Suchergebnisse

Περίληψη: One of the main objectives of Municipal Waste Planning and Management in Europe is to integrate the environmental impacts into the process of developing or revising waste management plans, taking into account economic and technological availability constraints. The complex interplay between these conflicting objectives hinders the determination of an optimal Municipal Solid Waste management scheme for municipalities and other competent authorities. The tool was developed for the purpose of supporting the involved actors in waste management planning in Balkan countries under a set of social, technical, environmental, economical and legislative constraints and thus, determining the most effective waste management system for the target area. Most of the Balkan regions lack experience, data availability and infrastructure due to the absence of a waste management strategic and/or monitoring plan. Thus, it was important to be able to overcome these constraints by incorporating default—country specific values, identified through bibliography and the experience of waste management plans in various Balkan regions. The present work describes the structure of the tool and presents the results from the applications of the tool for the region of Attiki and the island of Crete, Greece. ; Περίληψη: Ένας από τους κύριους στόχους της Ολοκληρωμένης Διαχείρισης Στερεών Απορριμάτων στην Ευρώπη είναι η ενσωμάτωση των περιβαλλοντικών επιπτώσεων στη διαδικασία κατάρτισης ή αναθεώρησης των σχεδίων διαχείρισης αποβλήτων, λαμβάνοντας υπόψη τους οικονομικούς και τεχνολογικούς περιορισμούς. Η πολύπλοκη αλληλεπίδραση μεταξύ αυτών των αντικρουόμενων στόχων εμποδίζει τον προσδιορισμό του βέλτιστου σεναρίου διαχείρισης αποβλήτων για τους δήμους και τις λοιπές αρμόδιες αρχές. Το γραφικό περιβάλλον με το ενσωματωμένο Σύστημα Υποστήριξης Αποφάσεων (ΣΥΑ) που παρουσιάζεται στην παρούσα εργασία, αναπτύχθηκε για την υποστήριξη λήψης αποφάσεων των εμπλεκόμενων φορέων στη διαχείριση των αποβλήτων στις Βαλκανικές χώρες δεδομένου ενός συνόλου ...

Over the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with a thermal simulation model of the building developed using detailed thermal simulation engines such as EnergyPlus. As designing better controllers requires interacting with the simulation model, a central part of our methodology is the control improvement (or optimisation) module, facilitating two simulation-based control improvement methodologies: one based in multi-criteria decision analysis methods and the other based on state-space identification of dynamical systems using Gaussian process models and reinforcement learning. We evaluate the proposed methodology in a set of simulation-based experiments using the thermal simulation model of a real building located in Portugal. Our results indicate that the proposed methodology could be a viable alternative to model predictive control-based supervisory control in buildings. ; Research leading to these results has been partially supported by the Modelling Optimization of Energy Efficiency in Buildings for Urban Sustainability (MOEEBIUS) project. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 680517. Georgios Giannakis and Dimitrios Rovas gratefully acknowledge financial support from the European Commission H2020-EeB5-2015 project "Optimised Energy Efficient Design Platform for Refurbishment at District Level" under Contract #680676 (OptEEmAL). Georgios Kontes and Christopher Mutschler gratefully acknowledge financial support from the Federal Ministry of Education and Research of Germany in the framework of Machine Learning Forum (grant number 01IS17071). Georgios Kontes, Natalia Panagiotidou, Simone Steiger and Gunnar Gruen gratefully acknowledge use of the services and facilities of the Energie Campus Nürnberg. The APC was funded by MOEEBIUS project. This paper reflects only the authors' views and the Commission is not responsible for any use that may be made of the information contained therein.