Statistical modelling for ship propulsion efficiency

Jóan Petur Petersen, Daniel J. Jacobsen, Ole Winther

    Research output: Contribution to journalJournal articleResearchpeer-review


    This paper presents a state-of-the-art systems approach to statistical modelling of fuel efficiency in ship propulsion, and also a novel and publicly available data set of high quality sensory data. Two statistical model approaches are investigated and compared: artificial neural networks and Gaussian processes (GP). The data presented is a publicly available full-scale data set, with a whole range of features sampled over a period of 2 months. We further discuss interpretations of the operational data in relation to the underlying physical system.
    Original languageEnglish
    JournalJournal of Marine Science and Technology
    Issue number1
    Pages (from-to)30-39
    Publication statusPublished - 2012


    • Vessel efficiency
    • Artificial neural networks
    • Gaussian processes
    • Energy conservation
    • Advisory systems
    • Computer simulation


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