Chouikri, Khalil (Lis), Graton, Guillaume (Ecole Centrale de Marseille), Noura, Hassan (LIS Laboratory (UMR CNRS 7020), Aix-Marseille University, 13397 ), Rapuc, Stéphane (CMA CGM)

Hybrid Semi-Parametric Model for Ship Power Prediction

Scheduled for presentation during the Regular Session "Modelling and simulation" (WeCB), Wednesday, June 11, 2025, 17:50−18:10,

33rd Mediterranean Conference on Control and Automation, June 10-13, 2025, Tangier, Morocco

This information is tentative and subject to change. Compiled on May 9, 2025

Abstract

Power prediction is crucial for dynamic ship routing, a key strategy for reducing fuel consumption and minimizing environmental impact. Ships, equipped with some of the largest and most powerful engines, contribute significantly to global pollution. Accurate power prediction enables naval architects to evaluate different routes and select the most fuel-efficient option. This study presents a comprehensive comparison of three power prediction methods, assessing their performance for maritime applications. The first method is a physics-based approach using computational fluid dynamics data to estimate power requirements. The second method is a data-driven model, relying on historical data inputs such as speed, draft, and wind speed. The third method is a hybrid approach that integrates computational fluid dynamics data interpolations with machine learning techniques. Results highlight the strengths and limitations of each method, offering valuable insights for optimizing ship routing and power management strategies.