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Predicting dynamic fuel oil consumption on ships with automated machine learning

Ahlgren, Fredrik ; Mondejar, Maria E. LU and Thern, Marcus LU (2019) 10th International Conference on Applied Energy, ICAE 2018 In Energy Procedia 158. p.6126-6131
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... (More)

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.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Auto machine learning, Energy efficiency, Predicting fuel consumption, Shipping
in
Energy Procedia
volume
158
pages
6 pages
publisher
Elsevier
conference name
10th International Conference on Applied Energy, ICAE 2018
conference location
Hong Kong, China
conference dates
2018-08-22 - 2018-08-25
external identifiers
  • scopus:85063916104
ISSN
1876-6102
DOI
10.1016/j.egypro.2019.01.499
language
English
LU publication?
yes
id
c879bda0-a10a-4de0-8d03-37e37ba5f114
date added to LUP
2019-04-23 12:41:34
date last changed
2020-01-13 01:39:48
@article{c879bda0-a10a-4de0-8d03-37e37ba5f114,
  abstract     = {<p>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.</p>},
  author       = {Ahlgren, Fredrik and Mondejar, Maria E. and Thern, Marcus},
  issn         = {1876-6102},
  language     = {eng},
  pages        = {6126--6131},
  publisher    = {Elsevier},
  series       = {Energy Procedia},
  title        = {Predicting dynamic fuel oil consumption on ships with automated machine learning},
  url          = {http://dx.doi.org/10.1016/j.egypro.2019.01.499},
  doi          = {10.1016/j.egypro.2019.01.499},
  volume       = {158},
  year         = {2019},
}