Shipping companies are forced by the current EU regulation to set up a system for monitoring, reporting, and verification of harmful emissions from their fleet. In this regulatory background, data collected from onboard sensors can be utilized to assess the ship's operating conditions and quantify its CO₂ emission levels. The standard approach for analyzing such data sets is based on summarizing the measurements obtained during a given voyage by the average value. However, this compression step may lead to significant information loss since most variables present a dynamic profile that is not well approximated by the average value only. Therefore, in this work, we test two feature‐oriented methods that are able to extract additional features, namely, profile‐driven features (PdF) and statistical pattern analysis (SPA). A real data set from a Ro‐Pax ship is then considered to test the selected methods. The data set is segregated according to the voyage distance into short, medium, and long routes. Both PdF and SPA are compared with the standard approach, and the results demonstrate the benefits of employing more systematic and informative feature‐oriented methods. For the short route, no method is able to predict CO₂ emissions in a satisfactory way, whereas for the medium and long routes, regression models built using features obtained from both PdF and SPA improve their prediction performance. In particular, for the long route, the standard approach failed to provide reasonably good predictions.

中文翻译：

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使用面向特征的方法预测船舶的二氧化碳排放量

航运公司受现行欧盟法规的约束，必须建立一个监控，报告和验证船队有害排放物的系统。在这种监管背景下，从船上传感器收集的数据可用于评估船舶的运行状况并量化其CO ₂排放水平。分析此类数据集的标准方法是基于通过平均值总结给定航程中获得的测量值。但是，此压缩步骤可能会导致大量的信息丢失，因为大多数变量都呈现出动态轮廓，而该轮廓不能仅通过平均值很好地近似。因此，在这项工作中，我们测试了两种能够提取附加特征的面向特征的方法，即，配置文件驱动特征（PdF）和统计模式分析（SPA）。然后考虑来自Ro-Pax船的真实数据集来测试所选方法。根据航行距离将数据集分为短，中和长路线。PdF和SPA均与标准方法进行了比较，结果表明，采用更系统，信息量更广的面向特征的方法的好处。对于短途路线，没有方法能够预测CO₂排放令人满意，而对于中长距离路线，使用从PdF和SPA获得的特征构建的回归模型可改善其预测性能。特别是，对于长途旅行，标准方法无法提供合理的良好预测。