Paper Conference

Proceedings of Building Simulation 2021: 17th Conference of IBPSA


Collecting data for urban building energy modelling by remote sensing and machine learning

Philip Gorzalka 1, Oana M. Garbasevschi 2,5, Jacob Estevam Schmiedt 3, Ariane Droin 2, Magdalena Linkiewicz 4, Michael Wurm, Bernhard Hoffschmidt
1 Institute of Solar Research at German Aerospace Center (DLR), J├╝lich, Germany
2 German Remote Sensing Data Center (DFD) at German Aerospace Center (DLR), Oberpfaffenhofen, Germany
3 Institute for the Protection of Terrestrial Infrastructures at German Aerospace Center (DLR), Sankt Augustin, Germany
4 Institute of Optical Sensor Systems at German Aerospace Center (DLR), Berlin-Adlershof, Germany
5 ifo Institute for Economic Research, Munich, Germany

Abstract: High-quality data on the investigated area is crucial for modelling urban building energy demands, but its availability is often insufficient. We present an approach to acquire (i) building geometries, (ii) their ages, and (iii) their retrofit states. It consists of creating a 3D model from aerial imagery, determining building ages through machine learning, generating a simulation model based on open-source tools, and assessing retrofit states by comparing simulated temperatures with infrared thermography (IRT) measurements. The demonstration on a case study quarter in Berlin shows that heat demand results are comparable to other tools. Using machine learning is already wellsuited to close knowledge gaps regarding building ages. However, retrofit state assessment using IRT was unsatisfactory due to insufficient measurement accuracy and is envisaged for improvement in future research, along with a validation of the approach.
Keywords: urban building energy modelling, aerial infrared thermography, photogrammetry, machine learning, energy retrofit
Pages: 1139 - 1146