An Artificial Neural Network for fuel efficiency analysis for cargo vessel operation

Mei Ling Fam, Zhi Yung Tay, Dimitrios Konovessis

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

There is increasing interest in understanding fuel consumption from the perspective of increasing energy efficiency on a vessel. Thus the aim of this paper is to present a new framework for data-driven estimation of fuel consumption by employing a combination of (i) traditional statistical analysis and (ii) Artificial Neural Networks. The output of the analysis is the most frequently occurring fuel-speed curves corresponding to the respective operational profile. The inputs to the model consider important explanatory variables like draft, sea current and wind. The methodology is applied to a case study of a fleet of 9000 twenty-foot equivalent units (TEU) vessels, in which telemetry data on the fuel consumption, vessel speed, current, wind direction and strength were analysed. The performance of the method is validated in terms of error estimation criterion like R 2 values and against physical phenomena obtained from the data. The results can be used to study the economic and environmental benefits of slow-steaming and or fuel levies, or by extending this part of the model into exergy analysis for a more holistic review of energy saving initiatives.
Original languageEnglish
Article number112437
Number of pages11
JournalOcean Engineering
Volume264
Early online date12 Sep 2022
DOIs
Publication statusPublished - 15 Nov 2022

Keywords

  • fuel consumption in vessels
  • artificial neural networks
  • telemetry data

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