Improving Long-Range Significant Wave Height Forecasts for Maritime Energy Efficiency: A Residual U-Net Approach Validated with Real-Ship Fuel Consumption Data
長距離有義波高予測の改善と海事エネルギー効率: 実船燃料消費データで検証されたResidual U-Netアプローチ (AI 翻訳)
Lee H, Jung J, Roh J
🤖 gxceed AI 要約
日本語
本研究は、Residual U-Netを用いて波浪予測モデルWW3の有義波高を補正し、実船の燃料消費データで検証。補正予測は7-8日先まで観測に近い精度を示し、気象海象を考慮した航路選定による燃料削減と海運の脱炭素化に貢献可能。
English
This study proposes a Residual U-Net deep learning model to correct significant wave height forecasts from WAVEWATCH III, validated with real-ship fuel consumption data. The corrected forecasts show improved accuracy up to 7-8 days ahead, enabling fuel-efficient weather routing and contributing to maritime decarbonization.
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
日本は海運大国であり、国際海運のGHG削減目標達成に向けて、本手法は運航効率向上と燃料コスト削減の両面で実用的価値が高い。日本船主・海運各社の運航最適化にAIを活用する好事例となる。
In the global GX context
As IMO pushes for shipping decarbonization, this AI-driven wave forecasting correction offers a practical tool for fuel-efficient voyage planning. The validation with real-world fuel consumption data strengthens the case for integrating AI into maritime operations globally.
👥 読者別の含意
🔬研究者:The Residual U-Net correction method can be adapted to other weather-dependent forecasting tasks, and the validation framework using real fuel data is a rigorous benchmark.
🏢実務担当者:Shipping companies can use this approach to improve weather routing and reduce fuel consumption, directly impacting operational costs and emissions.
🏛政策担当者:The study provides evidence that AI-enhanced wave forecasts can enable significant fuel savings, supporting IMO and national strategies for maritime decarbonization.
📄 Abstract（原文）
Accurate significant wave height prediction is essential for fuel-efficient ship operation and weather routing, as wave-induced resistance directly affects propulsion demand and fuel consumption. This study proposes a Residual U-Net-based deep learning correction model to improve long-range SWH forecasts from WAVEWATCH III (WW3). WW3 global forecast fields were corrected using the proposed model, with CMEMS reanalysis data used as the ground-truth reference. The corrected outputs, denoted as WW3_UNET, were evaluated against 10-minute-resolution main engine fuel oil consumption (ME1_FOC) records and onboard wave observations from a commercial vessel traversing the South Atlantic in 2025. WW3_UNET showed markedly improved agreement with ship observations compared with the raw WW3 forecast across all lead times from 0 to 288 h. When a 24-hour moving average was applied, WW3_UNET achieved a correlation of 0.720 with ME1_FOC at the 168–180 h lead time, closely approaching the 0.736 obtained from onboard wave measurements. These results indicate that AI-corrected forecasts can provide observation-consistent wave information up to 7–8 days in advance. The proposed approach can support fuel-aware weather routing and voyage planning, thereby contributing to improved maritime energy efficiency and decarbonization.
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.20944/preprints202606.0758.v1first seen 2026-06-19 04:26:37
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