条件付き拡散モデルを用いた船舶燃料消費の確率的予測
Probabilistic Fuel Consumption Prediction for Marine Vessels Using Conditional Diffusion Model (原題)
Tong-Tong Wang, Qin Liang, Bai-Heng Wu, Hou-Xiang Zhang, Masayoshi Tomizuka
🤖 gxceed AI 要約
日本語
本論文は、船舶の運航・環境条件を条件付けした拡散確率モデルにより、燃料消費量の完全な条件付き分布を予測する手法を提案する。実航海データセットでの実験で、従来の回帰・点推定モデルを上回る予測精度・較正・不確実性表現を達成した。航路最適化や脱炭素計画の意思決定支援ツールとしての有用性を示す。
English
This paper proposes a conditional diffusion probabilistic model that predicts the full conditional distribution of marine vessel fuel consumption given operational and environmental contexts. Using real-world voyage data across ship types, it outperforms regression and point-estimate baselines in accuracy, calibration, and uncertainty representation, supporting route optimization and decarbonization planning.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
海運は日本にとって国際物流・輸出産業の基盤であり、IMO規制や燃料消費報告義務への対応が進む。本手法は船舶の燃費予測精度を高め、Scope 1排出量の算定・削減計画や運航効率化に資する。国内海運・造船業のGX対応に実務的示唆を与える。
In the global GX context
Maritime shipping sits at the center of global decarbonization debates (IMO GHG strategy, EU ETS extension to shipping, FuelEU Maritime). This work advances probabilistic fuel-consumption modeling, which underpins accurate Scope 1 accounting, voyage-level emissions reporting, and transition planning for shipping firms and their financiers.
👥 読者別の含意
🔬研究者:拡散モデルを物理制約下の時系列予測に応用する手法として、不確実性定量化研究に参考になる。
🏢実務担当者:船舶運航・海運企業が燃費予測と排出量算定の精度向上に活用でき、運航最適化や報告義務対応に役立つ。
🏛政策担当者:IMO・EU ETS等の海運排出規制における算定精度向上や、脱炭素計画の信頼性確保に示唆を与える。
📄 Abstract（原文）
Accurate prediction of marine vessel fuel consumption plays a crucial role in improving operational efficiency, enabling route optimization, and supporting emission reduction strategies in modern maritime transportation. Traditional deterministic models and deep learning approaches often provide point estimates that fail to capture the inherent uncertainty in maritime environments and operational variations. This paper introduces a conditional diffusion probabilistic model for data-driven fuel consumption prediction, which captures the full conditional distribution of fuel usage given vessel operational and environmental contexts. The proposed model leverages the diffusion process to learn how to generate diverse yet physically realistic fuel consumption trajectories, conditioned on features such as navigation patterns and engine parameters. By modeling multimodal and stochastic dependencies within the data, the diffusion framework provides a more robust representation of uncertainty compared to traditional regression-based or point-estimate models. Extensive experiments are conducted using a real-world dataset collected from multiple vessel voyages covering a range of operating conditions and ship types. The results demonstrate that the proposed diffusion model achieves superior predictive accuracy, calibration, and uncertainty representation, highlighting its potential as a reliable tool for fuel optimization, decision support, and decarbonization planning in the maritime domain.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://asmedigitalcollection.asme.org/OMAE/proceedings-pdf/OMAE2026/89596/V010T14A042/7645921/v010t14a042-omae2026-181796.pdffirst seen 2026-10-01 05:33:13 · last seen 2026-10-02 05:33:19
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