| 딥러닝을 이용한 주요항만별 LNG 벙커링 수요예측 연구 | |
| Demand Forecasting for Liquified Natural Gas Bunkering at Major Ports in South Korea | |
| 채기영* ․ 이철용**† | |
| Chae, Gi-Young* and Lee, Chul-Yong**† | |
| Environmental regulations on ship exhaust emissions are being strengthened by the International Maritime Organization (IMO) and the Korean government. Stakeholders consider liquified natural gas (LNG) to be the most realistic alternative to existing fuels. This study predicted the LNG bunkering demand of five major domestic ports: Busan Port, Ulsan Port, Incheon Port, Pyeongtaek and Dangjin Port, and Gwangyang Port. Forecasting using recent performance data and deep learning techniques found that the LNG bunkering demand at Busan Port will increase from 220,000 tons in 2025 to 580,000 tons in 2040. The demand for LNG bunkering at Busan Port was the greatest, followed by Gwangyang Port, Ulsan Port, Pyeongtaek and Dangjin Port, and Incheon Port. The results of this study can be used as important data for the establishment of government carbon-neutral policies and, in terms of industry, it can be used as key data for investment decisions regarding LNG bunkering facilities and LNG-powered ship construction. | |
| LNG Bunkering, Demand Forecasting, Port, Regulations for Ship Exhaust Emissions, Deep Learning | |
 1305-11.pdf | |
| 2093-5919 | |
| 2586-2782 | |
| 2022-10 |
