Accurate, reliable, and real-time prediction of ship fuel consumption is the basis and premise of the development of fuel optimization; however, ship fuel consumption data mainly come from noon reports, and many current modeling methods have been based on a single model; therefore they have low accuracy and robustness. In this study, we propose a novel hybrid fuel consumption prediction model based on sensor data collected from an ocean-going container ship. First, a data processing method is proposed to clean the collected data. Secondly, the Bayesian optimization method of hyperparameters is used to reasonably set the hyperparameter values of the model. Finally, a hybrid fuel consumption prediction model is established by integrating extremely randomized tree (ET), random forest (RF), Xgboost (XGB) and multiple linear regression (MLR) methods. The experimental results show that data cleaning, the size of the dataset, marine environmental factors, and hyperparameter optimization can all affect the accuracy of the model, and the proposed hybrid model provides better predictive performance (higher accuracy) and greater robustness (smaller standard deviation) as compared with a single model. The proposed hybrid model should play a significant role in ship fuel consumption real-time monitoring, fault diagnosis, energy saving and emission reduction, etc.

中文翻译：

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基于传感器数据的新型远洋集装箱船混合动力油耗预测模型

准确，可靠，实时地预测船舶油耗是发展燃油优化的基础和前提；但是，船舶油耗数据主要来自中午报告，目前许多建模方法都基于单个模型。因此它们具有较低的准确性和鲁棒性。在这项研究中，我们基于从远洋集装箱船收集的传感器数据，提出了一种新型的混合燃料消耗预测模型。首先，提出了一种数据处理方法以清理收集到的数据。其次，采用贝叶斯超参数优化方法合理设置模型的超参数值。最后，通过整合极度随机树（ET），随机森林（RF），Xgboost（XGB）和多元线性回归（MLR）方法，建立了混合油耗预测模型。实验结果表明，数据清理，数据集的大小，海洋环境因素以及超参数优化均会影响模型的准确性，并且所提出的混合模型具有更好的预测性能（更高的准确性）和更大的鲁棒性（较小的标准偏差）。 ）与单个模型相比。提出的混合模型应在船舶油耗实时监测，故障诊断，节能减排等方面发挥重要作用。与单个模型相比，提出的混合模型提供了更好的预测性能（更高的准确性）和更大的鲁棒性（更小的标准偏差）。提出的混合模型应在船舶油耗实时监测，故障诊断，节能减排等方面发挥重要作用。与单个模型相比，提出的混合模型提供了更好的预测性能（更高的准确性）和更大的鲁棒性（更小的标准偏差）。提出的混合模型应在船舶油耗实时监测，故障诊断，节能减排等方面发挥重要作用。