%0 Book Section %B CRC Press %D 2021 %T An agent-based simulation model for energy saving in large passenger and cruise ships %A Christos Bouras %A Eirini Barri %A Apostolos Gkamas %A Nikos Karacapilidis %A Dimitris Karadimas %A Giorgos Kournetas %A Yiannis Panaretou %X 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]. %B CRC Press %G eng %0 Book Section %B Springer Book of SIMULTECH 2020 %D 2021 %T A Novel Approach to Energy Management in Large Passenger and Cruise Ships: Integrating Simulation and Machine Learning Models %A Christos Bouras %A Eirini Barri %A Apostolos Gkamas %A Nikos Karacapilidis %A Dimitris Karadimas %A Georgios Kournetas %A Yiannis Panaretou %X 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. %B Springer Book of SIMULTECH 2020 %G eng %0 Conference Paper %B 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2020) %D 2020 %T Blending simulation and Machine Learning models to advance energy management in large ships %A Eirini Barri %A Christos Bouras %A Apostolos Gkamas %A Nikos Karacapilidis %A Dimitris Karadimas %A Georgios Kournetas %A Yiannis Panaretou %X 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. %B 10th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2020) %P 101-109 %G eng %0 Conference Paper %B 7th International Conference on Energy, Substainability and Climate Change (ESCC 2020), Skiathos, Greece %D 2020 %T A Novel Approach for Handling Diverse Energy Consumption Issues in Large Passenger and Cruise Ships %A Eirini Barri %A Christos Bouras %A Apostolos Gkamas %A Georgios Kournetas %A Nikos Karacapilidis %X 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. %B 7th International Conference on Energy, Substainability and Climate Change (ESCC 2020), Skiathos, Greece %G eng %0 Conference Paper %B 6th IEEE International Energy Conference (ENERGYCon 2020), Gammarth, Tunisia %D 2020 %T Towards an informative simulation-based application for energy saving in large passenger and cruise ships %A Eirini Barri %A Christos Bouras %A Apostolos Gkamas %A Nikos Karacapilidis %A Dimitris Karadimas %A Georgios Kournetas %A Yiannis Panaretou %X 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’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. %B 6th IEEE International Energy Conference (ENERGYCon 2020), Gammarth, Tunisia %G eng %0 Conference Paper %B 2015 International Conference on Interactive Mobile Communication Technologies and Learning, Thessaloniki, Greece %D 2015 %T A Mobile Learning Application for Self-Management of Health and Disease %A Christos Bouras %A Vaggelis Kapoulas %A Nikos Karacapilidis %A Vasileios Kokkinos %A Andreas Papazois %X

Supporting self-management of patients is a highly challenging task, which needs to meaningfully exploit and interrelate approaches and technologies concerning interactive communication, personalized health and mobile learning. In line with these remarks, this paper reports on the development of an innovative clinical decision support platform for selfmanagement of health and disease purposes. Work presented focuses on two basic components of the platform, namely a webbased collaboration support tool and a mobile application, both aiming to augment the interaction of all types of stakeholders with the platform. The functionality of the above components is sketched through a realistic use case.

%B 2015 International Conference on Interactive Mobile Communication Technologies and Learning, Thessaloniki, Greece %P 101-105 %8 November 19 - 20 %G eng