Blending Simulation and Machine Learning Models to Advance Energy Management in Large Ships

Eirini Barri, Christos Bouras, Apostolos Gkamas, Nikos Karacapilidis, Dimitris Karadimas, Georgios Kournetas, Yiannis Panaretou


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 uncertainty by blending the process-centric character of a simulation model and the data-centric character of Machine Learning algorithms.


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