Predicting dynamic fuel oil consumption on ships with automated machine learning

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

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.

Detaljer

Författare
Enheter & grupper
Externa organisationer
  • Linnaeus University
  • Technical University of Denmark
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Energiteknik

Nyckelord

Originalspråkengelska
Sidor (från-till)6126-6131
Antal sidor6
TidskriftEnergy Procedia
Volym158
StatusPublished - 2019
PublikationskategoriForskning
Peer review utfördJa
Evenemang10th International Conference on Applied Energy, ICAE 2018 - Hong Kong, Kina
Varaktighet: 2018 aug 222018 aug 25