User-data-based optimization of buildings and facilities at the example of Munich University of Applied Sciences.

Project duration
01.04.2019 – 31.10.2022

Funding organization
The Federal Ministry for Economic Affairs and Climate Action (BMWK)

Project Sponsor
Project Sponsor Jülich, PtJ

Project partners
University of Applied Sciences Munich, Prof. Dr. –Ing. Werner Jensch (Department of Building Services Engineering)
Prof. Dr. –Ing. Simon Schramm (Department of Electrical Engineering and Information Technology)
Prof. Dr. Peter Mandl (Department of Computer Science and Mathematics)

Although the goal of climate-neutral building operation can be planned thanks to today’s technical possibilities, practical implementation is difficult due to different user-specific requirements and influences. Due to their structure and size, cities and districts usually appear to be too complex to adequately capture the structural and energetic challenges. In contrast, the building stock of a university is easier to understand due to its manageable size, networking and recording as a district. It’s heterogeneity in terms of existing buildings, used technologies and various uses make it possible to transfer the results achieved to other districts. 

Based on the predecessor projects HoEff (The University on its way to energy-efficient building operation) and HoEff-CIM (Energy-efficient university – Campus Information Modeling), which were carried out at the Ludwig Maximilian University in Munich (LMU), it is now intended to conduct research on the own building and apply findings at the Munich University of Applied Sciences. The building-stock will initially be recorded and classified energetically with the help of the findings, methods, and tools of the predecessor projects, including its types of use and users. 

The Institute of Energy Efficient and Sustainable Design and Building investigates the partial aspects of socio-economic modelling of user influences and sustainable reference plant concepts. The following questions are specifically investigated:

  • How can the total energy demand of complex building structures be automated, data-based and cost-efficiently analyzed, evaluated and reduced?
  • How can the necessary measuring effort be reduced by correlation with data from additionally available sources?
  • How can the user be consciously involved in the transformation to a climate-neutral campus or an energy-efficient building operation?
  • Which kind of information and data is needed, to map the user with sufficient accuracy?
  • How can user effects be adequately considered during planning or reliably detected in the operational diagnosis?
  • How can the energetic effects be quantified? Does more technology also guarantee a better solution?

The findings of the overall project are to be incorporated into the planning of new building projects and the energetic renovation or repair of the building-stock at the Munich University of Applied Sciences in cooperation with the university management and the Munich State Building Authority II.

Project team
Farzan Banihashemi, Sebastian Botzler, Daniel Kierdorf


  • Banihashemi, F.; Weber, M.; Deghim, F.; Zong, C.; Lang, W.: Occupancy modeling on non-intrusive indoor environmental data through machine learning. Building and Environment 254, 2024, 111382 more…
  • Banihashemi, F.; Weber, M.; Lang, W.: Deep learning for predictive window operation modeling in open-plan offices. Energy and Buildings 310, 2024, 114109 more…


  • Deghim, F.; Banihashemi, F.; Koth, S.; Lang, W.: A data-driven approach for predicting occupant thermal comfort in offices. Proceedings of 33. Forum Bauinformatik, 2022 more…
  • Zong, C.; Banihashemi, F.; Vollmer, M.; Lang, W.: Implementation of occupant behaviour models for window control using co-simulation approach. BauSIM 2022, 2022 more…


  • Banihashemi, F.; Weber, M.; Lang, W.: Model order reduction of building energy simulation models using a convolutional neural network autoencoder. Building and Environment, 2021, 108498 more…


  • Ehlers, N.; Kierdorf, D.; Banihashemi, F.; Lang, W: Energy supply optimization of the Munich University of Applied Sciences through parametric studies in thermal building simulation. 10 years of MSE: Energy Research in Bavaria, 2020 more…
  • Pinter, S.; Kierdorf, D.; Vollmer, M.; Banihashemi, F.; Harter, H.; Lang, W.: Measuring Box for Indoor Climate and Thermal Comfort. 10 years of MSE: Energy Research in Bavaria, 2020 more…