用机器学习预测哥本哈根机场航油需求，提前30天更准更省
Data-driven jet fuel demand forecasting: A case study of Copenhagen Airport
- 用时间序列、LSTM和混合模型对比预测航油需求
- 混合模型在30天预测上误差最低，优于传统方法
- 适合燃料供应链优化的从业者参考
准确预测航油需求对优化航空供应链至关重要。燃料分销商需精准预估以避免库存不足或过剩。然而，现有研究较少使用机器学习模型分析此问题，行业多依赖确定性或经验模型。本研究基于丹麦主要航油分销商提供的大量数据，评估了数据驱动方法的性能，比较了传统时间序列模型、Prophet、LSTM序列到序列神经网络及混合模型的预测能力。关键挑战在于需提前30天预测需求以优化采购策略。为确保模型可靠性并为从业者提供洞见，我们分析了三个不同数据集。本研究旨在通过完整案例展示数据驱动模型的优势，并强调引入额外变量对预测精度的提升作用。
原文摘要 · Abstract (English)
Accurate forecasting of jet fuel demand is crucial for optimizing supply chain operations in the aviation market. Fuel distributors specifically require precise estimates to avoid inventory shortages or excesses. However, there is a lack of studies that analyze the jet fuel demand forecasting problem using machine learning models. Instead, many industry practitioners rely on deterministic or expertise-based models. In this research, we evaluate the performance of data-driven approaches using a substantial amount of data obtained from a major aviation fuel distributor in the Danish market. Our analysis compares the predictive capabilities of traditional time series models, Prophet, LSTM sequence-to-sequence neural networks, and hybrid models. A key challenge in developing these models is the required forecasting horizon, as fuel demand needs to be predicted for the next 30 days to optimize sourcing strategies. To ensure the reliability of the data-driven approaches and provide valuable insights to practitioners, we analyze three different datasets. The primary objective of this study is to present a comprehensive case study on jet fuel demand forecasting, demonstrating the advantages of employing data-driven models and highlighting the impact of incorporating additional variables in the predictive models.
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