Ship Power Prediction Using Machine Learning

One of the biggest challenges facing the shipping industry in the coming decades is the reduction of carbon emissions. A promising approach to this end is the use of the growing amount of data collected by vessels to optimize a voyage so as to minimize power consumption. The focus of this paper is on building and testing machine learning models that can accurately predict the shaft power of a vessel under different conditions. The models examined include pure theoretical models, pure neural network models, and combinations of the two. Using data on two car carrying vessels for 8 years it was found that neural networks incorporating some physical intuition can achieve a mean absolute percentage error of less than 5%, and an R-squared above 95%. This performance can be further improved by the addition of wave information, but it deteriorates when the data collection becomes less frequent.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Kriezis, Anthony

Advisor dc:contributor.advisor

• Sapsis, Themistoklis

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/144672

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/144672