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Korea Society for Naval Science and Technology - Vol. 7 , No. 1

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[ Article ]
Journal of the KNST - Vol. 5, No. 1, pp. 97-105
Abbreviation: KNST
ISSN: 2635-4926 (Print)
Print publication date 31 Mar 2022
Received 30 Jan 2022 Revised 02 Mar 2022 Accepted 24 Mar 2022
DOI: https://doi.org/10.31818/JKNST.2022.03.5.1.97
전기추진 선박의 발전 방향과 실데이터를 활용한 전력 부하 예측
진성훈¹ ; 홍창우²^{, *}

1해군 소령/잠수함사령부

2해군 소령/해군사관학교 기계시스템공학과 교수
Electric Power Load Forecasting Using Real Data and the Recent Advancements of Electric Propulsion Ships
Sung Hoon Jin¹ ; Chang Woo Hong²^{, *}
1LCDR, ROK Navy/Submarine Force Command

2LCDR, ROK Navy/Professor, Dept. of Mechanical System Engineering, ROK Naval Academy

Correspondence to : ^*Chang Woo Hong Republic of Korea Naval Academy PO box number 88-4-1, 1 Jungwon-ro, Jinhae-gu, Changwon-si, Gyungnam, 51704, Republic of Korea Tel: +82-55-907-5304 E-mail: spearw@navy.ac.kr
© 2022 Korea Society for Naval Science & Technology

초록

본 논문에서는 가스 운반선의 실데이터를 활용한 전력 부하 예측 실험결과를 도출하였다. 먼저 전기추진 함정의 도입으로 인한 전기에너지 중요성과 패러다임의 변화를 소개하고, 전력 부하 예측기법과 예측 사례를 살펴보았다. 전력 부하 예측 실험은 가스 운반선의 실데이터를 활용하여 딥러닝으로 수행하였으며 높은 정확도를 도출하였다. 이후 대한민국 해군에서의 적용과 향후 발전 방향에 대해 논의하였다.

Abstract

In this paper, the results of the electric power load forecasting experiment using the actual data of the gas carrier were derived. First, the importance of electric energy and paradigm changes due to the introduction of electric propulsion vessels were introduced, and electric power load forecasting techniques and prediction cases were examined. The electric power load forecasting experiment was performed through deep learning using the actual data of the gas carrier, and high accuracy was derived. After that, the application of the Korean Navy and the direction of future development were discussed.


Keywords: DTC(Decision Tree Classification), All Electric Ship, Electric Power Load Forecasting, Gas Carrier, Deep Learning 키워드: 전기추진 함정, 전력 부하 예측, 가스 운반선, 딥 러닝

Acknowledgments

이 논문은 2021년도 해군 군수사령부 함정기술지에 발표된 내용을 편집한 논문임.

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