Vessels fuel consumption forecast and trim optimisation: a data analytics perspective

Andrea Coraddu, Luca Oneto, Francesco Baldi, Davide Anguita

Research output: Contribution to journalReview articlepeer-review

59 Citations (Scopus)


In this paper the authors investigate the problems of predicting the fuel consumption and of providing the best value for the trim of a vessel in real operations based on data measured by the onboard automation systems. Three different approaches for the prediction of the fuel consumption are compared: White, Black and Gray Box Models. White Box Models (WBM) are based on the knowledge of the physical underling processes. Black Box Models (BBMs) build upon statistical inference procedures based on the historical data collection. Finally, the authors propose two different Gray Box Model (GBM) which are able to exploit both mechanistic knowledge of the underlying physical principles and available measurements. Based on these predictive models of the fuel consumption a new strategy for the optimisation of the trim of a vessel is proposed. Results on real world operational data show that the BBM is able to remarkably improve a state-of-the-art WBM, while the GBM is able to encapsulate the a-priory knowledge of the WBM into the BBM so to achieve the same performance of the latter but requiring less historical data. Moreover, results show that the GBM can be used as an effective tool for optimising the trim of a vessel in real operational conditions.

Original languageEnglish
Pages (from-to)351-370
Number of pages20
JournalOcean Engineering
Early online date13 Dec 2016
Publication statusPublished - 15 Jan 2017


  • black box models
  • data analytics
  • fuel consumption
  • gray box model
  • naval propulsion plant
  • numerical models
  • sensors data collection
  • ship efficiency
  • trim optimisation
  • white box models


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