@inbook {4497,
	title = {An agent-based simulation model for energy saving in large passenger and cruise ships},
	booktitle = {CRC Press},
	year = {2021},
	abstract = {Undoubtedly, 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].},
	author = {Christos Bouras and Eirini Barri and Apostolos Gkamas and Nikos Karacapilidis and Dimitris Karadimas and Giorgos Kournetas and Yiannis Panaretou}
}
@inbook {4498,
	title = {A Novel Approach to Energy Management in Large Passenger and Cruise Ships: Integrating Simulation and Machine Learning Models},
	booktitle = {Springer Book of SIMULTECH 2020},
	year = {2021},
	abstract = {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{\textquoteright}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.},
	author = {Christos Bouras and Eirini Barri and Apostolos Gkamas and Nikos Karacapilidis and Dimitris Karadimas and Georgios Kournetas and Yiannis Panaretou}
}
@conference {4457,
	title = {Blending simulation and Machine Learning models to advance energy management in large ships},
	booktitle = {10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2020)},
	year = {2020},
	pages = {101-109},
	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{\textquoteright}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.},
	author = {Eirini Barri and Christos Bouras and Apostolos Gkamas and Nikos Karacapilidis and Dimitris Karadimas and Georgios Kournetas and Yiannis Panaretou}
}
@conference {4463,
	title = {Towards an informative simulation-based application for energy saving in large passenger and cruise ships},
	booktitle = {6th IEEE International Energy Conference (ENERGYCon 2020), Gammarth, Tunisia},
	year = {2020},
	abstract = {Over 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{\textquoteright}s decks and position of energy consuming facilities, as well as data concerning the ship{\textquoteright}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.},
	author = {Eirini Barri and Christos Bouras and Apostolos Gkamas and Nikos Karacapilidis and Dimitris Karadimas and Georgios Kournetas and Yiannis Panaretou}
}
