{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T18:18:38Z","timestamp":1770747518116,"version":"3.49.0"},"reference-count":43,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T00:00:00Z","timestamp":1636934400000},"content-version":"vor","delay-in-days":318,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>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\u2010based model to forecast hourly sea wind speeds. Because mode mixing affects the accuracy of the empirical mode decomposition\u2010 (EMD\u2010) 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\u2019 outputs. The proposed model can predict wind speed in the next 3 hours according to the past 24 hours\u2019 wind speed data. The experiment results on three different sea areas show that the performance of this model surpasses those of a state\u2010of\u2010the\u2010art model, several benchmarks, and decomposition\u2010based models.<\/jats:p>","DOI":"10.1155\/2021\/2727218","type":"journal-article","created":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T01:50:09Z","timestamp":1637027409000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Decomposition\u2010Based Multistep Sea Wind Speed Forecasting Using Stacked Gated Recurrent Unit Improved by Residual Connections"],"prefix":"10.1155","volume":"2021","author":[{"given":"Jupeng","family":"Xie","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5135-8198","authenticated-orcid":false,"given":"Huajun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Linfan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Mengchuan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yixin","family":"Su","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,11,15]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.5194\/nhess-17-2041-2017"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rpg.2018.5917"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2946414"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2020.112524"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.3015336"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2020.112824"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2018.07.041"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106996"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2014.11.011"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/tste.2014.2300150"},{"key":"e_1_2_9_11_2","doi-asserted-by":"crossref","unstructured":"SomanS. S. ZareipourH. MalikO. andMandalP. A review of wind power and wind speed forecasting methods with different time horizons Proceedings of the North American Power Symposium 2010 September 2010 Arlington TX USA https:\/\/doi.org\/10.1109\/naps.2010.5619586 2-s2.0-78650045524.","DOI":"10.1109\/NAPS.2010.5619586"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2014.03.033"},{"key":"e_1_2_9_13_2","doi-asserted-by":"crossref","unstructured":"YaoW. HuangP. andJiaZ. Multidimensional LSTM networks to predict wind speed Proceedings of the 2018 37th Chinese Control Conference (CCC) July 2018 Wuhan China https:\/\/doi.org\/10.23919\/chicc.2018.8484017 2-s2.0-85056097924.","DOI":"10.23919\/ChiCC.2018.8484017"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1002\/we.2405"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2020.125380"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1063\/1.5113555"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.3390\/en12020254"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2008.02.002"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2018.10.080"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.renene.2019.01.031"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106350"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.05.028"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1080\/0952813x.2013.813976"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.05.099"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2019.103176"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05141-w"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.1998.0193"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2018.01.010"},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/6856139"},{"key":"e_1_2_9_30_2","doi-asserted-by":"publisher","DOI":"10.1063\/5.0051965"},{"key":"e_1_2_9_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2288675"},{"key":"e_1_2_9_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40565-018-0471-8"},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_2_9_34_2","doi-asserted-by":"crossref","unstructured":"HeK. Deep residual learning for image recognition Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition June 2016 Las Vegas NV USA https:\/\/doi.org\/10.1109\/cvpr.2016.90 2-s2.0-84986274465.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_9_35_2","unstructured":"WuY. Google\u2032s neural machine translation system: bridging the gap between human and machine translation 2016 http:\/\/arxiv.org\/abs\/1609.08144."},{"key":"e_1_2_9_36_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.09.049"},{"key":"e_1_2_9_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2010.10.015"},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jappgeo.2012.05.002"},{"key":"e_1_2_9_39_2","unstructured":"ChungJ. Empirical evaluation of gated recurrent neural networks on sequence modeling 2014 http:\/\/arxiv.org\/abs\/1412.3555."},{"key":"e_1_2_9_40_2","doi-asserted-by":"publisher","DOI":"10.1002\/qj.3803"},{"key":"e_1_2_9_41_2","unstructured":"KingmaD. P.andBaJ. Adam: a method for stochastic optimization 2014 http:\/\/arxiv.org\/abs\/1412.6980."},{"key":"e_1_2_9_42_2","unstructured":"MastersD.andLuschiC. Revisiting small batch training for deep neural networks 2018 http:\/\/arxiv.org\/abs\/1804.07612."},{"key":"e_1_2_9_43_2","unstructured":"AbadiM. AgarwalA. BarhamP. BrevdoE. ChenZ. CitroC. CorradoG. S. DavisA. DeanJ. DevinM. GhemawatS. GoodfellowI. HarpA. IrvingG. IsardM. JiaY. JozefowiczR. KaiserL. KudlurM. LevenbergJ. ManeD. MongaR. MooreS. MurrayD. OlahC. SchusterM. ShlensJ. SteinerB. SutskeverI. TalwarK. TuckerP. VanhouckeV. VasudevanV. ViegasF. VinyalsO. WardenP. WattenbergM. WickeM. YuY. andZhengX. Tensorflow: large-scale machine learning on heterogeneous distributed systems 2016 http:\/\/arxiv.org\/abs\/1603.04467."}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/2727218.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2021\/2727218.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/2727218","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T22:30:08Z","timestamp":1723242608000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/2727218"}},"subtitle":[],"editor":[{"given":"Nishant","family":"Malik","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/2727218"],"URL":"https:\/\/doi.org\/10.1155\/2021\/2727218","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-05-17","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-10-29","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"2727218"}}