A Novel Approach to Energy Management in Large Passenger and Cruise Ships: Integrating Simulation and Machine Learning Models

Abstract

It is generally confessed that the energy consumption in large passenger and cruise ships cannot be predicted and that is a complex and also difficult issue. Intending to resolve it, this chapter reports on an unique technique that improves an innovative agent-based simulation model, which considers varied specifications such as the size, kind as well as behavior of the different categories of passengers onboard, the energy consuming centers as well as devices of a ship, spatial data concerning the layout of a ship’s decks, and also alternate ship procedures. Based on the suggested approach, results acquired from multiple simulation runs are then used up by appropriate Machine Learning algorithms to extract purposeful patterns in between the structure of passengers as well as the corresponding energy needs in a ship. By doing this, our approach is able to predict different energy consumption situations as well as activate significant insights concerning the total power management in a ship. On the whole, the proposed approach may handle the hidden unpredictability by blending the process-centric character of a simulation model and the data-centric character of Machine Learning algorithms. The chapter also describes the general architecture of the suggested remedy, which is based upon the microservices strategy.