Comparison of supervised machine learning methods to predict ship propulsion power at sea
Artikel i vetenskaplig tidskrift, 2022

As the shipping moves towards digitization, a large amount of ship energy performance-related information collected during a ship's sailing provides opportunities to derive data-driven performance models using different machine learning algorithms. This paper compares several typical supervised machine learning algorithms, i.e., eXtreme Gradient Boosting (XGBoost), artificial neural network, support vector machine, and statistical regression methods, for the ship speed–power modeling. First, a general data pre-processing framework is presented. The different machine learning based models are trained by both ship operational parameters and encountered metocean conditions. Based on the full-scale measurement data collected at two types of worldwide sailing ships, the pros and cons of different machine learning algorithms for the ship's speed–power performance modeling are compared. Finally, the best performed XGboost model is chosen to analyze the sensitivity due to the amount of available ship data, assumed time period for each stationary waypoint (data sample) used for the model training, and their impact on online performance prediction.


Supervised machine learning

Metocean environments

Full-scale measurements

Ship propulsion power


Xiao Lang

Chalmers, Mekanik och maritima vetenskaper, Marin teknik

Da Wu

Wuhan University of Technology

Wengang Mao

Chalmers, Mekanik och maritima vetenskaper, Marin teknik

Ocean Engineering

0029-8018 (ISSN)

Vol. 245 110387


Annan data- och informationsvetenskap

Bioinformatik (beräkningsbiologi)

Sannolikhetsteori och statistik



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