In the current shipping industry, quantitative measures of ship fuel consumption (SFC) have become one of the most important research topics in environmental protection and energy management related to shipping operations. In particular, the rapid development of sensor technologies enables multisource data collection to improve the modeling of the SFC problem. To address the features of such heterogeneous data, this paper proposes an integrated model for the estimation of SFC that includes three modules: a multisource data collection module, a heterogeneous data feature fusion module and a fuel consumption estimation module. First, in the data collection module, data related to SFC are collected by multiple sensors installed aboard the ship. Second, the feature fusion module employs a series of moving overlapped frames to merge different frequency data into small frames so that fusion features can be extracted from the heterogeneous data of multiple sources. Finally, in the fuel estimation module, the fusion features provide a novel way to consider the modeling and estimation of SFC as a classical time-series analysis using various machine learning techniques. Experimentally, linear regression (LR), support vector regression (SVR), and artificial neural network (ANN) were employed as the machine learning methods to train SFC models. Compared with the traditional feature extraction method, the accuracy of LR, SVR, and ANN were improved by 8.5, 0.35 and 51.5%, respectively, using the proposed method. The main contribution of this work is to consider the multisource and heterogeneous problem of sensor-based SFC data and propose an integrated model to extract the information of SFC data. Moreover, the experimental results showed that the estimation accuracy can be greatly improved.

中文翻译：

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基于多源异构数据的船舶燃油消耗建模-以客船为例

在当前的航运业中，船舶燃料消耗量（SFC）的定量测​​量已成为与航运运营相关的环境保护和能源管理中最重要的研究主题之一。特别是，传感器技术的飞速发展使多源数据收集能够改善SFC问题的建模。为了解决此类异构数据的特征，本文提出了一种用于SFC估算的集成模型，该模型包括三个模块：多源数据收集模块，异构数据特征融合模块和燃油消耗估算模块。首先，在数据收集模块中，与SFC相关的数据由船上安装的多个传感器收集。第二，特征融合模块采用一系列移动的重叠帧将不同频率的数据合并为小帧，以便可以从多个源的异构数据中提取融合特征。最后，在燃料估算模块中，融合功能提供了一种新颖的方法，可以将SFC的建模和估算视为使用各种机器学习技术的经典时间序列分析。实验上，线性回归（LR），支持向量回归（SVR）和人工神经网络（ANN）被用作训练SFC模型的机器学习方法。与传统的特征提取方法相比，使用该方法可以将LR，SVR和ANN的精度分别提高8.5、0.35和51.5％。这项工作的主要贡献是考虑了基于传感器的SFC数据的多源和异构问题，并提出了一个集成模型来提取SFC数据的信息。此外，实验结果表明估计精度可以大大提高。