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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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