Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/14180
DC FieldValueLanguage
dc.contributor.authorPanapakidis, Ioannis P.-
dc.contributor.authorMichailides, Constantine-
dc.contributor.authorAngelides, Demos C.-
dc.date.accessioned2019-06-30T06:55:11Z-
dc.date.available2019-06-30T06:55:11Z-
dc.date.issued2019-04-
dc.identifier.citationElectronics, 2019, vol. 8, no. 4en_US
dc.identifier.issn20799292-
dc.descriptionThis article belongs to the Special Issue Deep Learning Applications with Practical Measured Results in Electronics Industriesen_US
dc.description.abstractWind speed forecasting is an important element for the further development of offshore wind turbines. Due to its importance, many researchers have proposed different models for wind speed forecasting that differ in terms of the time-horizon of the forecast, types and number of inputs, complexity, structure, and others. Wind speed series present high nonlinearity and volatilities, and thus an effective model should successfully deal with those features. An approach to deal with the nonlinearities and volatilities is to utilize a time series processing technique such as the wavelet transform. In the present paper, an ensemble data-driven short-term wind speed forecasting model is developed, tested and applied. The term “ensemble” refers to the combination of two different predictors that run in parallel and the prediction is obtained by the predictor that leads to the lowest error. The proposed model utilizes the wavelet transform and is compared with other models that have been presented in the related literature and outperforms their accuracy. The proposed forecasting model can be used effectively for 1 min and 10 min ahead horizon wind speed predictions.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofElectronicsen_US
dc.rights© This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly citeden_US
dc.subjectComputational intelligenceen_US
dc.subjectOffshore winden_US
dc.subjectForecastingen_US
dc.subjectMachine learningen_US
dc.subjectNeural networksen_US
dc.subjectNeuro-fuzzy systemsen_US
dc.titleA data-driven short-term forecasting model for offshore wind speed prediction based on computational intelligenceen_US
dc.typeArticleen_US
dc.collaborationUniversity of Thessalyen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationAristotle University of Thessalonikien_US
dc.subject.categoryElectrical Engineering - Electronic Engineering - Information Engineeringen_US
dc.journalsOpen Accessen_US
dc.countryGreeceen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.3390/electronics8040420en_US
dc.relation.issue4en_US
dc.relation.volume8en_US
cut.common.academicyear2018-2019en_US
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.journal.journalissn2079-9292-
crisitem.journal.publisherMDPI-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-2016-9079-
crisitem.author.parentorgFaculty of Engineering and Technology-
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