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Data fusion and machine learning for ship fuel efficiency modeling: part III - sensor data and meteorological data


Du, Y and Chen, Yanyu and Li, XH and Schonborn, A and Sun, Z, Data fusion and machine learning for ship fuel efficiency modeling: part III - sensor data and meteorological data, Communications in Transportation Research, 2 Article 100072. ISSN 2772-4247 (2022) [Refereed Article]

Copyright Statement

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 Attribution 4.0 International (CC BY 4.0) (

DOI: doi:10.1016/j.commtr.2022.100072


Sensors installed on a ship return high quality data that can be used for ship bunker fuel efficiency analysis. However, important information about weather and sea conditions the ship sails through, such as waves, sea currents, and sea water temperature, is often absent from sensor data. This study addresses this issue by fusing sensor data and publicly accessible meteorological data, constructing nine datasets accordingly, and experimenting with widely adopted machine learning (ML) models to quantify the relationship between a ship's fuel consumption rate (ton/day, or ton/h) and its voyage-based factors (sailing speed, draft, trim, weather conditions, and sea conditions). The best dataset found reveals the benefits of fusing sensor data and meteorological data for ship fuel consumption rate quantification. The best ML models found are consistent with our previous studies, including Extremely randomized trees (ET), Gradient Tree Boosting (GB) and XGBoost (XG). Given the best dataset from data fusion, their R2 values over the training set are 0.999 or 1.000, and their R2 values over the test set are all above 0.966. Their fit errors with RMSE values are below 0.75 ton/day, and with MAT below 0.52 ton/day. These promising results are well beyond the requirements of most industry applications for ship fuel efficiency analysis. The applicability of the selected datasets and ML models is also verified in a rolling horizon approach, resulting in a conjecture that a rolling horizon strategy of "5-month training + 1-month test/applicatoin" could work well in practice and sensor data of less than five months could be insufficient to train ML models.

Item Details

Item Type:Refereed Article
Keywords:ship fuel efficiency, fuel consumption rate, sensor data, 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:Du, Y (Dr Bill Du)
UTAS Author:Chen, Yanyu (Miss Yanyu Chen)
UTAS Author:Li, XH (Mr Xiaohe Li)
ID Code:151922
Year Published:2022
Deposited By:Maritime and Logistics Management
Deposited On:2022-08-08
Last Modified:2022-11-28

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