Most modern ships have several measurement evices that keep track of the vessel speed, fuel consumption, weather conditions etc. Storing, analysing and acting upon these data could become a valuable asset for the ship owners and operators. In this article a freely available data set is presented collected on a ferry. The data were used to develop the models used in an onboard trim optimisation application. 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.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A machine-learning approach to predict main energy consumption under realistic operational conditions


    Additional title:

    Maschinelles Lernverfahren zur Vorhersage des Hauptenergieverbrauches unter realistischen Betriebsbedingungen


    Contributors:

    Published in:

    Publication date :

    2012


    Size :

    9 Seiten, 9 Bilder, 3 Tabellen, 11 Quellen



    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English





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

    Petersen, Joan P. / Winther, Ole / Jacobsen, Daniel J. | Taylor & Francis Verlag | 2012


    Simulation Evaluation of Controller-Managed Spacing Tools under Realistic Operational Conditions

    Callantine, Todd J. / Hunt, Sarah M. / Prevot, Thomas | NTRS | 2014


    MACHINE LEARNING TO PREDICT PART CONSUMPTION USING FLIGHT DEMOGRAPHICS

    SANZONE ANDREA / STERLING MILLIE / ASHOK RAHUL et al. | European Patent Office | 2023

    Free access

    Machine Learning Application to Predict Turbocharger Performance under Steady-State and Transient Conditions

    Jeyamoorthy, Arravind / Tanabe, Iku / Kusaka, Jin et al. | SAE Technical Papers | 2021