将时间序列和分类机器学习模型与操作数据相结合实现港口船舶燃油效率

IF 3.9 Q2 TRANSPORTATION Maritime Transport Research Pub Date : 2022-01-01 Epub Date: 2022-09-18 DOI:10.1016/j.martra.2022.100073
Januwar Hadi , Dimitrios Konovessis , Zhi Yung Tay
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引用次数: 5

摘要

本文介绍了通过时间序列和分类预测模型相结合来预测港口船只燃料消耗率的工作。这项研究使用了机器学习工具,该工具使用5个月的原始操作数据进行训练,即燃油率、船只位置和风力数据。Haar小波变换对燃料流量数据中的噪声读数进行滤波。将风数据转换为风效应(阻力),并通过将船舶位置的GPS坐标转换为随时间行驶的船舶距离来获取船舶速度。随后,k-means聚类将来自相同操作(即巡航和拖曳)的拖船操作数据分组,用于分类模型的训练。并行执行时间序列(LSTM网络)和分类模型以产生预测结果。通过对经验结果的比较,讨论了不同结构和超参数对预测性能的影响。最后,作为本文方法的一个直接应用,提出了通过假设调整船舶速度来优化燃料使用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Achieving fuel efficiency of harbour craft vessel via combined time-series and classification machine learning model with operational data
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