iCity 2: EnSim4iCity project

In the intensification phase of the smart city flagship project iCity, insights and networks from the initial phase are used to advance the implementation of new concepts for sustainable, energy-efficient and resource-saving urban development.

Sensor data is increasingly being collected and made available in the course of smart building, smart city and Industry 4.0 projects. In the impulse project "EnSim4iCity", the potential of this data is used to increase the economic efficiency of commercially and industrially used buildings and entire industrial locations and to improve the quality of life and work in industrial and urban environments.

To this end, different aspects and different technological approaches are being considered in 3 sub-projects. For a specific industrial site, which is a self-contained, largely energy-autonomous unit, sub-project 1 uses data from classic building management systems to develop an automated data analysis for an entire property with over 70 buildings. However, there are also buildings on this site that are not connected to the building management system. For these, sub-project 2 is investigating potential as well as suitability of decentralized wireless solutions so far only used in the smart home sector.

For urban neighborhoods, the pollution caused by road traffic is being investigated in particular. Since wind currents, which contribute significantly to the transport of pollutants, can only be measured in a real urban area with considerable effort, subproject 3 uses numerical flow simulation to determine local wind currents around buildings and thus to analyze and evaluate the effect of both natural and technical particulate matter reduction measures away from the measuring stations.

The development of automated procedures significantly facilitates the energy management of a large industrial property and makes it more efficient. The use of smart home technologies as an extension of classic building automation in industrial and commercial environments is enabled through the systematic recording and analysis of use cases.

The further development of simulation methods allows the understanding and analysis of traffic-induced pollution and enables the targeted evaluation and optimization of pollution reduction measures in urban environments.