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Accurate modeling of ship performance is crucial for the shipping industry tooptimize fuel consumption and subsequently reduce emissions. However,predicting the speed-power relation in real-world conditions remains achallenge. In this study, we used in-service monitoring data from multiplevessels with different hull shapes to compare the accuracy of data-drivenmachine learning (ML) algorithms to traditional methods for assessing shipperformance. Our analysis consists of two main parts: (1) a comparison of seatrial curves with calm-water curves fitted on operational data, and (2) abenchmark of multiple added wave resistance theories with an ML-based approach.Our results showed that a simple neural network outperformed establishedsemi-empirical formulas following first principles. The neural network onlyrequired operational data as input, while the traditional methods requiredextensive ship particulars that are often unavailable. These findings suggestthat data-driven algorithms may be more effective for predicting shipperformance in practical applications.

Towards Improved Prediction of Ship Performance: A Comparative Analysis on In-service Ship Monitoring Data for Modeling the Speed-Power Relation