PhD Thesis Final Defense to be held on April 3rd, 2018 at 13:00

Papastamatiou Thesis Image

The examination is open to anyone who wishes to attend.

(Room 0.2.2, Decision Support Systems Laboratory, Old ECE Building)

Thesis Title: Decision Support Systems on Energy Management and Energy Efficiency for buildings in "Smart Cities" using innovative web-based applications.


Cities are becoming more and more focal points of our economies and societies. Energy is an essential component of life in cities, as it supports the whole spectrum of their economic activities and secures a certain level of quality of live. Climate change and the subsequent problems, caused by the on-going urbanization of cities, require urgent actions. To meet their public policy objectives under these circumstances, cities need to change and develop in a smart way, taking into account the issues of energy efficiency and sustainability. The proposed Framework and the novel Decision Support Tools for Cities’ Energy Assessment and Optimization contribute to the transition from “Traditional Cities” to Smart “Energy Cities". In this context the main objective of this doctoral thesis is to present a Decision Support Framework able to assess and optimize the energy use in Smart Cities.

The proposed methodology is addressed to the local authorities of the cities, so as to optimize the energy use in their premises and achieve significant reduction in CO2 emissions. In particular, this thesis contributes to the development of the proposed novel Framework based on two pillars: “Assessment” and “Management”. The ‘Assessment’ pillar highlights the strengths, the underperforming sectors and the potentials of a city in terms of energy optimization. The ‘Management’ pillar includes a number of targeted action plans that can be used by the energy managers of a city. The proposed actions in this pillar derive from the DSS for Energy Management component of the Framework that offers short-term scenarios on a weekly basis, and from the DSS for Energy Efficiency component that offers long-term scenarios on a yearly basis.

Supervisor: Psarras John, Professor

PhD student: Papastamatiou Ilias