Speaker Details

Assistant Professor, Democritus University of Thrace

Speaker Stelios_Krinidis

Stelios krinidis

Prof. Stelios Krinidis is an Assistant Professor in the Department of Management Science and Technology at the Democritus University of Thrace (DUTH). He received his Diploma degree and Ph.D. Degree in Computer Science from the Department of Computer Science, Aristotle University of Thessaloniki (AUTH), Greece, in 1999 and 2004 respectively. He has also served as a contract professor at the Aristotle University of Thessaloniki, the Democritus University of Thrace and the Technological Institute of Kavala in the period 2005-2012. From 2012 to 2021 he was a postdoctoral researcher at the Center for Research and Technology Hellas (CERTH), one of the largest research organizations in Greece. In November 2021, he was elected Assistant Professor in the Department of Management Science and Technology at International Hellenic University (IHU), while in March 2024, he moved to Democritus University of Thrace (DUTH), where he teaches courses on machine learning, artificial intelligence and big data. His main research interests include computational intelligence, signal processing and analysis, pattern recognition, data analysis, optimization, decision-making techniques, visual analytics, etc. He has authored more than 180 papers in prestigious international scientific journals and conferences, with more than 2.700 cross-references (Google Scholar). He is the owner of a patent, and his works have been distinguished more than 5 times. He has also participated in over 30 research projects funded by the EU and the Greek Government. In 1 of them, he is the project coordinator, in 5, the Scientific Manager of the project team, in 8, the deputy Scientific Manager, while in the rest, he worked as a researcher. Finally, he is a member of the committee of six (6) Doctoral Theses.

Presentation Abstract

FLEdge: A novel hierarchical edge-based flexibility management ecosystem at both building and city level

FLEdge aims to develop a hierarchical flexibility energy management system based on a distributed and decentralized architecture with a novel core element - the Edge-Energy Management (EEM) device. In EEM, energy flexibility will be managed in an automatic, optimal and decentralized manner with the ultimate goal to transform buildings, neighbourhoods, districts and cities to PED. FLEdge will be based on the EEM device, which will process locally all heterogeneous information gathered from the buildings towards energy sources (RES, storage, load) optimization. Furthermore, EEM will be equipped with an intelligent Decision Support System (DSS) which will optimize the management of the building in an automatic manner (e.g., load shifting, dimming, pre-heating, pre-cooling, etc.). FLEdge has a human-centric approach since the ultimate goal of all operations (i.e., energy sources and building operation optimization engines) will be the well-being and comfort of residents. Furthermore, FLEdge EEM will be capable of performing Demand/Response (D/R) commands from the Energy Management Node (EMaN) at upper level. FLEdge will be equipped with the appropriate EMaN nodes installed at neighbourhood, district and city levels to improve their performance as well towards smart and green cities.