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Predicting ship fuel consumption using a combination of metocean and on-board data

Lookup NU author(s): Dr Yi Zhou, Dr Kayvan Pazouki, Dr Alan J Murphy

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

Fuel Oil Consumption (FOC) accounts for a significant proportion of a vessel's operating costs. The cost of fuel for a fishing vessel operation may often go up to 50% or more. Accurate forecasting FOC in voyage planning stage is essential for route optimization decision support system with the objective of fuel-saving, which is difficult because the future state of the vessel and its power and machinery systems for fuel modelling are not available during route planning stage. Moreover, the state of the environment conditions and its impact on vessel performance should be considered. In this paper, machine learning approaches were applied to predict FOC from plannable in-situ variables and modelled speed through water. The latter is estimated from speed over ground and environmental variables in this work, whose prediction is also critical for decision support systems to avoid collisions. By applying the proposed methodology, the final selected Random Forest models can achieve high mean accuracies (over 92%) in predicting fuel consumption on unseen future data.

Publication metadata

Author(s): Zhou Y, Pazouki K, Murphy AJ, Uriondo Z, Granado I, Quincoces I, Fernandes-Salvador JA

Publication type: Article

Publication status: Published

Journal: Ocean Engineering

Year: 2023

Volume: 285

Issue: 2

Print publication date: 01/10/2023

Online publication date: 01/08/2023

Acceptance date: 29/07/2023

Date deposited: 01/08/2023

ISSN (print): 0029-8018

ISSN (electronic): 1873-5258

Publisher: Elsevier

URL: https://doi.org/10.1016/j.oceaneng.2023.115509

DOI: 10.1016/j.oceaneng.2023.115509

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