| Téléchargement | - Voir le manuscrit accepté : Data analytics for greener marine operations: towards a fuel optimization decision support system (PDF, 370 Kio)
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| Auteur | Rechercher : Piercey, Caitlin1 |
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| Affiliation | - Conseil national de recherches Canada. Génie océanique, côtier et fluvial
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| Format | Texte, Allocution |
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| Conférence | NECEC 2023: The 32nd Annual Newfoundland Electrical and Computer Engineering Conference (NECEC 2023), November 14, 2023, St. John's, Newfoundland, Canada |
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| Résumé | We present an exploratory data analysis (EDA) of data obtained from instrumented Canadian Coast Guard (CCG) vessels. The data is analyzed for the purposes of developing a fuel optimization real-time decision support system (DSS) for on-board implementation. The proposed DSS will assist ship navigators in determining the most fuel-efficient operations for their vessel based on controllable parameters of speed, shaft torque, path, etc., resulting in greener operations. We show how the collected data can be integrated with ice data, and how operational modes can be extracted from the data as a new feature. A preliminary predictive model for fuel flow rate using machine learning provides the baseline predictive accuracy of the data for future implementation as a fuel optimisation DSS. |
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| Date de publication | 2023-11-14 |
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| Maison d’édition | IEEE |
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| Note | Paper was presented during the "Computer vision and AI 2" session (B2007A) |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | d756c084-b04e-433e-bfcc-3b467609f384 |
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| Enregistrement créé | 2026-05-01 |
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| Enregistrement modifié | 2026-05-07 |
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