Scientific Knowledge-Guided Machine Learning for Vessel Power Prediction: A Comparative Study

TL;DR

This paper presents a hybrid physics-informed machine learning framework for vessel power prediction, combining baseline physics models with residual learning to improve accuracy and generalization in operational conditions.

Contribution

It introduces a novel hybrid modeling approach that integrates physics-based power curves with machine learning residuals, enhancing vessel power prediction accuracy.

Findings

Hybrid models outperform pure data-driven models in sparse data regions.

Incorporating physics-based baseline simplifies learning and improves generalization.

Validation shows consistent performance improvements across different ML methods.

Abstract

Accurate prediction of main engine power is essential for vessel performance optimization, fuel efficiency, and compliance with emission regulations. Conventional machine learning approaches, such as Support Vector Machines, variants of Artificial Neural Networks (ANNs), and tree-based methods like Random Forests, Extra Tree Regressors, and XGBoost, can capture nonlinearities but often struggle to respect the fundamental propeller law relationship between power and speed, resulting in poor extrapolation outside the training envelope. This study introduces a hybrid modeling framework that integrates physics-based knowledge from sea trials with data-driven residual learning. The baseline component, derived from calm-water power curves of the form $P = cV^n$, captures the dominant power-speed dependence, while another, nonlinear, regressor is then trained to predict the residual power, representing deviations caused by environmental and operational conditions. By constraining the machine learning task to residual corrections, the hybrid model simplifies learning, improves generalization, and ensures consistency with the underlying physics. In this study, an XGBoost, a simple Neural Network, and a Physics-Informed Neural Network (PINN) coupled with the baseline component were compared to identical models without the baseline component. Validation on in-service data demonstrates that the hybrid model consistently outperformed a pure data-driven baseline in sparse data regions while maintaining similar performance in populated ones. The proposed framework provides a practical and computationally efficient tool for vessel performance monitoring, with applications in weather routing, trim optimization, and energy efficiency planning.