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 existing machine-learning methods for the prediction of main propulsion efficiency. Neural networks are applied in both real-time and predictive settings. Performance results for the real-time models are shown. The presented models were successfully deployed in a trim optimisation application onboard a product tanker.
A Machine-Learning Approach to Predict Main Energy Consumption under Realistic Operational Conditions
Ship Technology Research ; 59 , 1 ; 64-72
2012-01-01
9 pages
Article (Journal)
Electronic Resource
English
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European Patent Office | 2023
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