Please use this identifier to cite or link to this item:
Files in This Item:
There are no files associated with this item.
Title: Predicting ship fuel consumption using a combination of metocean and on-board data
Authors: Zhou, Yi; Pazouki, Kayvan; Murphy, Alan J.; Uriondo, Zigor; Granado, Igor; Quincoces, Inaki; Fernandes, Jose A.
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.
Keywords: Fuel oil consumption prediction; Ship energy efficiency; Multiple regression; Machine learning; Decision support system; REGRESSION; RIDGE
Issue Date: 2023
Type: Article; Early Access
DOI: 10.1016/j.oceaneng.2023.115509
ISSN: 0029-8018
E-ISSN: 1873-5258
Funder: European Union [869342]
Appears in Publication types:Artículos científicos

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.