Comparative study of ship fuel consumption prediction models based on multi-source operational and environmental data under dynamic ocean conditions

Journal article, 2026

Accurate prediction of ship fuel consumption is essential for improving maritime energy efficiency under variable hydrometeorological conditions. This study compares semi-empirical, physics-based, and artificial intelligence (AI)-based models using full-scale operational and environmental data from a Kamsarmax bulk carrier. The novelty of the work lies in the unified assessment of these three modelling methods under realistic and dynamic ocean conditions. The semi-empirical model estimates fuel use through resistance, propulsion efficiency, and engine fuel consumption relationships, while the physics-based model incorporates hydrodynamic, manoeuvring, and environmental effects. The AI model applies an attention-enhanced bidirectional long short-term memory network to learn nonlinear relationships from operational data. The comparative results show that the AI-based model provides the most accurate and stable predictions across unseen voyages, followed by the physics-guided model, whereas the semi-empirical model shows larger deviations under complex environmental conditions. These findings highlight the potential of AI-based prediction for fuel efficiency optimization and operational decision support, while physics-based modelling remains valuable for interpretation and diagnostic analysis.