Positive Energy District (PED) is graded as an essential component towards climate neutrality and smart cities. The development of PED brings great needs in new processes and the related cooperation among stakeholders. This project (PED-ACT) aims to optimise the process iteratively of PED development for enhanced stakeholder cooperation and reinforced decision-making.

PED-ACT extracts the main characterization of PEDs automatically by machine learning approaches, through standardisation of the common database for existing PEDs, by focusing on Sweden, Turkey, and Austria. The digital PED references are then generated, based on a multi-criteria characterization matrix, by auto-matching the development needs/priorities from different stakeholders during bilateral learning and co-design processes among four specific cities (Borlänge, Umea, Karsiyaka-Izmir, Ankara) and a county (Lower Austria). PED-ACT finally evaluates the feasibility of these digital PED references to further develop the replication/upscaling plans, towards climate-neutrality and smart cities goals.

PED-ACT deeply learns a wealth of information from the existing PEDs and then maps the practical needs for PED development in local contexts iteratively. It leads to concrete cooperation from the outset with support of data and involvement of stakeholders. The project results will reinforce decision-making of PEDs and support public administrators/local stakeholders in the new pathways and development.