01115nas a2200169   4500008004100000245009000041210006900131520044400200100002100644700001800665700002200683700002500705700002400730700002300754700002300777856014500800       2021                        eng d00aAn agent-based simulation model for energy saving in large passenger and cruise ships0 aagentbased simulation model for energy saving in large passenger3 aUndoubtedly, energy saving is of paramount importance in the shipping industry, as far as both the protection of environment and the reduction of the associated operating costs are concerned. In this direction, the
International Maritime Organization aims to reduce ship emissions by at least 50% by 2050, while ships to be built by 2025 are expected to be a massive 30% more energy efficient than those built some years ago [IMO,
2018].1 aBouras, Christos1 aBarri, Eirini1 aGkamas, Apostolos1 aKaracapilidis, Nikos1 aKaradimas, Dimitris1 aKournetas, Giorgos1 aPanaretou, Yiannis  uhttp://telematics.upatras.gr/telematics/publications/agent-based-simulation-model-energy-saving-large-passenger-and-cruise-ships?language=en02057nas a2200169   4500008004100000245013000041210006900171520132000240100002101560700001801581700002201599700002501621700002401646700002401670700002301694856017001717       2021                        eng d00aA Novel Approach to Energy Management in Large Passenger and Cruise Ships: Integrating Simulation and Machine Learning Models0 aNovel Approach to Energy Management in Large Passenger and Cruis3 aIt 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.1 aBouras, Christos1 aBarri, Eirini1 aGkamas, Apostolos1 aKaracapilidis, Nikos1 aKaradimas, Dimitris1 aKournetas, Georgios1 aPanaretou, Yiannis  uhttp://telematics.upatras.gr/telematics/publications/novel-approach-energy-management-large-passenger-and-cruise-ships-integrating-simulation-and-machine?language=en01847nas a2200181   4500008004100000245009600041210006900137300001200206520113500218100001801353700002101371700002201392700002501414700002401439700002401463700002301487856015501510       2020                        eng d00aBlending simulation and Machine Learning models to advance energy management in large ships0 aBlending simulation and Machine Learning models to advance energ  a101-1093 aThe 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.1 aBarri, Eirini1 aBouras, Christos1 aGkamas, Apostolos1 aKaracapilidis, Nikos1 aKaradimas, Dimitris1 aKournetas, Georgios1 aPanaretou, Yiannis  uhttp://telematics.upatras.gr/telematics/publications/blending-simulation-and-machine-learning-models-advance-energy-management-large-ships?language=en01447nas a2200169   4500008004100000245011000041210006900151520073500220100001800955700002100973700002200994700002501016700002401041700002401065700002301089856016501112       2020                        eng d00aTowards an informative simulation-based application for energy saving in large passenger and cruise ships0 aTowards an informative simulationbased application for energy sa3 aOver the years, the need to save energy and efficiently manage its consumption becomes increasingly imperative. This paper reports on the development of a novel application for handling diverse energy consumption issues in large passenger and cruise ships. Our overall approach is based on a comprehensive agent-based simulation model, which takes into account spatial data concerning a ship’s decks and position of energy consuming facilities, as well as data concerning the ship’s passengers and their behavior during the operation of the vessel. The proposed application may predict energy consumption for a particular vessel and passenger group and accordingly facilitate informed decision making on energy saving matters.1 aBarri, Eirini1 aBouras, Christos1 aGkamas, Apostolos1 aKaracapilidis, Nikos1 aKaradimas, Dimitris1 aKournetas, Georgios1 aPanaretou, Yiannis  uhttp://telematics.upatras.gr/telematics/publications/towards-informative-simulation-based-application-energy-saving-large-passenger-and-cruise-ships?language=en