构建船舶燃油消耗预测新基准，验证基础模型有效性和环境因素重要性
FuelCast: Benchmarking Tabular and Temporal Models for Ship Fuel Consumption
- 构建包含三艘船运行与环境数据的新基准数据集
- 融合环境信息的模型显著优于仅依赖航速的多项式基线
- 首次应用上下文学习的TabPFN在表格预测中表现最优
航运业中，燃油消耗与排放是影响经济效率和环境可持续性的关键因素。准确预测船舶燃油消耗对优化海上运营至关重要。然而，方法异质性和高质量数据集稀缺导致模型比较困难。本文贡献包括：(1) 发布新数据集（https://huggingface.co/datasets/krohnedigital/FuelCast），涵盖三艘船的运行与环境数据；(2) 建立覆盖表格回归与时间序列回归的标准化基准；(3) 首次探索使用TabPFN基础模型进行上下文学习在船舶油耗建模中的应用。结果表明，所有模型均表现良好，支持在船上实现数据驱动的燃油预测。包含环境条件的模型始终优于仅依赖航速的多项式基线。TabPFN略胜于其他方法，凸显具备上下文学习能力的基础模型在表格预测中的潜力。引入时间上下文可进一步提升精度。
原文摘要 · Abstract (English)
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 (https://huggingface.co/datasets/krohnedigital/FuelCast) 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.
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