@inproceedings{GorzalkaDahlkeGoettscheetal.2018,
  author    = {Gorzalka, Philip and Dahlke, Dennis and G{\"o}ttsche, Joachim and Israel, Martin and Patel, Dhruvkumar and Prahl, Christoph and Schmiedt, Jacob Estevam and Frommholz, Dirk and Hoffschmidt, Bernhard and Linkiewicz, Magdalena},
  title     = {Building Tomograph-From Remote Sensing Data of Existing Buildings to Building Energy Simulation Input},
  series = {EBC, Annex 71, Fifth expert meeting, October 17-19, 2018, Innsbruck, Austria},
  booktitle = {EBC, Annex 71, Fifth expert meeting, October 17-19, 2018, Innsbruck, Austria},
  pages     = {17 Seiten},
  year      = {2018},
  language  = {en}
}
@article{GorzalkaSchmiedtSchorn2021,
  author    = {Gorzalka, Philip and Schmiedt, Jacob Estevam and Schorn, Christian},
  title     = {Automated Generation of an Energy Simulation Model for an Existing Building from UAV Imagery},
  series = {Buildings},
  volume    = {11},
  journal   = {Buildings},
  number    = {9},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2075-5309},
  doi       = {10.3390/buildings11090380},
  pages     = {15 Seiten},
  year      = {2021},
  abstract  = {An approach to automatically generate a dynamic energy simulation model in Modelica for a single existing building is presented. It aims at collecting data about the status quo in the preparation of energy retrofits with low effort and costs. The proposed method starts from a polygon model of the outer building envelope obtained from photogrammetrically generated point clouds. The open-source tools TEASER and AixLib are used for data enrichment and model generation. A case study was conducted on a single-family house. The resulting model can accurately reproduce the internal air temperatures during synthetical heating up and cooling down. Modelled and measured whole building heat transfer coefficients (HTC) agree within a 12\% range. A sensitivity analysis emphasises the importance of accurate window characterisations and justifies the use of a very simplified interior geometry. Uncertainties arising from the use of archetype U-values are estimated by comparing different typologies, with best- and worst-case estimates showing differences in pre-retrofit heat demand of about ±20\% to the average; however, as the assumptions made are permitted by some national standards, the method is already close to practical applicability and opens up a path to quickly estimate possible financial and energy savings after refurbishment.},
  language  = {en}
}