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

Publication: ResearchJournal article – Annual report year: 2012

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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
Publication date2012
Volume59
Issue1
Pages64-72
ISSN0937-7255
StatePublished

Keywords

  • Neural net, Power prognosis, Trim
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