A Novel Approach To Energy Management In Large Passenger And Cruise Ships: Integrating Simulation And Machine Learning Models

It has been broadly admitted that the prediction of energy consumption in large passenger and cruise ships is a complex and challenging issue. Aiming to address it, this chapter 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. According to the proposed approach, 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. The chapter also describes the overall architecture of the proposed solution, which is based on the microservices approach.