Short-term offshore wind speed forecasting approach based on multi-stage decomposition and deep residual network with self-attention

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dc.contributor.authorAcikgoz, Hakan
dc.contributor.authorKorkmaz, Deniz
dc.date.accessioned2026-06-19T06:39:59Z
dc.date.available2026-06-19T06:39:59Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractWind energy is one of the widely used renewable energy systems. Wind speed forecasting is used to produce of wind energy and to ensure the sustainability of the power system. However, offshore wind speed forecasting is a challenging task with complex variables and highly nonlinear temporal dynamics of the ocean. This paper proposes a hybrid and robust offshore wind speed forecasting approach based on multi-stage decomposition, deep convolutional neural network (CNN), and extreme learning machine (ELM). Unlike conventional preprocessing for forecasting of renewable energy problems, the proposed approach combines two efficient decomposition methods as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and ensemble empirical mode decomposition (EEMD). This method can decompose high-frequency and lowfrequency components of the wind speed. While high-frequency components are decomposed with the EEMD, low-frequency components are directly sent to the ELM model. The obtained mode functions from the EEMD are then fed to the designed network for forecasting. The CNN model is constructed with the deep residual network and self-attention (SA) mechanism to improve the network performance. In the comparative evaluations, while other approaches give lower forecasting performance between 0.8233 and 2.1885 for the root mean square error (RMSE), the proposed method presents the lowest RMSE value as 0.5400. The experimental results show that the proposed method exhibits more accurate and robust forecasting performance compared with other model combinations and deep learning models.
dc.identifier.doi10.1016/j.engappai.2025.110313
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.orcid0000-0002-6432-7243
dc.identifier.orcid0000-0002-5159-0659
dc.identifier.scopus2-s2.0-85217943460
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.engappai.2025.110313
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5896
dc.identifier.volume146
dc.identifier.wosWOS:001482111400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofEngineering Applications of Artificial Intelligence
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260612
dc.subjectOffshore Wind Speed Forecasting
dc.subjectMulti-Stage Decomposition
dc.subjectDeep Residual Network
dc.subjectDeep Learning
dc.subjectSelf-Attention Mechanism
dc.titleShort-term offshore wind speed forecasting approach based on multi-stage decomposition and deep residual network with self-attention
dc.typeArticle

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