A Machine Learning Ship’s Speed Over Ground Prediction Model and Sailing Time Control Strategy
Artikel i vetenskaplig tidskrift, 2022

This paper proposes a machine learning–based ship speed over a ground prediction model, driven by the eXtreme Gradient Boosting (XGBoost) algorithm. The data set 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: through propeller revolutions per minute (RPM) or propulsion power. This model is further combined with the particle swarm optimization algorithm to integrate a sailing time control method. It optimizes constant RPM or power operation strategy to meet the requirements of a fixed estimated time of arrival.

ETA

speed over ground

particle swarm optimization

full-scale measurements

Machine learning

XGBoost

Författare

Xiao Lang

Chalmers, Mekanik och maritima vetenskaper, Marin teknik

Da Wu

Wuhan University of Technology

Wengang Mao

Chalmers, Mekanik och maritima vetenskaper, Marin teknik

International Journal of Offshore and Polar Engineering

1053-5381 (ISSN)

Vol. 32 4 386-393

Styrkeområden

Transport

Ämneskategorier

Reglerteknik

Signalbehandling

Annan elektroteknik och elektronik

DOI

10.17736/ijope.2022.jc876

Mer information

Senast uppdaterat

2023-10-25