The CI-NERGY ITN will provide a very high quality training network for urban energy management, where high profile universities and research centres cooperate with the industrial sector (both SME and large enterprises) to develop solutions for the smart cities of tomorrow. The goal of the CI-NERGY ITN is to develop a comprehensive methodology for the planning and operation of future energy systems of cities through the training and cultivation of ERs. The approach adopts a holistic vision that integrates energy efficiency measures together with energy services synergies, behavioural changes, innovative buildings design, advanced energy conversion technologies and advanced control strategies. The methodologies chosen for each PhD project depends on the scale (component, building, city quarter) and Level of Detail associated with the research question: Within the high-level decision support working group, the first step is the analysis of the stakeholder needs within the two case study cities and the definition of use cases. The resulting data information management and software architecture development is then iteratively refined throughout the research program by continuous feedback from the case study cities. The urban energy modelling group on the other hand, uses both laboratory and living lab experiments to calibrate and validate all models developed.

The intent is to create through the PhD research projects the following relevant tools and ultimately, integrate these under one unified platform:

  • 1. Semantically enriched GIS databases for energy modelling based on automated capture and interpretation of urban features.
  • 2. A set of simulation models to represent the system dynamics including different energy sources and qualities, the buildings’ performances, the interconnecting grids and the technologies that will interact with stochastic inhabitant behaviours and the control and management strategies related to the operation of the integrated system.
  • 3. A decision support system using analysis and optimization techniques to assist in the selection and the characterization of the most promising options (ecological and economical) and the definition of potential markets for energy services companies and industry.
  • 4. The research methodology is appropriate to the goal in that it provides ample opportunity for various private and academic partners from the energy management field to contribute to model accuracy and further opportunity to apply these tools to case study cities, allowing for model validation.