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Research Article | Open Access

Data fusion and machine learning for ship fuel efficiency modeling: Part I – Voyage report data and meteorological data

Xiaohe Lia,b, Yuquan Dua( ), Yanyu Chena,c, Son Nguyena, Wei Zhanga, Alessandro Schönbornd, Zhuo Sune
Centre for Maritime and Logistics Management, Australian Maritime College, University of Tasmania, Launceston, TAS 7250, Australia
College of Power and Energy Engineering, Harbin Engineering University, Harbin, 150001, China
Institute for Marine and Antarctic Studies, College of Sciences and Engineering, University of Tasmania, Taroona, TAS 7053, Australia
Maritime Energy Management, World Maritime University, Fiskehamnsgatan 1, 201 24 Malmö, Sweden
College of Transportation Engineering, Dalian Maritime University, Dalian, 116026, China
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Abstract

The International Maritime Organization has been promoting energy-efficient operational measures to reduce ships' bunker fuel consumption and the accompanying emissions, including speed optimization, trim optimization, weather routing, and the virtual arrival policy. The theoretical foundation of these measures is a model that can accurately forecast a ship's bunker fuel consumption rate according to its sailing speed, displacement/draft, trim, weather conditions, and sea conditions. Voyage report is an important data source for ship fuel efficiency modeling but its information quality on weather and sea conditions is limited by a snapshotting practice with eye inspection. To overcome this issue, this study develops a solution to fuse voyage report data and publicly accessible meteorological data and constructs nine datasets based on this data fusion solution. Eleven widely-adopted machine learning models were tested over these datasets for eight 8100-TEU to 14,000-TEU containerships from a global shipping company. The best datasets found reveal the benefits of fusing voyage report data and meteorological data, as well as the practically acceptable quality of voyage report data. Extremely randomized trees (ET), AdaBoost (AB), Gradient Tree Boosting (GB) and XGBoost (XG) present the best fit and generalization performances. Their R values over the best datasets are all above 0.96 and even reach 0.99 to 1.00 for the training set, and 0.74 to 0.90 for the test set. Their fit errors on daily bunker fuel consumption are usually between 0.5 and 4.0 ton/day. These models have good interpretability in explaining the relative importance of different determinants to a ship's fuel consumption rate.

Keywords

Machine learningData fusionShip fuel efficiencyFuel consumption rateVoyage report

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Communications in Transportation Research
Volume 2 Issue 1,
2022
Article number: 100074
DOI: 10.1016/j.commtr.2022.100074
Cite this article:
Li X, Du Y, Chen Y, et al. Data fusion and machine learning for ship fuel efficiency modeling: Part I – Voyage report data and meteorological data. Communications in Transportation Research, 2022, 2(1): 100074. https://doi.org/10.1016/j.commtr.2022.100074

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Received: 30 April 2022
Revised: 18 June 2022
Accepted: 19 June 2022
Published: 06 July 2022
© 2022 The Author(s). Published by Elsevier Ltd on behalf of Tsinghua University Press.

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
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