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To accurately predict the fuel consumption of in service ships, analyze the complex and variable influencing factors of fuel consumption, and quantify their respective impacts, this study selects tankers and bulk carriers for operational data collection and preprocessing. A fuel consumption prediction model based on the Extreme Gradient Boosting (XGBoost) algorithm is established, and factor importance is evaluated using the Gain method. The results demonstrate that the proposed model achieves strong computational and predictive performance, with mean absolute percentage errors of 4.88% and 3.92% for the tanker and bulk carrier models, respectively. Among internal factors, ship speed shows the greatest influence, with weights of 0.671 and 0.429 for the two vessel types. Regarding external factors, navigation environment conditions such as wind and waves also exhibit significant impacts.

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为准确预测在航船舶油耗水平,分析油耗复杂多变影响因素,量化各因素影响程度,选取油船和散货船分别进行营运数据采集与预处理,建立一种基于极端梯度提升树(XGBoost)算法的船舶油耗预测模型,采用XGBoost内置增益方法(Gain)完成影响程度分析。结果表明,所提出的模型预测性能良好,两类船舶油耗预测模型的平均绝对百分比误差分别为4.88%、3.92%;内部因素中,船速影响最大,权重分别为0.671和0.429;外部因素中,风、浪等航运环境影响较大。

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于淳(1984—),男,高级工程师,研究方向为船舶能效与温室气体评估技术。E-mail:
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常圣岱(1992—),男,工程师,研究方向为船舶能效与温室气体排放评估技术。E-mail:

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基于XGBoost模型的在航船舶油耗预测与影响因素分析
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常圣岱 1 , 孙永刚 1 , 于淳 2
中国航海 | 绿色航运 2025,48(4): 176-182
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中国航海 | 绿色航运 2025, 48(4): 176-182
基于XGBoost模型的在航船舶油耗预测与影响因素分析
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常圣岱1 , 孙永刚1, 于淳2
作者信息
  • 1. 中国船级社 科创试验中心,北京 100007
  • 2. 中国船级社 科技和信息处,北京 100007
  • 常圣岱(1992—),男,工程师,研究方向为船舶能效与温室气体排放评估技术。E-mail:

通讯作者:

于淳(1984—),男,高级工程师,研究方向为船舶能效与温室气体评估技术。E-mail:
Based on the XGBoost model: prediction of fuel consumption of underway ships and analysis of influencing factors
Shengdai CHANG1 , Yonggang SUN1, Chun YU2
Affiliations
  • 1.Science & Technology Innovation and Test Center, China Classification Society, 100007, Beijing
  • 2.Technology & Information Department, China Classification Society, 100007, Beijing
出版时间: 2025-12-25 doi: 10.3969/j.issn.1000-4653.2025.04.020
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为准确预测在航船舶油耗水平,分析油耗复杂多变影响因素,量化各因素影响程度,选取油船和散货船分别进行营运数据采集与预处理,建立一种基于极端梯度提升树(XGBoost)算法的船舶油耗预测模型,采用XGBoost内置增益方法(Gain)完成影响程度分析。结果表明,所提出的模型预测性能良好,两类船舶油耗预测模型的平均绝对百分比误差分别为4.88%、3.92%;内部因素中,船速影响最大,权重分别为0.671和0.429;外部因素中,风、浪等航运环境影响较大。

船舶油耗影响因素  /  油耗预测  /  XGBoost

To accurately predict the fuel consumption of in service ships, analyze the complex and variable influencing factors of fuel consumption, and quantify their respective impacts, this study selects tankers and bulk carriers for operational data collection and preprocessing. A fuel consumption prediction model based on the Extreme Gradient Boosting (XGBoost) algorithm is established, and factor importance is evaluated using the Gain method. The results demonstrate that the proposed model achieves strong computational and predictive performance, with mean absolute percentage errors of 4.88% and 3.92% for the tanker and bulk carrier models, respectively. Among internal factors, ship speed shows the greatest influence, with weights of 0.671 and 0.429 for the two vessel types. Regarding external factors, navigation environment conditions such as wind and waves also exhibit significant impacts.

ship fuel consumption influencing factors  /  fuel consumption prediction  /  XGBoost
常圣岱, 孙永刚, 于淳. 基于XGBoost模型的在航船舶油耗预测与影响因素分析. 中国航海, 2025 , 48 (4) : 176 -182 . DOI: 10.3969/j.issn.1000-4653.2025.04.020
Shengdai CHANG, Yonggang SUN, Chun YU. Based on the XGBoost model: prediction of fuel consumption of underway ships and analysis of influencing factors[J]. Navigation of China, 2025 , 48 (4) : 176 -182 . DOI: 10.3969/j.issn.1000-4653.2025.04.020
  • 国家重点研发计划项目(2022YFB4300801)
2025年第48卷第4期
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doi: 10.3969/j.issn.1000-4653.2025.04.020
  • 接收时间:2024-05-24
  • 首发时间:2026-03-17
  • 出版时间:2025-12-25
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  • 收稿日期:2024-05-24
基金
国家重点研发计划项目(2022YFB4300801)
作者信息
    1. 中国船级社 科创试验中心,北京 100007
    2. 中国船级社 科技和信息处,北京 100007

通讯作者:

于淳(1984—),男,高级工程师,研究方向为船舶能效与温室气体评估技术。E-mail:
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2种不同金属材料的力学参数

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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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