The Cities Mission’s 1st central goal is to deliver 100 climate neutral European cities in the EU by 2030. This is a highly complex task and cannot be tackled through separate silo approaches. It is crucial to look at a system of systems and the complexity of a climate neutral city as a whole. In order to be still manageable, this should be approached first from the smallest representative scale, i.e. the District scale. Measuring, analysing, understanding and subsequent modelling of the potential Positive clean Energy District (PED) is necessary to get the best possible picture of the status quo and the extent of the challenge. The digital twin will help to find the most effective integration of solutions and to manage the system in real time and adapt/optimise the real system over time.
-Develop and test a digital twin – of a (project defined) potential Positive clean Energy District (PED) in a European city including: benchmarking, modelling, advanced data analytics, energy forecasting, risk analysis, local grid management, visualization, citizen interaction and behavioural energy aspects, data security strategy
-Use the digital twin for improved evidence-based decision-making and to create district development pathways and laying out a clear timeline for associated transformation actions
-Integrate citizen engagement and co-creation. Test and integrate positive feedback loops and gamification to improve energy awareness and better energy efficient/energy conservation behaviour
-Increased number of (tangible) city planning actions for positive clean energy districts using the (proto-)PED design, development and management digital twin tools (based on pre-market research learnings) using open-standards based components which can be reused elsewhere
-Enhanced data gathering approaches with identification relevant (standardised) multi-dimensional data set and relevant forecasting data, drawing also on the work of common European data spaces, including the smart communities data space
-Consolidation of city sensor network specifications (based on optimal density necessary), complemented by appropriate data gathering approaches for soft data
-Improved performance of AI based self-learning systems for optimization of positive clean energy districts and bottom-up complex models