A Machine Learning Ship's Speed Prediction Model and Sailing Time Control Strategy
Paper in proceeding, 2022

This paper proposes a machine learning based ship speed over ground prediction model, driven by the eXtreme Gradient Boosting (XGBoost) algorithm. The dataset is acquired from a world-sailing chemical tanker with five years of full-scale measurements. The model is trained using encountered metocean environments and ship operation profiles in two scenarios, i.e., through RPM or propulsion power. This model is further combined with the particle swarm optimization (PSO) algorithm to integrate a sailing time control method. It optimizes constant RPM or power operation strategy to meet the requirements of fixed ETA.

particle swarm optimization

speed over ground

ETA

XGBoost

Machine learning

full-scale measurements

Author

Xiao Lang

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Other publications Research

Da Wu

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Other publications Research

Wengang Mao

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Other publications Research

Proceedings of the International Offshore and Polar Engineering Conference

10986189 (ISSN) 15551792 (eISSN)

3598-3605
978-1-880653-81-4 (ISBN)

The 32nd International Ocean and Polar Engineering Conference
Shanghai, China,

E-Nav - Efficient Electronic Navigation at Sea

VINNOVA (2019-01059), 2019-03-01 -- 2021-08-31.

VINNOVA (2019-01059), 2019-04-11 -- 2021-10-11.

European Commission (EC), 2019-04-11 -- 2021-10-11.

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Explore innovative solutions for arctic shipping

The Swedish Foundation for International Cooperation in Research and Higher Education (STINT) (Dnr:CH2016-6673), 2017-05-01 -- 2020-06-30.

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EcoSail - Eco-friendly and customer-driven Sail plan optimisation service

European Commission (EC) (EC/H2020/820593), 2018-11-01 -- 2021-04-30.

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How do you realize the most energy-efficient ship trip in practice?

Swedish Transport Administration, 2020-10-01 -- 2022-09-30.

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Driving Forces

Sustainable development

Areas of Advance

Transport

Subject Categories

Marine Engineering

Probability Theory and Statistics

Signal Processing

Other Electrical Engineering, Electronic Engineering, Information Engineering

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Latest update

10/25/2023

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