船舶燃料消費分析のための信頼度重み付き説明可能学習：マルチモデル比較フレームワーク

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🔬研究者:信頼度重み付けとXAI・不確実性定量を統合した船舶エネルギー分析の比較ベンチマークとして参照できる。

🏢実務担当者:運航最適化や燃費ベンチマーク、荷主への排出量説明に活用できる船舶単位の解釈可能な推計手法を提供する。

🏛政策担当者:海運の省エネ規制や排出量報告制度の設計において、不確実性を伴う船舶レベル推計の扱いを検討する材料になる。

<title>Abstract</title> <p>Improving vessel energy efficiency is essential for reducing fuel consumption, greenhouse-gas emissions, and operating costs while supporting maritime decarbonisation objectives. Although advances in maritime analytics and machine learning have improved vessel monitoring and fuel-consumption prediction, existing studies focus mainly on predictive accuracy and offer limited insight into the engineering, operational, and reliability factors that are associated with vessel energy demand. In addition, confidence-aware learning, uncertainty quantification, explainable artificial intelligence (XAI), and comparative evaluation of machine-learning models remain underexplored within maritime energy-intelligence research. This paper presents a confidence-weighted explainable learning framework for vessel fuel-consumption analysis and conducts a systematic comparison of five model families, namely Random Forest (RF), Extreme Gradient Boosting (XGBoost), Gaussian Process Regression (GPR), Bayesian Ridge Regression (BRR), and Elastic Net, within a unified confidence-aware, explainable, and uncertainty-aware analytical architecture. Confidence integration is formulated as a reliability-weighted signal-fusion problem in which vessel-level confidence scores derived from heterogeneous data sources are incorporated directly into model learning. The Vessel Energy Efficiency Intelligence (VEEI) combines statistical driver analysis, duty-cycle intelligence, explainable machine learning, and uncertainty quantification to generate interpretable vessel-level energy intelligence. Experiments use a tidal River Thames inland-waterway dataset comprising 26 representative vessels and 131 engineering, operational, propulsion, fuel-consumption, and confidence-related attributes. Spearman rank correlation analysis identifies operating hours ($OH$) and installed engine power ($EP$) as the only statistically significant direct fuel-consumption drivers ($p<0.05$), while medium-cruise operation exhibits the strongest significant duty-cycle association with fuel demand. Among the evaluated models, Bayesian Ridge Regression achieves the highest predictive performance with an MAE of 41.23~L, an RMSE of 59.87~L, and an $R^{2}$ of 0.881, followed by XGBoost ($R^{2}=0.876$) and Random Forest ($R^{2}=0.873$). Confidence-weighted learning consistently improves predictive performance across all model families relative to their unweighted counterparts. Cross-model SHAP analysis identifies vessel draft and duty-cycle behaviour as the dominant explanatory factors, while uncertainty analysis consistently highlights vessels with elevated predictive uncertainty. The results show that operational utilisation, propulsion characteristics, vessel geometry, duty-cycle behaviour, and data reliability jointly influence vessel fuel consumption. By integrating confidence-aware learning, multi-model explainability, and uncertainty quantification in a unified framework, the proposed approach provides transparent, trustworthy vessel-level energy intelligence to support operational optimisation, fleet benchmarking, and evidence-based maritime decarbonisation planning.</p>

• Research Square https://doi.org/10.21203/rs.3.rs-11388094/v1first seen 2026-11-14 04:22:27