Ship efficiency forecast based on sensors data collection

Improving numerical models through data analytics

Conference paper (2015)

Authors

A. Coraddu University of Genova
Luca Oneto University of Genova
Francesco Baldi Chalmers University of Technology
Davide Anguita University of Genova
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Published Date

2015

Language

English

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Abstract

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.