Cross-scale fusion network and two-stage decomposition for power forecasting of offshore wind turbines

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dc.contributor.authorKorkmaz, Deniz
dc.contributor.authorAcikgoz, Hakan
dc.contributor.authorUstundag, Mehmet
dc.date.accessioned2026-06-19T06:39:45Z
dc.date.available2026-06-19T06:39:45Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractThis research presents a hybrid forecasting model for offshore wind power, which is based on a two-stage decomposition process and a densely connected convolutional network. Initially, the offshore wind power data is decomposed into several components utilizing an improved complete ensemble empirical mode decomposition with adaptive noise. The high-frequency component is further divided into multiple components through empirical mode decomposition. Following the decomposition, the dataset is transformed into the HSV color space. The proposed model features a sequential and multi-scale convolutional block architecture, inspired by the clique network approach. Furthermore, a squeeze-and-excitation module is incorporated to enhance the network performance. Comparative experiments are conducted against state-of-the-art deep learning models using data from two offshore wind turbines. The results indicate that the proposed model achieves superior performance metrics for WT3 and WT4, with root mean square error, mean absolute error, and mean absolute percentage error values ranging from 4.4796 to 4.8578, 3.2736 to 3.6543, and 0.2127 to 0.2193 for 1-h ahead forecast; 4.9674 to 5.7693, 3.5980 to 4.2028, and 0.2214 to 0.2295 for 3-h ahead forecast; and 5.8889 to 5.6338, 4.4247 to 4.1148, and 0.3064 to 0.2436 for 5-h ahead forecast, respectively. This pioneering two-stage decomposition and cross-scale CNN outperforms benchmarks by up to 74 % in RMSE. The proposed methodology improves short-term offshore wind power prediction by removing irregularities in the datasets.
dc.identifier.doi10.1016/j.oceaneng.2025.122541
dc.identifier.issn0029-8018
dc.identifier.issn1873-5258
dc.identifier.orcid0000-0002-5159-0659
dc.identifier.orcid0000-0002-6432-7243
dc.identifier.orcid0000-0003-4936-7690
dc.identifier.scopus2-s2.0-105013847334
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.oceaneng.2025.122541
dc.identifier.urihttps://hdl.handle.net/20.500.12899/5765
dc.identifier.volume341
dc.identifier.wosWOS:001595308000006
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofOcean Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260612
dc.subjectOffshore Wind Power Forecasting
dc.subjectMulti-Stage Decomposition
dc.subjectDense Network
dc.subjectClique Network
dc.subjectSqueeze And Excitation Module
dc.titleCross-scale fusion network and two-stage decomposition for power forecasting of offshore wind turbines
dc.typeArticle

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