Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.4401
DC FieldValueLanguage
dc.contributor.authorEberle, Sebastian-
dc.contributor.authorCevasco, Debora-
dc.contributor.authorSchwarzkopf, Marie-Antoinette-
dc.contributor.authorHollm, Marten-
dc.contributor.authorSeifried, Robert-
dc.date.accessioned2022-06-20T12:31:42Z-
dc.date.available2022-06-20T12:31:42Z-
dc.date.issued2022-06-16-
dc.identifier.citationWind 2 (2) : 394-414 (2022-06-16)de_DE
dc.identifier.issn2674-032Xde_DE
dc.identifier.urihttp://hdl.handle.net/11420/12929-
dc.description.abstractIn the estimation of future investments in the offshore wind industry, the operation and maintenance (O&M) phase plays an important role. In the simulation of the O&M figures, the weather conditions should contain information about the waves’ main characteristics and the wind speed. As these parameters are correlated, they were simulated by using a multivariate approach, and thus by generating vectors of measurements. Four different stochastic weather time series generators were investigated: Markov chains (MC) of first and second order, vector autoregressive (VAR) models, and long short-term memory (LSTM) neural networks. The models were trained on a 40-year data set with 1 h resolution. Thereafter, the models simulated 25-year time series, which were analysed based on several time series metrics and criteria. The MC (especially the one of second order) and the VAR model were shown to be the ones capturing the characteristics of the original time series the best. The novelty of this paper lies in the application of LSTM models and multivariate higher-order MCs to generate offshore weather time series, and to compare their simulations to the ones of VAR models. Final recommendations for improving these models are provided as conclusion of this paper.en
dc.language.isoende_DE
dc.publisherMDPIde_DE
dc.relation.ispartofWindde_DE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/de_DE
dc.subject.ddc600: Technikde_DE
dc.subject.ddc620: Ingenieurwissenschaftende_DE
dc.titleMultivariate simulation of offshore weather time series: a comparison between markov chain, autoregressive, and long short-term memory modelsde_DE
dc.typeArticlede_DE
dc.identifier.doi10.15480/882.4401-
dc.type.diniarticle-
dcterms.DCMITypeText-
tuhh.identifier.urnurn:nbn:de:gbv:830-882.0188559-
tuhh.oai.showtruede_DE
tuhh.abstract.englishIn the estimation of future investments in the offshore wind industry, the operation and maintenance (O&M) phase plays an important role. In the simulation of the O&M figures, the weather conditions should contain information about the waves’ main characteristics and the wind speed. As these parameters are correlated, they were simulated by using a multivariate approach, and thus by generating vectors of measurements. Four different stochastic weather time series generators were investigated: Markov chains (MC) of first and second order, vector autoregressive (VAR) models, and long short-term memory (LSTM) neural networks. The models were trained on a 40-year data set with 1 h resolution. Thereafter, the models simulated 25-year time series, which were analysed based on several time series metrics and criteria. The MC (especially the one of second order) and the VAR model were shown to be the ones capturing the characteristics of the original time series the best. The novelty of this paper lies in the application of LSTM models and multivariate higher-order MCs to generate offshore weather time series, and to compare their simulations to the ones of VAR models. Final recommendations for improving these models are provided as conclusion of this paper.de_DE
tuhh.publisher.doi10.3390/wind2020021-
tuhh.publication.instituteMechanik und Meerestechnik M-13de_DE
tuhh.identifier.doi10.15480/882.4401-
tuhh.type.opus(wissenschaftlicher) Artikel-
dc.type.driverarticle-
dc.type.casraiJournal Article-
tuhh.container.issue2de_DE
tuhh.container.volume2de_DE
tuhh.container.startpage394de_DE
tuhh.container.endpage414de_DE
dc.rights.nationallicensefalsede_DE
local.status.inpressfalsede_DE
local.type.versionpublishedVersionde_DE
local.funding.infoThis research was supported by the Bundesministerium für Wirtschaft Und Klimaschutz (BMWK.IIC6) under the grant agreement no. 01186400/1, financial code 03EE3001B, for the ProBeKo project (subproject of the SOBeKo project). Furthermore, the second and third authors received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 815083 (COREWIND).de_DE
local.publisher.peerreviewedtruede_DE
datacite.resourceTypeJournal Article-
datacite.resourceTypeGeneralText-
item.cerifentitytypePublications-
item.openairetypeArticle-
item.mappedtypeArticle-
item.fulltextWith Fulltext-
item.grantfulltextopen-
item.creatorGNDEberle, Sebastian-
item.creatorGNDCevasco, Debora-
item.creatorGNDSchwarzkopf, Marie-Antoinette-
item.creatorGNDHollm, Marten-
item.creatorGNDSeifried, Robert-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.creatorOrcidEberle, Sebastian-
item.creatorOrcidCevasco, Debora-
item.creatorOrcidSchwarzkopf, Marie-Antoinette-
item.creatorOrcidHollm, Marten-
item.creatorOrcidSeifried, Robert-
item.languageiso639-1en-
crisitem.author.deptMechanik und Meerestechnik M-13-
crisitem.author.deptMechanik und Meerestechnik M-13-
crisitem.author.orcid0000-0002-2706-6662-
crisitem.author.orcid0000-0002-9368-9390-
crisitem.author.orcid0000-0001-6699-0551-
crisitem.author.orcid0000-0001-5139-8918-
crisitem.author.orcid0000-0001-5795-7610-
crisitem.author.parentorgStudiendekanat Maschinenbau-
crisitem.author.parentorgStudiendekanat Maschinenbau-
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