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

Joan P Petersen, Ole Winther, Daniel J Jacobsen

    Research output: Contribution to journalJournal articleResearch

    Abstract

    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.
    Original languageEnglish
    JournalShip Technology Research
    Volume59
    Issue number1
    Pages (from-to)64-72
    ISSN0937-7255
    Publication statusPublished - 2012

    Keywords

    • Neural net
    • Power prognosis
    • Trim

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