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