Abstract
With the growth of global maritime transportation, energy optimization has become crucial for reducing costs and ensuring operational efficiency. Shaft power is the mechanical power transmitted from the engine to the shaft and directly impacts fuel consumption, making its accurate prediction a paramount step in optimizing vessel performance. Power consumption is highly correlated with ship parameters such as speed and shaft rotation per minute, as well as weather and sea conditions. Frequent access to this operational data can improve prediction accuracy. However, obtaining high-quality sensor data is often infeasible and costly, making alternative sources such as noon reports a viable option. In this paper, we propose a transfer learning-based approach for predicting vessels shaft power, where a model is initially trained on high-frequency data from a vessel and then fine-tuned with low-frequency daily noon reports from other vessels. We tested our approach on sister vessels (identical dimensions and configurations), a similar vessel (slightly larger with a different engine), and a different vessel (distinct dimensions and configurations). The experiments showed that the mean absolute percentage error decreased by 10.6 percent for sister vessels, 3.6 percent for a similar vessel, and 5.3 percent for a different vessel, compared to the model trained solely on noon report data.
Abstract (translated)
随着全球海上运输的增长，能源优化已成为降低运营成本和提高效率的关键。轴功率是从发动机传递到船轴的机械功率，直接关系到燃油消耗，因此准确预测轴功率是船舶性能优化的重要一步。能耗与船速、每分钟转数等船只参数以及天气和海况密切相关。频繁访问此类操作数据可以改善预测精度。然而，获取高质量的传感器数据往往难以实现且成本高昂，使得使用午报等替代来源成为一种可行的选择。 在本文中，我们提出了一种基于迁移学习的方法来预测船舶轴功率：首先在一个船只上用高频数据训练模型，然后利用其他船只每日午报中的低频数据对其进行微调。我们在姐妹船（相同尺寸和配置）、类似船（稍大且发动机不同）以及完全不同类型的船只上测试了这种方法。实验结果显示，在姐妹船上，平均绝对百分比误差减少了10.6%，在类似船舶上减少了3.6%，而在不同类型船舶上则减少了5.3%。相较于仅基于午报数据训练的模型而言，这一改进尤为显著。
URL
https://arxiv.org/abs/2510.03003
