Buildings are responsible for 40% of energy consumption and 36% of CO₂ emissions in Europe. Optimization of building technology is necessary to achieve the goals of the European Green Deal.

The actual energy consumption of buildings often deviates by up to 30% from the planned values due to operating errors and unforeseen user behavior. Traditional methods for fault detection and diagnosis (FDD) require a great deal of manual effort and are unsuitable for widespread use due to a lack of scalability and explainability. The lack of availability and consistency of semantic building data makes it difficult to implement automated solutions.

In the project, Causal AI methods are developed that derive semantic data based on time series on the one hand and are used for FDD applications on the other. Causal methods offer the advantage that they aim to identify cause-and-effect relationships instead of only statistical correlations, which increases the explainability of the models and significantly improves their performance and reliability compared to conventional approaches.

The consortium combines ISTA's knowledge in the theoretical foundations of Causal AI with TU Wien's applied research in the field of AI for building and energy systems, complemented by the practical expertise of industry partners DiLT Analytics and EAM Systems. Together, the consortium covers the entire innovation chain, from basic research to the implementation of market-ready technologies, and aims to create the foundation for a technological leadership in the field of causality-based, scalable FDD solutions.