Decomposition-Based Multistep Sea Wind Speed Forecasting Using Stacked Gated Recurrent Unit Improved by Residual Connections

Sea wind speed forecast is important for meteorological navigation system to keep ships in safe areas. The high volatility and uncertainty of wind make it difficult to accurately forecast multistep wind speed. This paper proposes a new decomposition-based model to forecast hourly sea wind speeds. Be...

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Autores principales:	Jupeng Xie, Huajun Zhang, Linfan Liu, Mengchuan Li, Yixin Su
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi-Wiley 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/9d4e933c8e4a43738d2d1691343d4c2e
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spelling	oai:doaj.org-article:9d4e933c8e4a43738d2d1691343d4c2e2021-11-29T00:55:44ZDecomposition-Based Multistep Sea Wind Speed Forecasting Using Stacked Gated Recurrent Unit Improved by Residual Connections1099-052610.1155/2021/2727218https://doaj.org/article/9d4e933c8e4a43738d2d1691343d4c2e2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/2727218https://doaj.org/toc/1099-0526Sea wind speed forecast is important for meteorological navigation system to keep ships in safe areas. The high volatility and uncertainty of wind make it difficult to accurately forecast multistep wind speed. This paper proposes a new decomposition-based model to forecast hourly sea wind speeds. Because mode mixing affects the accuracy of the empirical mode decomposition- (EMD-) based models, this model uses the variational mode decomposition (VMD) to alleviate this problem. To improve the accuracy of predicting subseries with high nonlinearity, this model uses stacked gate recurrent units (GRU) networks. To alleviate the degradation effect of stacked GRU, this model modifies them by adding residual connections to the deep layers. This model decomposes the nonlinear wind speed data into four subseries with different frequencies adaptively. Each stacked GRU predictor has four layers and the residual connections are added to the last two layers. The predictors have 24 inputs and 3 outputs, and the forecast is an ensemble of five predictors’ outputs. The proposed model can predict wind speed in the next 3 hours according to the past 24 hours’ wind speed data. The experiment results on three different sea areas show that the performance of this model surpasses those of a state-of-the-art model, several benchmarks, and decomposition-based models.Jupeng XieHuajun ZhangLinfan LiuMengchuan LiYixin SuHindawi-WileyarticleElectronic computers. Computer scienceQA75.5-76.95ENComplexity, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 Jupeng Xie Huajun Zhang Linfan Liu Mengchuan Li Yixin Su Decomposition-Based Multistep Sea Wind Speed Forecasting Using Stacked Gated Recurrent Unit Improved by Residual Connections
description	Sea wind speed forecast is important for meteorological navigation system to keep ships in safe areas. The high volatility and uncertainty of wind make it difficult to accurately forecast multistep wind speed. This paper proposes a new decomposition-based model to forecast hourly sea wind speeds. Because mode mixing affects the accuracy of the empirical mode decomposition- (EMD-) based models, this model uses the variational mode decomposition (VMD) to alleviate this problem. To improve the accuracy of predicting subseries with high nonlinearity, this model uses stacked gate recurrent units (GRU) networks. To alleviate the degradation effect of stacked GRU, this model modifies them by adding residual connections to the deep layers. This model decomposes the nonlinear wind speed data into four subseries with different frequencies adaptively. Each stacked GRU predictor has four layers and the residual connections are added to the last two layers. The predictors have 24 inputs and 3 outputs, and the forecast is an ensemble of five predictors’ outputs. The proposed model can predict wind speed in the next 3 hours according to the past 24 hours’ wind speed data. The experiment results on three different sea areas show that the performance of this model surpasses those of a state-of-the-art model, several benchmarks, and decomposition-based models.
format	article
author	Jupeng Xie Huajun Zhang Linfan Liu Mengchuan Li Yixin Su
author_facet	Jupeng Xie Huajun Zhang Linfan Liu Mengchuan Li Yixin Su
author_sort	Jupeng Xie
title	Decomposition-Based Multistep Sea Wind Speed Forecasting Using Stacked Gated Recurrent Unit Improved by Residual Connections
title_short	Decomposition-Based Multistep Sea Wind Speed Forecasting Using Stacked Gated Recurrent Unit Improved by Residual Connections
title_full	Decomposition-Based Multistep Sea Wind Speed Forecasting Using Stacked Gated Recurrent Unit Improved by Residual Connections
title_fullStr	Decomposition-Based Multistep Sea Wind Speed Forecasting Using Stacked Gated Recurrent Unit Improved by Residual Connections
title_full_unstemmed	Decomposition-Based Multistep Sea Wind Speed Forecasting Using Stacked Gated Recurrent Unit Improved by Residual Connections
title_sort	decomposition-based multistep sea wind speed forecasting using stacked gated recurrent unit improved by residual connections
publisher	Hindawi-Wiley
publishDate	2021
url	https://doaj.org/article/9d4e933c8e4a43738d2d1691343d4c2e
work_keys_str_mv	AT jupengxie decompositionbasedmultistepseawindspeedforecastingusingstackedgatedrecurrentunitimprovedbyresidualconnections AT huajunzhang decompositionbasedmultistepseawindspeedforecastingusingstackedgatedrecurrentunitimprovedbyresidualconnections AT linfanliu decompositionbasedmultistepseawindspeedforecastingusingstackedgatedrecurrentunitimprovedbyresidualconnections AT mengchuanli decompositionbasedmultistepseawindspeedforecastingusingstackedgatedrecurrentunitimprovedbyresidualconnections AT yixinsu decompositionbasedmultistepseawindspeedforecastingusingstackedgatedrecurrentunitimprovedbyresidualconnections
_version_	1718407786702831616

Decomposition-Based Multistep Sea Wind Speed Forecasting Using Stacked Gated Recurrent Unit Improved by Residual Connections

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