Singapore Institute of Technology
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An Artificial Neural Network based decision support system for cargo vessel operations

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conference contribution
posted on 2022-04-20, 04:19 authored by Mei Ling Fam, Zhi Yung TayZhi Yung Tay, Dimitrios Konovessis

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 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 R2 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.


Journal/Conference/Book title

31st European Safety and Reliability Conference (ESREL 2021)

Publication date



  • Post-print

Project ID

  • 22 (R-MOE-E103-F010) Sustainable Marine Transport: Ship Energy Systems & Environmental Technologies

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