Abstract
The fuel efficiency of marine vessels can be increased through operational considerations such as improved cargo arrangements and weather routing. The first step toward this goal is to analyze how the ship’s powering performance changes under different operational settings and weather conditions. Existing analytical and empirical methods are not sufficiently satisfactory in accurately predicting the powering performance of full-scale ships. In this study, we suggest the use of machine learning techniques to estimate the ship’s powering performance by constructing a regression model that can predict the ship speed and engine power under various weather conditions.
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