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Applying artificial neural networks for modelling ship speed and fuel consumption

Abstrakt

This paper deals with modelling ship speed and fuel consumption using artificial neural network (ANN) techniques. These tools allowed us to develop ANN models that can be used for predicting both the fuel consumption and the travel time to the destination for commanded outputs (the ship driveline shaft speed and the propeller pitch) selected by the ship operator. In these cases, due to variable environmental conditions, making decisions regarding setting the proper commanded outputs to is extraordinarily difficult. To support such decisions, we have developed a decision support system. Its main elements are the ANN models enabling ship fuel consumption and speed prediction. To collect data needed for building ANN models, sea trials were conducted. In this paper, the decision support system concept, input and variables of the ship driveline system models, and data acquisition methods are presented. Based on them, we developed appropriate ANN models. Subsequently, we performed a quality assessment of the collected data set, data normalization and division of the data set, selection of an ANN model architecture and assessment of their quality.

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Wersja publikacji
Accepted albo Published Version
Licencja
Creative Commons: CC-BY otwiera się w nowej karcie

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Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
NEURAL COMPUTING & APPLICATIONS nr 32, strony 17379 - 17395,
ISSN: 0941-0643
Język:
angielski
Rok wydania:
2020
Opis bibliograficzny:
Tarełko W., Rudzki K.: Applying artificial neural networks for modelling ship speed and fuel consumption// NEURAL COMPUTING & APPLICATIONS -Vol. 32, (2020), s.17379-17395
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1007/s00521-020-05111-2
Weryfikacja:
Politechnika Gdańska

wyświetlono 29 razy

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