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Ship efficiency forecast based on sensors data collection: Improving numerical models through data analytics

A. Coraddu ; L. Oneto ; Francesco Baldi (Institutionen för sjöfart och marin teknik, Maritim miljö och energisystem) ; D. Anguita
MTS/IEEE OCEANS 2015 - Genova, Italy, 18-21 May 2015 (2015)
[Konferensbidrag, refereegranskat]

In this paper authors investigate the problem of predicting the fuel consumption of a vessel in real scenario based on data measured by the onboard automation systems. The goal is achieved by exploiting three different approaches: 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. Author proposal is a Gray Box Model (GBM) which is able to exploit both mechanistic knowledge of the underlying physical principles and available measurements. Results on real world data shows 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.

Nyckelord: Fuel Consumption, Gray Box Model, Machine Learning, Naval Propulsion Plant, Ship Efficiency

Denna post skapades 2016-03-02. Senast ändrad 2016-09-12.
CPL Pubid: 232659


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Institutioner (Chalmers)

Institutionen för sjöfart och marin teknik, Maritim miljö och energisystem (2015-2017)



Chalmers infrastruktur