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 empirical 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 R2 above 95%. This performance can be further improved by the addition of wave information, but it deteriorates when the data collection becomes less frequent.
Predicting Ship Power Using Machine Learning Methods
Kriezis, Anthony Constantine, Sapsis, Themistoklis , and Chryssostomos Chryssostomidis. "Predicting Ship Power Using Machine Learning Methods." Paper presented at the SNAME Maritime Convention, Houston, Texas, USA, September 2022. doi: https://doi.org/10.5957/SMC-2022-065
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