Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management
The LNG price is basically determined based on the oil price, but other than that, it is also determined by the influence of the method of LNG transportation; storage; processes; and political, economic, and geographical instability. Liquefied natural gas (LNG) may not reflect its market value if th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/10/12/1814 |
| _version_ | 1797456963757408256 |
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| author | Kyunghwan Kim Sangseop Lim Chang-hee Lee Won-Ju Lee Hyeonmin Jeon Jinwon Jung Dongho Jung |
| author_facet | Kyunghwan Kim Sangseop Lim Chang-hee Lee Won-Ju Lee Hyeonmin Jeon Jinwon Jung Dongho Jung |
| author_sort | Kyunghwan Kim |
| collection | DOAJ |
| description | The LNG price is basically determined based on the oil price, but other than that, it is also determined by the influence of the method of LNG transportation; storage; processes; and political, economic, and geographical instability. Liquefied natural gas (LNG) may not reflect its market value if the destination of the purchase is restricted or the purchase contract includes a take-or-pay clause. Furthermore, it is difficult for the buyer to flexibly manage procurement, resulting in the decoupling of oil and natural gas prices. Therefore, as the LNG bunker price is expected to be more volatile than the marine bunker price in the future, shipping companies need to prepare countermeasures based on scientific forecasting techniques. This study aims to be the first to analyze the forecasting of short-term LNG bunker prices using recurrent neural network (RNN) models suitable for highly volatile data such as time series. Predictive analysis was performed using simple RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) models, which effectively forecast time-series data, and the prediction performance of LSTM among the three models was excellent. LSTM had relatively excellent prediction performance of outliers and beyond. In addition, it was possible to effectively manage ship operating costs with improved forecasting in practice. Furthermore, this study contributes to establishing a systematic strategy for supervisors in global shipping companies, port authorities, and LNG bunkering companies. |
| first_indexed | 2024-03-09T16:15:19Z |
| format | Article |
| id | doaj.art-e2a842d8554e446f9464b8c834cde440 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T16:15:19Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-e2a842d8554e446f9464b8c834cde4402023-11-24T15:54:46ZengMDPI AGJournal of Marine Science and Engineering2077-13122022-11-011012181410.3390/jmse10121814Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement ManagementKyunghwan Kim0Sangseop Lim1Chang-hee Lee2Won-Ju Lee3Hyeonmin Jeon4Jinwon Jung5Dongho Jung6College of Maritime Sciences, Korea Maritime & Ocean University, Busan 49112, Republic of KoreaCollege of Maritime Sciences, Korea Maritime & Ocean University, Busan 49112, Republic of KoreaCollege of Maritime Sciences, Korea Maritime & Ocean University, Busan 49112, Republic of KoreaCollege of Maritime Sciences, Korea Maritime & Ocean University, Busan 49112, Republic of KoreaCollege of Maritime Sciences, Korea Maritime & Ocean University, Busan 49112, Republic of KoreaFuel Gas Technology Center Carbon Neutrality Technology Research Team, Busan Mieum Headquarters, Korea Marine Equipment Research Institute, Busan 49111, Republic of KoreaOffshore Platform Research Division, Korea Research Institute of Ship and Ocean Engineering, KRISO, Daejeon 34103, Republic of KoreaThe LNG price is basically determined based on the oil price, but other than that, it is also determined by the influence of the method of LNG transportation; storage; processes; and political, economic, and geographical instability. Liquefied natural gas (LNG) may not reflect its market value if the destination of the purchase is restricted or the purchase contract includes a take-or-pay clause. Furthermore, it is difficult for the buyer to flexibly manage procurement, resulting in the decoupling of oil and natural gas prices. Therefore, as the LNG bunker price is expected to be more volatile than the marine bunker price in the future, shipping companies need to prepare countermeasures based on scientific forecasting techniques. This study aims to be the first to analyze the forecasting of short-term LNG bunker prices using recurrent neural network (RNN) models suitable for highly volatile data such as time series. Predictive analysis was performed using simple RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) models, which effectively forecast time-series data, and the prediction performance of LSTM among the three models was excellent. LSTM had relatively excellent prediction performance of outliers and beyond. In addition, it was possible to effectively manage ship operating costs with improved forecasting in practice. Furthermore, this study contributes to establishing a systematic strategy for supervisors in global shipping companies, port authorities, and LNG bunkering companies.https://www.mdpi.com/2077-1312/10/12/1814liquefied natural gasbunker pricelong short-term memoryrecurrent neural networkgated recurrent unitforecasting |
| spellingShingle | Kyunghwan Kim Sangseop Lim Chang-hee Lee Won-Ju Lee Hyeonmin Jeon Jinwon Jung Dongho Jung Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management Journal of Marine Science and Engineering liquefied natural gas bunker price long short-term memory recurrent neural network gated recurrent unit forecasting |
| title | Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management |
| title_full | Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management |
| title_fullStr | Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management |
| title_full_unstemmed | Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management |
| title_short | Forecasting Liquefied Natural Gas Bunker Prices Using Artificial Neural Network for Procurement Management |
| title_sort | forecasting liquefied natural gas bunker prices using artificial neural network for procurement management |
| topic | liquefied natural gas bunker price long short-term memory recurrent neural network gated recurrent unit forecasting |
| url | https://www.mdpi.com/2077-1312/10/12/1814 |
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