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

Citation

Li, XH and Du, Y and Chen, Yanyu and Nguyen, S and Zhang, Wei and Schonborn, A and Sun, Z, Data fusion and machine learning for ship fuel efficiency modeling: Part I - voyage report data and meteorological data, Communications in Transportation Research, 2 pp. 1-29. ISSN 2772-4247 (2022) [Refereed Article]


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DOI: doi:10.1016/j.commtr.2022.100074

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 R2 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.

Item Details

Item Type:Refereed Article
Keywords:ship fuel efficiency, fuel consumption rate, voyage report, data fusion, machine learning
Research Division:Commerce, Management, Tourism and Services
Research Group:Transportation, logistics and supply chains
Research Field:Maritime transportation and freight services
Objective Division:Transport
Objective Group:Water transport
Objective Field:International sea freight transport (excl. live animals, food products and liquefied gas)
UTAS Author:Li, XH (Mr Xiaohe Li)
UTAS Author:Du, Y (Dr Bill Du)
UTAS Author:Chen, Yanyu (Miss Yanyu Chen)
UTAS Author:Nguyen, S (Mr Son Nguyen)
UTAS Author:Zhang, Wei (Ms Wei Zhang)
ID Code:151920
Year Published:2022
Deposited By:Maritime and Logistics Management
Deposited On:2022-08-08
Last Modified:2022-08-09
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