EnergyML

Energy simulation of the building stock

As part of the research project "Densification in the Context of Climate Change", we developed a method for real-time simulation of energy demands using a machine learning approach (EnergyML).

Method and functionality
This method uses an energy model trained by parametric simulation data, to predict heating and cooling energy demands. The application is also possible for inexperienced users via a Google Colab. More information can be found in the final report of the project, which will be published soon.Link to the final report (publication in 2023):
https://www.zsk.tum.de/zsk/veroeffentlichungen/

The files required for the application are available at the following link:
https://syncandshare.lrz.de/getlink/fiSeQuLLgadqKceXUnvB1Y/

Further information on the research project:
https://www.cee.ed.tum.de/enpb/forschung/laufende-forschungsprojekte/nachverdichtung-im-kontext-des-klimawandels/

For detailed information please contact:
Roland Reitberger
roland.reitberger@tum.de

Farzan Banihashemi 
farzan.banihashemi@tum.de