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Forecasting future electric power consumption in Busan New Port using a deep learning model

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dc.contributor.authorKim, Geunsub-
dc.contributor.authorLee, Gunwoo-
dc.contributor.authorAn, Seunghyun-
dc.contributor.authorLee, Joowon-
dc.date.accessioned2023-07-05T05:42:33Z-
dc.date.available2023-07-05T05:42:33Z-
dc.date.issued2023-06-
dc.identifier.issn2092-5212-
dc.identifier.issn2352-4871-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/113225-
dc.description.abstractAs smart and environmentally friendly technologies and equipment are introduced in the sea port industry, electric power consumption is expected to rapidly increase. However, there is a paucity of research on the creation of electric power management plans, specifically in relation to electric power consumption forecasting, in ports. In order to address this gap, this study forecasts future electric power consumption in Busan New Port (South Korea's largest container port) and, comparing this with the current standard electric power supply capacity, investigated the feasibility of maintaining a stable electric power supply in the future. We applied a Long Short-Term Memory (LSTM) model trained using electric power consumption and throughput data of the last 10 years to forecast the future electric power consumption of Busan New Port. According to the results, electric power consumption is expected to increase at an annual average of 4.9 % until 2040, exceeding the predicted annual 4.7 % increase in throughput during the same period. Given these results, the current standard electric power supply capacity is forecast to reach only 35 % of demand in 2040, indicating that additional electrical power supply facilities will be needed for stable port operation in the future. © 2023 The Authors-
dc.format.extent16-
dc.language영어-
dc.language.isoENG-
dc.publisherKorean Association of Shipping and Logistics, Inc.-
dc.titleForecasting future electric power consumption in Busan New Port using a deep learning model-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.1016/j.ajsl.2023.04.001-
dc.identifier.scopusid2-s2.0-85156136046-
dc.identifier.wosid001009572500001-
dc.identifier.bibliographicCitationAsian Journal of Shipping and Logistics, v.39, no.2, pp 78 - 93-
dc.citation.titleAsian Journal of Shipping and Logistics-
dc.citation.volume39-
dc.citation.number2-
dc.citation.startPage78-
dc.citation.endPage93-
dc.type.docTypeArticle-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClassesci-
dc.relation.journalResearchAreaTransportation-
dc.relation.journalWebOfScienceCategoryTransportation-
dc.subject.keywordPlusARTIFICIAL NEURAL-NETWORK-
dc.subject.keywordPlusENERGY MANAGEMENT-
dc.subject.keywordPlusEFFICIENCY-
dc.subject.keywordPlusTECHNOLOGIES-
dc.subject.keywordPlusPERFORMANCE-
dc.subject.keywordPlusSIMULATION-
dc.subject.keywordPlusEQUIPMENT-
dc.subject.keywordPlusSEAPORTS-
dc.subject.keywordAuthorAlternative marine power-
dc.subject.keywordAuthorBusan new port-
dc.subject.keywordAuthorDeep learning model-
dc.subject.keywordAuthorLong-short-term memory model-
dc.subject.keywordAuthorSeaport, electrical power consumption-
dc.subject.keywordAuthorSupply and demand-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S2092521223000184-
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ERICA 공학대학 (DEPARTMENT OF TRANSPORTATION AND LOGISTICS ENGINEERING)
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