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Maritime fuel efficiency : ship fuel consumption prediction using machine learning and deep learning ; Synergies in data analytics and cyber security. DACS 2024. Lecture notes in electrical engineering, vol. 1479
| Τίτλος: | Maritime fuel efficiency : ship fuel consumption prediction using machine learning and deep learning ; Synergies in data analytics and cyber security. DACS 2024. Lecture notes in electrical engineering, vol. 1479 |
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| Συγγραφείς: | Sharma, Utkarsh, Zhou, Zeyang, Puthal, Deepak, Li, Jun, Tran, Tien Anh, West, Jason, Prasad, Mukesh |
| Στοιχεία εκδότη: | Springer Nature |
| Έτος έκδοσης: | 2026 |
| Συλλογή: | University of Malta: OAR@UM / L-Università ta' Malta |
| Θεματικοί όροι: | Ships -- Fuel consumption, Ships -- Energy conservation, Marine engineering -- Data processing, Machine learning, Deep learning (Machine learning) |
| Περιγραφή: | An accurate fuel consumption prediction system for transportation units is crucial for efficient fuel management, offering both cost reduction and emission savings. While extensive research has been conducted on fuel prediction for modes like airplanes, trucks, and vehicles, studies on cargo ships are scarce and often rely on traditional machine learning models. The complexity of real-world factors, such as data collection challenges and varying weather conditions, adds to the difficulty of accurate prediction. This paper addresses these challenges by comparing traditional machine learning algorithms with advanced deep learning models for predicting fuel consumption in ship engines. Our comparative study shows that LSTM-GRU hybrid models emerge as particularly effective, capturing the intricate dependencies and variabilities inherent in fuel consumption forecasting. The results underscore the superior capability of deep learning models, particularly LSTM-GRU, over tradi-tional regression techniques in managing the complexities of fuel consumption in cargo ships. ; peer-reviewed |
| Τύπος εγγράφου: | book part |
| Γλώσσα: | English |
| Relation: | https://www.um.edu.mt/library/oar/handle/123456789/143517 |
| DOI: | 10.1007/978-981-95-2680-2_53 |
| Διαθεσιμότητα: | https://www.um.edu.mt/library/oar/handle/123456789/143517 https://doi.org/10.1007/978-981-95-2680-2_53 |
| Rights: | info:eu-repo/semantics/restrictedAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. |
| Αριθμός Καταχώρησης: | edsbas.5F222577 |
| Βάση Δεδομένων: | BASE |
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