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Predicting dynamic ...
Predicting dynamic fuel oil consumption on ships with automated machine learning
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- Ahlgren, Fredrik, 1980- (författare)
- Linnaeus University,Linnéuniversitetet,Sjöfartshögskolan (SJÖ),DISA
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- Mondejar, Maria E. (författare)
- Technical University of Denmark, Denmark
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- Thern, Marcus (författare)
- Lund University,Lunds universitet,Kraftverksteknik,Institutionen för energivetenskaper,Institutioner vid LTH,Lunds Tekniska Högskola,Thermal Power Engineering,Department of Energy Sciences,Departments at LTH,Faculty of Engineering, LTH
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(creator_code:org_t)
- Elsevier, 2019
- 2019
- Engelska.
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Ingår i: Innovative Solutions for Energy Transitions. - : Elsevier. ; 158, s. 6126-6131
- Relaterad länk:
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Energiteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Energy Engineering (hsv//eng)
Nyckelord
- Shipping
- Auto machine learning
- Energy efficiency
- Predicting fuel consumption
- Sjöfartsvetenskap
- Maritime Science
- Auto machine learning
- Energy efficiency
- Predicting fuel consumption
- Shipping
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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