DC FieldValueLanguage
dc.contributorDepartment of Logistics and Maritime Studies-
dc.creatorYan, Ren_US
dc.creatorWang, Sen_US
dc.creatorDu, Yen_US
dc.date.accessioned2021-05-13T08:32:00Z-
dc.date.available2021-05-13T08:32:00Z-
dc.identifier.issn1366-5545en_US
dc.identifier.urihttp://hdl.handle.net/10397/89884-
dc.language.isoenen_US
dc.publisherPergamon Pressen_US
dc.subjectFuel consumption predictionen_US
dc.subjectMachine learningen_US
dc.subjectRandom forest regressoren_US
dc.subjectShip fuel efficiencyen_US
dc.subjectShip speed optimizationen_US
dc.titleDevelopment of a two-stage ship fuel consumption prediction and reduction model for a dry bulk shipen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1en_US
dc.identifier.epage22en_US
dc.identifier.volume138en_US
dc.identifier.doi10.1016/j.tre.2020.101930en_US
dcterms.abstractShipping industry is the backbone of global trade. However, the large quantities of greenhouse gas emissions from shipping, such as carbon dioxide (CO2), cannot be ignored. In order to comply with the international environmental regulations as well as to increase commercial profits, shipping companies have stronger motivations to improve ship energy efficiency. In this study, a two-stage ship fuel consumption prediction and reduction model is proposed for a dry bulk ship. At the first stage, a fuel consumption prediction model based on random forest regressor is proposed and validated. The prediction model takes into account ship sailing speed, total cargo weight, and sea and weather conditions and then predicts hourly fuel consumption of the main engine. The mean absolute percentage error of the random forest regressor is 7.91%. At the second stage, a speed optimization model is developed based on the prediction model proposed at the first stage while guaranteeing the estimated arrival time to the destination port. Numerical experiment on two consecutive-8-day voyages shows that the proposed model can reduce ship fuel consumption by 2–7%. The reduction in ship fuel consumption will also lead to lower CO2 emissions.-
dcterms.accessRightsembargoed access-
dcterms.bibliographicCitationTransportation research. Part E, Logistics and transportation review, June 2020, v. 138, 101930, p. 1-22, https://doi.org/10.1016/j.tre.2020.101930en_US
dcterms.isPartOfTransportation research. Part E, Logistics and transportation reviewen_US
dcterms.issued2020-06-
dc.identifier.scopus2-s2.0-85083831952-
dc.identifier.eissn1878-5794en_US
dc.identifier.artn101930en_US
dc.description.validate202105 bchy-
dc.description.oaNot applicable-
dc.identifier.FolderNumbera0794-n04en_US
dc.identifier.SubFormID1654en_US
dc.description.fundingSourceOthers-
dc.description.fundingTextNSFC projects-
dc.description.pubStatusEarly release-
dc.date.embargo2023.06.30en_US
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