Retrofitting using reality capture and data-driven energy analysis methods for ESG criteria

Retrofitting measures play a key role in achieving climate targets, as the building sector accounts for a significant proportion of total emissions. In the EU, this requires the thermal and energy-efficiency refurbishment of 90% of existing buildings. For Austria, this implies doubling the current refurbishment rate, and the financing of future refurbishment projects is increasingly being linked to ESG criteria.

Valid data collection on existing buildings forms the basis for retrofitting projects. At present, the collection of condition data is a manual, error-prone, time-consuming and costly process.

The research project aims to enable the efficient implementation of high-quality refurbishments using digital technologies, as well as to evaluate retrofitting performance as a foundation for ESG rating models. By combining reality-capturing methods with artificial intelligence, pre-classified IFC models are generated semi-automatically. Digital energy consumption data (e.g. smart meters) serves as the basis for optimising energy consumption.

All data is aggregated in a data lake and semantically enriched with attributes from building component catalogues. The analysis of building-specific energy performance is based on data-driven methods and coupled building and system simulations to quantify energy-saving potential, CO₂ reduction and life-cycle assessments. The developed workflow is validated through real-world use cases and will serve in future as a decision-support tool for stakeholders in the construction sector to enable a well-founded assessment of the retrofitting performance of renovation measures.