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AuthorGao, Ruobin
AuthorLi, Ruilin
AuthorHu, Minghui
AuthorSuganthan, Ponnuthurai Nagaratnam
AuthorYuen, Kum Fai
Available date2023-02-08T07:52:15Z
Publication Date2023-01-01
Publication NameApplied Energy
Identifierhttp://dx.doi.org/10.1016/j.apenergy.2022.120261
CitationGao, R., Li, R., Hu, M., Suganthan, P. N., & Yuen, K. F. (2023). Dynamic ensemble deep echo state network for significant wave height forecasting. Applied Energy, 329, 120261.‏
ISSN03062619
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85141471856&origin=inward
URIhttp://hdl.handle.net/10576/39802
AbstractForecasts of the wave heights can assist in the data-driven control of wave energy systems. However, the dynamic properties and extreme fluctuations of the historical observations pose challenges to the construction of forecasting models. This paper proposes a novel dynamic ensemble deep Echo state networks (ESN) to learn the dynamic characteristics of the significant wave height. The dynamic ensemble ESN creates a profound representation of the input and trains an independent readout module for each reservoir. To begin, numerous reservoir layers are built in a hierarchical order, adopting a reservoir pruning approach to filter out the poorer representations. Finally, a dynamic ensemble block is used to integrate the forecasts of all readout layers. The suggested model has been tested on twelve available datasets and statistically outperforms state-of-the-art approaches.
Languageen
PublisherElsevier Ltd
SubjectDeep learning
Echo state network
Forecasting
Machine learning
Randomized neural networks
TitleDynamic ensemble deep echo state network for significant wave height forecasting
TypeArticle
Volume Number329
dc.accessType Abstract Only


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