Buildings are becoming increasingly complex. Unexpected difficulties can occur during commissioning. At the same time, many buildings have energy-saving reserves - which can still be increased during later conversions and renovations. This is where the new research project energyTWIN comes in: A digital twin for energy diagnosis is used to test commissioning and subsequent optimization so that they can be carried out faster and better in reality. Digital building models from Building Information Modelling (BIM), artificial intelligence, complex visualizations of building technology and augmented reality will aid in this process. IT and real estate companies from Aachen, Brückeburg, Hamburg and Cologne are working together on the joint project under the leadership of the Geodetic Institute and Char for Building Information Technology & Geoinformation Systems at RWTH Aachen University. The project is funded by the Federal Ministry of Economics and Energy.© GIA
The aim of the project "Energiediagnosestecker Digitaler Zwilling - energyTWIN" is to use various methods to collect data on systems engineering, functions, links, and communication structures for building information models and to make them BIM-capable. For example, technologies like reality capturing (photogrammetry, laser scanning, infrared technology) and methods of artificial intelligence are used for knowledge-based automated point cloud filtering, feature extraction, classification, and modeling. An essential element of the project is the representation via virtual reality (VR) and augmented reality (AR).
As a result, energyTWIN has three "building blocks" that are condensed into a building information model (AS-Built BIM): Information about the Geometry Topology and Systems Engineering Semantics. The topology of the physical system of the buildings is shown in a systems engineering wire-diagram. Finally, the information about functional and information technology relationships between authors and sensors is logically linked.
Exciting for the user: energyTWIN will, for the first time, combine the multitude of geometric, topological, and semantic information simultaneously with cloud-based methods for managing energy-related opening data at the field level. Then, machine learning methods will be used to classify the data to derive information about functional and information-technical correlations and automatically link them.
In this way, energyTWIN opens up a new dimension for BIM and supports the technical building equipment during commissioning, maintenance, and system optimization. It can be used in both new and existing buildings.
energyTWIN is a joint project funded by the German Federal Ministry of Economic Affairs and Energy (BMWi) and coordinated by the Building Informatics and Geoinformation Systems Insitute (gia) of the Faculty of Civil Engineering. Together with the Chair of Energy Efficiency and Sustainable Buildings (E3D) the project works on the topics "AS-Built-Entry" and "AI-based methods".