| Autor: |
Dimitrios Kaklis, Iraklis Varlamis, George Giannakopoulos, Takis J. Varelas, Constantine D. Spyropoulos |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
International Journal of Information Management Data Insights, Vol 3, Iss 2, Pp 100178- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2667-0968 |
| DOI: |
10.1016/j.jjimei.2023.100178 |
| Popis: |
Sustainability and environmental compliance in ship operations is a prominent research topic as the waterborne sector is obliged to adopt ”green” mitigation strategies towards a low emissions operational blueprint. Fuel-Oil-Consumption (FOC) estimation, constitutes one of the key components in maritime transport information systems for efficiency and environmental compliance. This paper deals with FOC estimation in a more novel way than methods proposed in literature, by utilizing a reduced-sized feature set, which allows predicting vessel’s Main-Engine rotational speed (RPM). Furthermore, this work aims to place the deployment of such models in the broader context of a cutting-edge information system, to improve efficiency and regulatory adherence. Specifically, we integrate B-Splines in the context of two Deep Learning architectures and compare their performance against state-of-the-art regression techniques. Finally, we estimate FOC by combining velocity measurements and the predicted RPM with vessel-specific characteristics and illustrate the performance of our estimators against actual FOC data. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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