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:	Refereed Article
Keywords:	fuel consumption prediction, ship fuel efficiency, ship speed optimization, random forest regressor, 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)
ID Code:	138824
Year Published:	2020
Web of Science® Times Cited:	19
Deposited By:	Maritime and Logistics Management
Deposited On:	2020-04-30
Last Modified:	2020-07-30
Downloads:	4 View Download Statistics