Both presentations dealt with the efficient simulation of large district heating systems. All forecasts assume that district heating will play a major role in the decarbonization of the building sector. Such simulations are important for the further development of existing district heating systems and the planning of new networks. In her contribution “Simplified representation of buildings in district heating network models – a data driven approach”, Julia Eicke showed how buildings can be classified and grouped in such a way that the computing times of the simulations are significantly reduced without greatly affecting the accuracy of the results. In his contribution “Modeling heat loads and return temperatures of buildings connected to a district heating network using a neural network”, Frederik Feike presented a method that can be used to determine the heat demand and the return temperature of the connected buildings without having to describe the heat transport in the buildings in detail and individually.