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@PHDTHESIS{Lauster:749705,
      author       = {Lauster, Moritz Robert},
      othercontributors = {Müller, Dirk and Nytsch-Geusen, Christoph},
      title        = {{P}arametrierbare {G}ebäudemodelle für dynamische
                      {E}nergiebedarfsrechnungen von {S}tadtquartieren; 1st ed.},
      volume       = {60},
      school       = {RWTH Aachen University},
      type         = {Dissertation},
      address      = {Aachen},
      publisher    = {E.ON Energy Research Center, RWTH Aachen University},
      reportid     = {RWTH-2018-230258},
      isbn         = {978-3-942789-59-2},
      series       = {E.ON Energy Research Center : EBC, Energy efficient
                      buildings and indoor climate},
      pages        = {1 Online-Ressource (xxv, 158 Seiten) : Illustrationen},
      year         = {2018},
      note         = {Auch veröffentlicht auf dem Publikationsserver der RWTH
                      Aachen University; Dissertation, RWTH Aachen University,
                      2018},
      abstract     = {This thesis presents a methodology and a software framework
                      for dynamic modeling and heat demand calculations of
                      building stocks using basic input data. Such heat demands
                      build the foundation for the investigation of innovative
                      efficiency measures, such as advanced control concepts and
                      optimal design of heat supply systems. The methodology
                      includes simplified dynamic building performance models that
                      fulfil the requirements regarding computational effort and
                      model complexity on urban scale. An accompanying model
                      parameterization routine on the basis of accessible input
                      data allows the efficient simulation of entire building
                      stocks. Both parts, parameterization and modelling, are
                      embedded in a workflow automation process and implemented
                      using the programming language Python and the modelling
                      language Modelica, respectively. The parameterization makes
                      use of archetypes for residential buildings, offices and
                      institute buildings, which allows for statistical enrichment
                      of individual datasets for the given building types. The
                      archetype for institute buildings is developed based on
                      statistical analyses of two research centers. The simplified
                      building performance models are based on a reduced thermal
                      network model, which is described in the German Guideline
                      VDI 6007-1 (2015). As part of a characterization of
                      different model types, the use of a second order model
                      including two state variables proves to be the best choice
                      concerning efficiency and complexity. This model uses of one
                      state variable each for interior and exterior building
                      elements in addition to one separate resistance for
                      windows.The investigations of three use cases show the
                      framework’s ability to simulate entire building stocks
                      using only basic input data. The use case of one of the
                      studied research centers results in differences between
                      simulation and measurement of less than $3\%$ in the annual
                      heat demand of all buildings. The simulation can in addition
                      capture the dynamic behavior of the heat demand, what is
                      highlighted by a coefficient of determination of 0,894. All
                      parts of the methodology comply with the requirements
                      regarding verification, accuracy, transparency, stability
                      and flexibility. They can contribute to the field of urban
                      energy modelling, if the individual application allows the
                      use of archetypes and reduced order models. The
                      parameterization is implemented in the Python library
                      TEASER, the reduced order building models are integrated
                      into the Modelica library AixLib. Both libraries are
                      available open source at https://github.com/RWTH-EBC/TEASER
                      and https://github.com/RWTH-EBC/AixLib.},
      cin          = {419510 / 080052},
      ddc          = {620},
      cid          = {$I:(DE-82)419510_20140620$ / $I:(DE-82)080052_20160101$},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      doi          = {10.18154/RWTH-2018-230258},
      url          = {https://publications.rwth-aachen.de/record/749705},
}