Leveraging big data for fuel oil consumption modelling

Gkerekos, Christos and Lazakis, Iraklis and Papageorgiou, Stylianos; (2018) Leveraging big data for fuel oil consumption modelling. In: 17th Conference on Computer and IT Applications in the Maritime Industries. Technische Universität Hamburg-Harburg, Hamburg, pp. 144-152. ISBN 9783892207078 (http://data.hiper-conf.info/compit2018_pavone.pdf)

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Abstract

Fuel oil consumption constitutes over 25% of a vessel’s overall running costs. Therefore, accurately forecasting, and optimising fuel costs majorly impacts a vessel’s operation sustainability and profitability. This paper presents data-driven, multivariate main engine fuel consumption models leveraging the vast amount of data currently being recorded onboard vessels. Different data-driven modelling methodologies, such as shallow neural networks, deep neural networks, support vector machines, and random forest regressors are presented and implemented, comparing results. The suggested multivariate modelling allows the uncovering of latent interconnections that increase the robustness of the model in varied operating conditions.

ORCID iDs

Gkerekos, Christos ORCID logoORCID: https://orcid.org/0000-0002-3278-9806, Lazakis, Iraklis ORCID logoORCID: https://orcid.org/0000-0002-6130-9410 and Papageorgiou, Stylianos;
CORE (COnnecting REpositories)