Ship Power Prediction Using Machine Learning
Name
kriezis-akriezis-smme-meche-2022-thesis.pdf
Description
Thesis PDF
Size
2.39 MB
Format
Adobe PDF
Checksum (MD5)
4bb6f3abd43e961585ebc5f55b11af34
Author(s)
Kriezis, Anthony
Advisor(s)
Sapsis, Themistoklis
Date Issued
May 2022
Publisher
Massachusetts Institute of Technology
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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In Copyright - Educational Use Permitted
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