energy diagnostic connector
New sensor-based and KI-based methods for the digital, BIM-based commissioning of technical systems in building structures and their energetic system optimization.
- Method development for automated generation of "digital twins" in the commissioning of technical building equipment and its continuous information enrichment in the operation and utilization phase,
- Recognition of geometry, system topology and energetic-functional and information-technical relationships of technical building equipment during construction and technical commissioning,
- Novel methods for high-resolution data capturing and automated as-built modeling based on as-planned data,
- Data segmentation and classification for the automatic recognition of components in mass data (e.g. point clouds, images),
- Organization and structured processing of data taking functional relationships into account,
- Application of new 5D BIM tools and intelligent asset management workflows,
- Use of artificial intelligence (AI) for digitalization of the “Energiewende” in the building sector.
Digitalization is a megatrend in all areas of the economy and society.
This is particularly true for the construction industry, which has a lot catching up to do compared to other sectors of the economy, since it is one of the least digitalized industries. From the perspective of the “Energiewende”, digitalization is a key component for managing/handling the volatility of renewable energies in the electricity, heat and mobility sectors on both the generation and consumption side.
In the future, more sophisticated solutions will be necessary to capture the increasingly complex energy system technologies. The highly complex linkage between technical building equipment (TBE) and building automation (BAM) must be captured by information and communication technology (ICT). For managing and analyzing all data in a holistic way, building information models (BIM) are to be used in a form that is comprehensible to humans.
To master the increasing complexity in future, moreover artificial intelligence (AI) and machine learning methods must be utilized.
The aim of the energyTWIN project is therefore to develop methods for the automated generation of "digital twins" in the commissioning of technical building equipment and its continuous information enrichment in the operation and utilization phase.