Navigating the complexity of urban renovation requires balancing conflicting goals: minimizing costs, maximizing energy savings, and ensuring social acceptance. The AI Decision Support engine solves this puzzle. Developed by UCD and TUM, with generative design inputs from IAAC, this tool harnesses machine learning to analyze vast datasets, including building typology, material costs, and user preferences.

Acting as a smart advisor, the AI automatically generates and evaluates thousands of potential renovation scenarios. It identifies the “optimal” roadmap for each building, recommending the best combination of technologies (e.g., specific insulation thickness, PV sizing, heat pump capacity) to reach net-zero targets while respecting budget constraints. It transforms raw data into actionable, optimized strategies for decision-makers.