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

IF 3.3 Q2 TRANSPORTATION
Geunsub Kim , Gunwoo Lee , Seunghyun An , Joowon Lee
{"title":"Forecasting future electric power consumption in Busan New Port using a deep learning model","authors":"Geunsub Kim ,&nbsp;Gunwoo Lee ,&nbsp;Seunghyun An ,&nbsp;Joowon Lee","doi":"10.1016/j.ajsl.2023.04.001","DOIUrl":null,"url":null,"abstract":"<div><p>As 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.</p></div>","PeriodicalId":46505,"journal":{"name":"Asian Journal of Shipping and Logistics","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asian Journal of Shipping and Logistics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2092521223000184","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 0

Abstract

As 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.

使用深度学习模型预测釜山新港未来的电力消耗
随着智能环保技术和设备被引入海港行业,电力消耗预计将迅速增加。然而,关于制定港口电力管理计划,特别是与电力消耗预测有关的计划的研究却很少。为了解决这一差距,本研究预测了釜山新港(韩国最大的集装箱港口)未来的电力消耗,并将其与当前的标准电力供应能力进行了比较,研究了未来保持稳定电力供应的可行性。我们应用使用过去10年的电力消耗和吞吐量数据训练的长短期记忆(LSTM)模型来预测釜山新港未来的电力消耗。根据结果,到2040年,电力消耗预计将以年均4.9%的速度增长,超过了同期预计的4.7%的年吞吐量增长。鉴于这些结果,预计到2040年,目前的标准电力供应能力将仅达到需求的35%,这表明未来港口稳定运营将需要额外的电力供应设施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
7.80
自引率
6.50%
发文量
23
审稿时长
92 days
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信