DecarbonAIte

Purpose and goal

The aim of this project is to adapt and apply ML algorithms to extract features from publicly available databases to enrich urban digital twin models and provide optimized renovation measures for decision-support. First, the project will develop a ML-based method to extract information needed to simulate the performance of buildings. Second, an optimization method based on Genetic Algorithms will be developed that includes energy simulation, Life Cycle Assessment and a Life Cycle Cost Analysis. Third, the developed methods will be implemented in a decision-support tool.

Expected effects and result

Two main outcomes are expected from this project. First, a scalable and future-proof workflow for enriching digital twins of cities with geometric features and semantic data. Second, the decision support tool will provide stakeholders, including real estate managers and municipalities with the right information for renovation planning.

Planned approach and implementation

The project will be led by Chalmers Industriteknik and is structured into 7 work packages led and supported by different partners. The project will be closely connected to research, communication, and dissemination activities at the Digital Twin Cities Centre (Chalmers), the GATE institute (Sofia University) and ETH Zurich.