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Authors: Eirini Barri 1 ; Christos Bouras 1 ; Apostolos Gkamas 2 ; Nikos Karacapilidis 3 ; Dimitris Karadimas 4 ; Georgios Kournetas 3 and Yiannis Panaretou 4

Affiliations: 1 Department of Computer Engineering and Informatics, University of Patras, 26504 Rio Patras, Greece ; 2 University Ecclesiastical, Academy of Vella, Ioannina, Greece ; 3 Industrial Management and Information Systems Lab, MEAD, University of Patras, 26504 Rio Patras, Greece ; 4 OptionsNet S.A, Patras, Greece

ISBN: 978-989-758-444-2

ISSN: 2184-2841

Keyword(s): Agent-based Simulation, Energy Consumption, Cruise Ships, Machine Learning Algorithms.

Abstract: The prediction of energy consumption in large passenger and cruise ships is certainly a complex and challenging issue. Towards addressing it, this paper reports on the development of a novel approach that builds on a sophisticated agent-based simulation model, which takes into account diverse parameters such as the size, type and behavior of the different categories of passengers onboard, the energy consuming facilities and devices of a ship, spatial data concerning the layout of a ship’s decks, and alternative ship operation modes. Outputs obtained from multiple simulation runs are then exploited by prominent Machine Learning algorithms to extract meaningful patterns between the composition of passengers and the corresponding energy demands in a ship. In this way, our approach is able to predict alternative energy consumption scenarios and trigger meaningful insights concerning the overall energy management in a ship. Overall, the proposed approach may handle the underlying uncertain ty by blending the process-centric character of a simulation model and the data-centric character of Machine Learning algorithms. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Barri, E.; Bouras, C.; Gkamas, A.; Karacapilidis, N.; Karadimas, D.; Kournetas, G. and Panaretou, Y. (2020). Blending Simulation and Machine Learning Models to Advance Energy Management in Large Ships. In Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH, ISBN 978-989-758-444-2; ISSN 2184-2841, pages 101-109. DOI: 10.5220/0009876601010109

@conference{simultech20,
author={Eirini Barri. and Christos Bouras. and Apostolos Gkamas. and Nikos Karacapilidis. and Dimitris Karadimas. and Georgios Kournetas. and Yiannis Panaretou.},
title={Blending Simulation and Machine Learning Models to Advance Energy Management in Large Ships},
booktitle={Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH,},
year={2020},
pages={101-109},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009876601010109},
isbn={978-989-758-444-2},
issn={2184-2841},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH,
TI - Blending Simulation and Machine Learning Models to Advance Energy Management in Large Ships
SN - 978-989-758-444-2
IS - 2184-2841
AU - Barri, E.
AU - Bouras, C.
AU - Gkamas, A.
AU - Karacapilidis, N.
AU - Karadimas, D.
AU - Kournetas, G.
AU - Panaretou, Y.
PY - 2020
SP - 101
EP - 109
DO - 10.5220/0009876601010109

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