Towards Data-Driven Models in the Prediction of Ship Performance (Speed—Power) in Actual Seas: A Comparative Study between Modern Approaches

Kiriakos Alexiou, Efthimios G. Pariotis, Helen C. Leligou and Theodoros C. Zannis ()
Additional contact information
Kiriakos Alexiou: Department of Industrial Design and Production Engineering, University of West Attica, 12243 Athens, Greece
Efthimios G. Pariotis: Naval Architecture and Marine Engineering Section, Hellenic Naval Academy, 18539 Piraeus, Greece
Helen C. Leligou: Department of Industrial Design and Production Engineering, University of West Attica, 12243 Athens, Greece
Theodoros C. Zannis: Naval Architecture and Marine Engineering Section, Hellenic Naval Academy, 18539 Piraeus, Greece

Energies, 2022, vol. 15, issue 16, 1-18

Abstract: In the extremely competitive environment of shipping, minimizing shipping cost is the key factor for the survival and growth of shipping companies. However, stricter rules and regulations that aim at the reduction of greenhouse gas emissions published by the International Maritime Organization, force shipping companies to increase the operational efficiency of their fleet. The prediction of a ship speed in actual seas with a given power by its engine is the most important performance indicator and thus makes it the “holy grail” in pursuing better efficiency. Traditionally, tank model tests and semi-empirical formulas were the preferred solution for the aforementioned prediction and are still widely applied. However, currently, with the increased computational power that is widely available, novel and more sophisticated methods taking into consideration computational fluid dynamics (CFD) and machine learning (ML) algorithms are emerging. In this paper, we briefly present the different approaches in the prediction of a ship’s speed but focus on ML methods comparing a representative number of the latest data-driven models used in papers, to provide guidelines, discover trends and identify the challenges to be faced by researchers. From this comparison, we can distinguish that artificial neural networks (ANN), being used in 73.3% of the reviewed papers, dominate as the algorithm of choice. Researchers mostly rely on physical laws governing the phenomena in the crucial part of data preprocessing tasks. Lastly, most researchers rely on data acquisition systems installed at ships in order to achieve usable results.

Keywords: machine learning (ML); supervised algorithms; artificial neural networks (ANN); data driven; fuel oil consumption (FOC); resistance; semi-empirical model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/16/6094/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/16/6094/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:16:p:6094-:d:894818

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().