The digital energy twin

Reason and background

The energy performance of buildings is playing an increasingly important role for building owners, operators, local authorities and residents, not least due to rising energy prices. With the introduction of the Building Energy Act in 2020, public interest is focussing in particular on the energy-efficient refurbishment of existing buildings.

At the same time, cities and local authorities are becoming increasingly interested in providing digital services and information. If the information is linked to a 3D visualisation, this is also referred to as a digital twin of the city. However, there are usually major deficits in the level of detail, completeness and consistency of the data. Efforts are being made to standardise data management (neighbourhood level: CityGML, XPlanung, building level: BIM/IFC, facility level: Administration Shell (AAS)), but these are still not used and linked enough in practice. The energy consumption of buildings has so far been relatively complex and laborious to record and has therefore only been processed on a large scale in individual cases. The realisation of a "digital energy twin", which provides an overview of the energy consumption of individual buildings and entire neighbourhoods in a city, is currently still coming up against limits in terms of data collection, data exchange and data protection.  

Solution approach

A methodology is needed to capture and link information from different information sources with relatively simple means and make it exchangeable across domains. This should make it possible to expand an existing digital twin of a city to include energy consumption data from buildings. Innovative tools for the digitalisation and integration of existing data sources as well as new approaches for data use are being developed. The digital energy twin can be used both as a visualisation tool and for urban development planning and the development of refurbishment concepts. Our research focuses here include 

  • the sensor-based recording of energy data, 
  • the management, harmonisation and standardisation of energy data at neighbourhood, building and facility level (GIS / BIM / AAS) 
  • as well as the determination of an integral energy footprint by linking AAS and BIM.

Further information on the projects can be found here!

ODH@Bochum-Weitmar

Smart-E-Factory

Big Data Platforms

Machine-Learning