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.


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    Title :

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


    Contributors:

    Published in:

    Publication date :

    2012-01-01


    Size :

    9 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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