Predicting dynamic fuel oil consumption on ships with automated machine learning
(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.
(Less)
- author
- Ahlgren, Fredrik ; Mondejar, Maria E. LU and Thern, Marcus LU
- organization
- publishing date
- 2019
- 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
- 2022-04-25 22:40:41
@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}},
keywords = {{Auto machine learning; Energy efficiency; Predicting fuel consumption; Shipping}},
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}},
}