Abstract
In the shipping industry, fuel consumption and emissions are critical factors due to their significant impact on economic efficiency and environmental sustainability. Accurate prediction of ship fuel consumption is essential for further optimization of maritime operations. However, heterogeneous methodologies and limited high-quality datasets hinder direct comparison of modeling approaches. This paper makes three key contributions: (1) we introduce and release a new dataset (this https URL) comprising operational and environmental data from three ships; (2) we define a standardized benchmark covering tabular regression and time-series regression (3) we investigate the application of in-context learning for ship consumption modeling using the TabPFN foundation model - a first in this domain to our knowledge. Our results demonstrate strong performance across all evaluated models, supporting the feasibility of onboard, data-driven fuel prediction. Models incorporating environmental conditions consistently outperform simple polynomial baselines relying solely on vessel speed. TabPFN slightly outperforms other techniques, highlighting the potential of foundation models with in-context learning capabilities for tabular prediction. Furthermore, including temporal context improves accuracy.
Abstract (translated)
在航运业中，燃油消耗和排放是至关重要的因素，因为它们对经济效率和环境可持续性有重大影响。准确预测船舶燃料消耗对于进一步优化海上运营至关重要。然而，异构方法论及高质量数据集的匮乏阻碍了建模方法的直接比较。本文做出了三项关键贡献：(1) 我们引入并发布了新的数据集（此链接），该数据集包含了来自三艘船的操作和环境数据；(2) 我们定义了一个标准化基准，涵盖表格回归和时间序列回归；(3) 我们研究了使用 TabPFN 基础模型进行船舶消耗建模的上下文学习应用——据我们所知，在此领域尚属首次。我们的结果表明所有评估模型在性能上表现出色，支持船载、数据驱动燃料预测的可行性。将环境条件纳入考虑范围的模型始终优于仅依赖船只速度的简单多项式基线模型。TabPFN 略微超越其他技术方法，突显了具有上下文学习能力的基础模型在表格预测中的潜力。此外，加入时间背景信息可以提高准确性。
URL
https://arxiv.org/abs/2510.08217
