Evaluation of Different Deep-Learning Models for the Prediction of a Ship’s Propulsion Power

Theodoropoulos, Panayiotis; Spandonidis, Christos C.; Themelis, Nikos; Giordamlis, Christos; Fassois, Spilios

doi:10.3390/jmse9020116

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Evaluation of Different Deep-Learning Models for the Prediction of a Ship’s Propulsion Power

Panayiotis Theodoropoulos

¹,

Christos C. Spandonidis

^1,*

Nikos Themelis

Christos Giordamlis

¹ and

Spilios Fassois

Prisma Electronics SA, Leof. Poseidonos 42, 17675 Kallithea, Greece

School of Naval Architecture and Marine Engineering, National Technical University of Athens, Iroon Polytechniou 9, 15780 Zografou, Greece

Department of Mechanical Engineering and Aeronautic, University of Patras, 26500 Patras, Greece

Author to whom correspondence should be addressed.

Academic Editor: Rosemary Norman

J. Mar. Sci. Eng. 2021, 9(2), 116; https://doi.org/10.3390/jmse9020116

Received: 29 December 2020 / Revised: 19 January 2021 / Accepted: 21 January 2021 / Published: 24 January 2021

(This article belongs to the Special Issue Performance Assessment of Ship Energy Efficiency)

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Abstract

Adverse conditions within specific offshore environments magnify the challenges faced by a vessel’s energy-efficiency optimization in the Industry 4.0 era. As the data rate and volume increase, the analysis of big data using analytical techniques might not be efficient, or might even be infeasible in some cases. The purpose of this study is the development of deep-learning models that can be utilized to predict the propulsion power of a vessel. Two models are discriminated: (1) a feed-forward neural network (FFNN) and (2) a recurrent neural network (RNN). Predictions provided by these models were compared with values measured onboard. Comparisons between the two types of networks were also performed. Emphasis was placed on the different data pre-processing phases, as well as on the optimal configuration decision process for each of the developed deep-learning models. Factors and parameters that played a significant role in the outcome, such as the number of layers in the neural network, were also evaluated. View Full-Text

Keywords: propulsion power prediction; ANN; RNN; deep learning

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Theodoropoulos, P.; Spandonidis, C.C.; Themelis, N.; Giordamlis, C.; Fassois, S. Evaluation of Different Deep-Learning Models for the Prediction of a Ship’s Propulsion Power. J. Mar. Sci. Eng. 2021, 9, 116. https://doi.org/10.3390/jmse9020116

AMA Style

Theodoropoulos P, Spandonidis CC, Themelis N, Giordamlis C, Fassois S. Evaluation of Different Deep-Learning Models for the Prediction of a Ship’s Propulsion Power. Journal of Marine Science and Engineering. 2021; 9(2):116. https://doi.org/10.3390/jmse9020116

Chicago/Turabian Style

Theodoropoulos, Panayiotis, Christos C. Spandonidis, Nikos Themelis, Christos Giordamlis, and Spilios Fassois. 2021. "Evaluation of Different Deep-Learning Models for the Prediction of a Ship’s Propulsion Power" Journal of Marine Science and Engineering 9, no. 2: 116. https://doi.org/10.3390/jmse9020116

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Evaluation of Different Deep-Learning Models for the Prediction of a Ship’s Propulsion Power

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