Prediction of Full-Scale Propulsion Power using Artificial Neural Networks

Benjamin Pjedsted Pedersen, Jan Larsen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Full scale measurements of the propulsion power, ship speed, wind speed and direction, sea and air temperature from four different loading conditions, together with hind cast data of wind and sea properties; and noon report data has been used to train an Artificial Neural Network for prediction of propulsion power. The model was optimized using a double cross validation procedure. The network was able to predict the propulsion power with accuracy between 0.8-1.7% using onboard measurement system data and 7% from manually acquired noon reports.
Original languageEnglish
Title of host publicationCOMPIT '09 : 8th International Conference on Computer and IT Applications in the Maritime Industries
Place of PublicationBudapest, 10-12 May 2009
Publication date2009
Pages537-550
Publication statusPublished - 2009
EventPrediction of Full-Scale Propulsion Power using Artificial Neural Networks - Budapest
Duration: 1 Jan 2009 → …

Conference

ConferencePrediction of Full-Scale Propulsion Power using Artificial Neural Networks
CityBudapest
Period01/01/2009 → …

Keywords

  • propulsion
  • vessel
  • fuel consumption
  • ship
  • performance
  • Artificial neural networks

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