Predicting dynamic fuel oil consumption on ships with automated machine learning

Fredrik Ahlgren*, Maria E. Mondejar, Marcus Thern

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

50 Downloads (Pure)

Abstract

This study demonstrates a method for predicting the dynamic fuel consumption on board ships using automated machine learning algorithms, fed only with data for larger time intervals from 12 hours up to 96 hours. The machine learning algorithm trained on dynamic data from shorter time intervals of the engine features together with longer time interval data for the fuel consumption. To give the operator and ship owner real-time energy efficiency statistics, it is essential to be able to predict the dynamic fuel oil consumption. The conventional approach to getting these data is by installing additional mass flow meters, but these come with added cost and complexity. In this study, we propose a machine learning approach using auto machine learning optimisation, with already available data from the machinery logging system.
Original languageEnglish
JournalEnergy Procedia
Volume158
Pages (from-to)6126-6131
ISSN1876-6102
DOIs
Publication statusPublished - 2019
Event10th International Conference on Applied Energy - Hong Kong Polytechnic University, Hong Kong, Hong Kong
Duration: 22 Aug 201825 Aug 2018
http://www.applied-energy.org/icae2018/

Conference

Conference10th International Conference on Applied Energy
LocationHong Kong Polytechnic University
CountryHong Kong
CityHong Kong
Period22/08/201825/08/2018
Internet address

Cite this

@article{774788e9e9234545931792d80f6bdc11,
title = "Predicting dynamic fuel oil consumption on ships with automated machine learning",
abstract = "This study demonstrates a method for predicting the dynamic fuel consumption on board ships using automated machine learning algorithms, fed only with data for larger time intervals from 12 hours up to 96 hours. The machine learning algorithm trained on dynamic data from shorter time intervals of the engine features together with longer time interval data for the fuel consumption. To give the operator and ship owner real-time energy efficiency statistics, it is essential to be able to predict the dynamic fuel oil consumption. The conventional approach to getting these data is by installing additional mass flow meters, but these come with added cost and complexity. In this study, we propose a machine learning approach using auto machine learning optimisation, with already available data from the machinery logging system.",
author = "Fredrik Ahlgren and Mondejar, {Maria E.} and Marcus Thern",
year = "2019",
doi = "10.1016/j.egypro.2019.01.499",
language = "English",
volume = "158",
pages = "6126--6131",
journal = "Energy Procedia",
issn = "1876-6102",
publisher = "Elsevier",

}

Predicting dynamic fuel oil consumption on ships with automated machine learning. / Ahlgren, Fredrik; Mondejar, Maria E.; Thern, Marcus.

In: Energy Procedia, Vol. 158, 2019, p. 6126-6131.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Predicting dynamic fuel oil consumption on ships with automated machine learning

AU - Ahlgren, Fredrik

AU - Mondejar, Maria E.

AU - Thern, Marcus

PY - 2019

Y1 - 2019

N2 - This study demonstrates a method for predicting the dynamic fuel consumption on board ships using automated machine learning algorithms, fed only with data for larger time intervals from 12 hours up to 96 hours. The machine learning algorithm trained on dynamic data from shorter time intervals of the engine features together with longer time interval data for the fuel consumption. To give the operator and ship owner real-time energy efficiency statistics, it is essential to be able to predict the dynamic fuel oil consumption. The conventional approach to getting these data is by installing additional mass flow meters, but these come with added cost and complexity. In this study, we propose a machine learning approach using auto machine learning optimisation, with already available data from the machinery logging system.

AB - This study demonstrates a method for predicting the dynamic fuel consumption on board ships using automated machine learning algorithms, fed only with data for larger time intervals from 12 hours up to 96 hours. The machine learning algorithm trained on dynamic data from shorter time intervals of the engine features together with longer time interval data for the fuel consumption. To give the operator and ship owner real-time energy efficiency statistics, it is essential to be able to predict the dynamic fuel oil consumption. The conventional approach to getting these data is by installing additional mass flow meters, but these come with added cost and complexity. In this study, we propose a machine learning approach using auto machine learning optimisation, with already available data from the machinery logging system.

U2 - 10.1016/j.egypro.2019.01.499

DO - 10.1016/j.egypro.2019.01.499

M3 - Journal article

VL - 158

SP - 6126

EP - 6131

JO - Energy Procedia

JF - Energy Procedia

SN - 1876-6102

ER -