A Machine-Learning Approach to Predict Main Energy Consumption under Realistic Operational Conditions

The paper presents a novel and publicly available set of high-quality sensory data collected from a ferry over a period of two months and overviews exixting machine-learning methods for the prediction of main propulsion efficiency. Neural networks are applied on both real-time and predictive settings.

Performance results for the real-time models are shown. The presented models were successfully developed in a trim optimisation application onboard a product tanker.