Conference paper
Prediction of Full-Scale Propulsion Power using Artificial Neural Networks
Coastal, Maritime and Structural Engineering, Department of Mechanical Engineering, Technical University of Denmark1
Department of Mechanical Engineering, Technical University of Denmark2
Cognitive Systems, Department of Informatics and Mathematical Modeling, Technical University of Denmark3
Department of Informatics and Mathematical Modeling, Technical University of Denmark4
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.
Language: | English |
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Year: | 2009 |
Pages: | 537-550 |
Proceedings: | Prediction of Full-Scale Propulsion Power using Artificial Neural Networks |
Journal subtitle: | 8th International Conference on Computer and It Applications in the Maritime Industries |
Types: | Conference paper |
ORCIDs: | Larsen, Jan |