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Power prediction for a vessel without recorded data using data fusion from a fleet of vessels

Power prediction for a vessel without recorded data using data fusion from a fleet of vessels
Power prediction for a vessel without recorded data using data fusion from a fleet of vessels
Recent legislation in shipping applies additional pressure to reducing fuel consumption. However, this is impossible without accurate power prediction, as it is required to allow comparisons between novel efficiency improving advancements and to have confidence in route optimisation. This prediction is particularly difficult in rough weather, which the traditional prediction methods struggle to account for. Neural networks trained on an operational dataset from the vessel are a potential solution to this problem, as they have been shown to predict powering to a mean error of 2% across all weather conditions. However, the gathering of these data is expensive and time consuming. There is currently no literature looking at how data from one vessel can be used to make predictions about another, reducing the cost and allowing prediction of the performance of new vessels. This paper investigates the accuracy in predicting powering for an unseen vessel, using a neural network trained on a fusion of data, from a range of sensors located on other vessels in a fleet. It demonstrates the level of extrapolation that can be achieved from the use of multiple datasets on a real application and suggests that, for the fleet of vessels used, ship parameters are less important for accurate power prediction than having sufficient data across the desired prediction domain. It concludes that prediction of around 4% error can be achieved for most ships in the fleet and discusses the cause of the higher errors seen for a minority of other vessels.
Machine learning, Naval architecture, Neural networks, Ocean engineering, Shaft power prediction
0957-4174
Parkes, Amy, Isabel
9fbc0481-7bcf-4d15-8474-4df77d4338ef
Sobey, Adam
e850606f-aa79-4c99-8682-2cfffda3cd28
Hudson, Dominic
3814e08b-1993-4e78-b5a4-2598c40af8e7
Savasta, Thomas
33a76398-3ff3-4479-8f10-f0d62011cf0a
Parkes, Amy, Isabel
9fbc0481-7bcf-4d15-8474-4df77d4338ef
Sobey, Adam
e850606f-aa79-4c99-8682-2cfffda3cd28
Hudson, Dominic
3814e08b-1993-4e78-b5a4-2598c40af8e7
Savasta, Thomas
33a76398-3ff3-4479-8f10-f0d62011cf0a

Parkes, Amy, Isabel, Sobey, Adam, Hudson, Dominic and Savasta, Thomas (2021) Power prediction for a vessel without recorded data using data fusion from a fleet of vessels. Expert Systems with Applications, 187, [115971]. (doi:10.1016/j.eswa.2021.115971).

Record type: Article

Abstract

Recent legislation in shipping applies additional pressure to reducing fuel consumption. However, this is impossible without accurate power prediction, as it is required to allow comparisons between novel efficiency improving advancements and to have confidence in route optimisation. This prediction is particularly difficult in rough weather, which the traditional prediction methods struggle to account for. Neural networks trained on an operational dataset from the vessel are a potential solution to this problem, as they have been shown to predict powering to a mean error of 2% across all weather conditions. However, the gathering of these data is expensive and time consuming. There is currently no literature looking at how data from one vessel can be used to make predictions about another, reducing the cost and allowing prediction of the performance of new vessels. This paper investigates the accuracy in predicting powering for an unseen vessel, using a neural network trained on a fusion of data, from a range of sensors located on other vessels in a fleet. It demonstrates the level of extrapolation that can be achieved from the use of multiple datasets on a real application and suggests that, for the fleet of vessels used, ship parameters are less important for accurate power prediction than having sufficient data across the desired prediction domain. It concludes that prediction of around 4% error can be achieved for most ships in the fleet and discusses the cause of the higher errors seen for a minority of other vessels.

Text
Power_prediction_for_a_vessel_without_recorded_data_using_data_fusion_from_a_fleet_of_vessels_SUBMITTED - Accepted Manuscript
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Accepted/In Press date: 22 September 2021
e-pub ahead of print date: 29 September 2021
Published date: 29 September 2021
Additional Information: Funding Information: This work was kindly funded by Shell Shipping and Maritime. The authors acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. Publisher Copyright: © 2021 Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Keywords: Machine learning, Naval architecture, Neural networks, Ocean engineering, Shaft power prediction

Identifiers

Local EPrints ID: 451719
URI: http://eprints.soton.ac.uk/id/eprint/451719
ISSN: 0957-4174
PURE UUID: 30960759-c842-4e1b-97e7-adbd04654669
ORCID for Adam Sobey: ORCID iD orcid.org/0000-0001-6880-8338
ORCID for Dominic Hudson: ORCID iD orcid.org/0000-0002-2012-6255

Catalogue record

Date deposited: 21 Oct 2021 16:32
Last modified: 28 Apr 2022 01:58

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Contributors

Author: Amy, Isabel Parkes
Author: Adam Sobey ORCID iD
Author: Dominic Hudson ORCID iD
Author: Thomas Savasta

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