Neural network-based fuel consumption estimation for container ships in Korea
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Le, Luan Thanh | - |
dc.contributor.author | Lee, Gunwoo | - |
dc.contributor.author | Park, Keun-Sik | - |
dc.contributor.author | Kim, Hwayoung | - |
dc.date.accessioned | 2021-06-22T06:01:17Z | - |
dc.date.available | 2021-06-22T06:01:17Z | - |
dc.date.created | 2021-01-21 | - |
dc.date.issued | 2020-07-03 | - |
dc.identifier.issn | 0308-8839 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/1001 | - |
dc.description.abstract | Due to the outstanding strength of advanced machine-learning techniques, they have become increasingly common in predictive studies in recent years, particularly in predicting ship energy performance. In constructing predictive models, prior studies have mostly employed vessels' technical parameters to establish machine-learning algorithms. To bridge this research gap and enable wider applications, this paper presents the design of a multilayer perceptron artificial neural network (MLP ANN) as a machine-learning technique to estimate ship fuel consumption. We utilized the real operational data from 100-143 container ships to estimate fuel consumption for five different container ships grouped by size. We compared the performance of two ANN models and two multiple-regression models. Four input parameters (sailing time, speed, cargo weight, and capacity) were included in the first ANN and the first regression model, while the other two models only consider two inputs from physical function. The mean absolute percentage error of the ANN models with four inputs was the smallest and less than those in extended statistical models, demonstrating the MLP's superiority over the statistical model. The MLP ANN model can thus be applied to confirm the effectiveness of the slow-steaming method for achieving energy efficiency. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD | - |
dc.subject | ENGINE PERFORMANCE | - |
dc.subject | BAYESIAN NETWORK | - |
dc.subject | CROSS-VALIDATION | - |
dc.subject | OPTIMIZATION | - |
dc.subject | SYSTEM | - |
dc.subject | MODEL | - |
dc.subject | PREDICTION | - |
dc.subject | EFFICIENCY | - |
dc.subject | EMISSIONS | - |
dc.subject | CREAM | - |
dc.title | Neural network-based fuel consumption estimation for container ships in Korea | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lee, Gunwoo | - |
dc.identifier.doi | 10.1080/03088839.2020.1729437 | - |
dc.identifier.scopusid | 2-s2.0-85088618395 | - |
dc.identifier.wosid | 000515043800001 | - |
dc.identifier.bibliographicCitation | MARITIME POLICY & MANAGEMENT, v.47, no.5, pp.615 - 632 | - |
dc.relation.isPartOf | MARITIME POLICY & MANAGEMENT | - |
dc.citation.title | MARITIME POLICY & MANAGEMENT | - |
dc.citation.volume | 47 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 615 | - |
dc.citation.endPage | 632 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Transportation | - |
dc.relation.journalWebOfScienceCategory | Transportation | - |
dc.subject.keywordPlus | ENGINE PERFORMANCE | - |
dc.subject.keywordPlus | BAYESIAN NETWORK | - |
dc.subject.keywordPlus | CROSS-VALIDATION | - |
dc.subject.keywordPlus | OPTIMIZATION | - |
dc.subject.keywordPlus | SYSTEM | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | EFFICIENCY | - |
dc.subject.keywordPlus | EMISSIONS | - |
dc.subject.keywordPlus | CREAM | - |
dc.subject.keywordAuthor | Fuel consumption prediction | - |
dc.subject.keywordAuthor | container ships | - |
dc.subject.keywordAuthor | artificial neural network | - |
dc.subject.keywordAuthor | multilayer perceptron | - |
dc.subject.keywordAuthor | liner shipping | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.