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Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship

Yan, R, Wang, S and Du, Y ORCID: 0000-0001-7540-493X 2020 , 'Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship' , Transportation Research Part E: Logistics and Transportation Review, vol. 138 , pp. 1-22 , doi: 10.1016/j.tre.2020.101930.

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Abstract

Shipping industry is the backbone of global trade. However, the large quantities of greenhouse gas emissions from shipping, such as carbon dioxide (CO2), cannot be ignored. In order to comply with the international environmental regulations as well as to increase commercial profits, shipping companies have stronger motivations to improve ship energy efficiency. In this study, a two-stage ship fuel consumption prediction and reduction model is proposed for a dry bulk ship. At the first stage, a fuel consumption prediction model based on random forest regressor is proposed and validated. The prediction model takes into account ship sailing speed, total cargo weight, and sea and weather conditions and then predicts hourly fuel consumption of the main engine. The mean absolute percentage error of the random forest regressor is 7.91%. At the second stage, a speed optimization model is developed based on the prediction model proposed at the first stage while guaranteeing the estimated arrival time to the destination port. Numerical experiment on two consecutive-8-day voyages shows that the proposed model can reduce ship fuel consumption by 2–7%. The reduction in ship fuel consumption will also lead to lower CO2 emissions.

Item Type: Article
Authors/Creators:Yan, R and Wang, S and Du, Y
Keywords: fuel consumption prediction, ship fuel efficiency, ship speed optimization, random forest regressor, machine learning
Journal or Publication Title: Transportation Research Part E: Logistics and Transportation Review
Publisher: Pergamon-Elsevier Science Ltd
ISSN: 1366-5545
DOI / ID Number: 10.1016/j.tre.2020.101930
Copyright Information:

© 2020 Elsevier Ltd. All rights reserved.

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