融合气象海况数据，用机器学习精准预测散货船油耗
Data Fusion and Machine Learning for Ship Fuel Consumption Modelling -- A Case of Bulk Carrier Vessel
- 结合航次记录与公开气象海况数据，构建多源融合模型
- 融合外部数据后模型精度显著提升，验证了数据增益潜力
- 适合航运碳排放管控、智能航行系统研发人员参考
国际海事组织（IMO） mandates 推动船舶减排，关键绩效指标如能源效率营运指数（EEOI）聚焦燃油效率。针对航速、载重、纵倾、气候与海况等影响因素，本研究基于一艘散货船2021年11月16日至2022年11月21日的296条航次报告，整合来自哥白尼海洋环境监测服务（CMEMS）的19项参数和欧洲中期天气预报中心（ECMWF）的61项参数，共28个变量。目标是评估融合外部公共数据是否提升油耗建模精度，并识别关键影响因子。结果表明，结合航次数据与气候海况数据可显著增强机器学习模型对船舶燃油消耗的预测能力。但需在同类船舶上进一步验证其泛化性能。
原文摘要 · Abstract (English)
There is an increasing push for operational measures to reduce ships' bunker fuel consumption and carbon emissions, driven by the International Maritime Organization (IMO) mandates. Key performance indicators such as the Energy Efficiency Operational Indicator (EEOI) focus on fuel efficiency. Strategies like trim optimization, virtual arrival, and green routing have emerged. The theoretical basis for these approaches lies in accurate prediction of fuel consumption as a function of sailing speed, displacement, trim, climate, and sea state. This study utilized 296 voyage reports from a bulk carrier vessel over one year (November 16, 2021 to November 21, 2022) and 28 parameters, integrating hydrometeorological big data from the Copernicus Marine Environment Monitoring Service (CMEMS) with 19 parameters and the European Centre for Medium-Range Weather Forecasts (ECMWF) with 61 parameters. The objective was to evaluate whether fusing external public data sources enhances modeling accuracy and to highlight the most influential parameters affecting fuel consumption. The results reveal a strong potential for machine learning techniques to predict ship fuel consumption accurately by combining voyage reports with climate and sea data. However, validation on similar classes of vessels remains necessary to confirm generalizability.
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