dong Sheng. PREDICTION OF SIGNIFICANT WAVE HEIGHT BASED ON MULTIVARIABLE DSD-LSTM MODEL[J]. Acta Energiae Solaris Sinica. 2024, 45(7): 121-127 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0421

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