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@ARTICLE{Kmper:904187,
      author       = {Kämper, Andreas and Holtwerth, Alexander and Leenders,
                      Ludger and Bardow, André},
      title        = {{A}uto{M}o{G} 3{D}: {A}utomated {D}ata-{D}riven {M}odel
                      {G}eneration of {M}ulti-{E}nergy {S}ystems {U}sing {H}inging
                      {H}yperplanes},
      journal      = {Frontiers in energy research},
      volume       = {9},
      issn         = {2296-598X},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {FZJ-2021-05757},
      pages        = {719658},
      year         = {2021},
      abstract     = {The optimal operation of multi-energy systems requires
                      optimization models that are accurate and computationally
                      efficient. In practice, models are mostly generated
                      manually. However, manual model generation is
                      time-consuming, and model quality depends on the expertise
                      of the modeler. Thus, reliable and automated model
                      generation is highly desirable. Automated data-driven model
                      generation seems promising due to the increasing
                      availability of measurement data from cheap sensors and data
                      storage. Here, we propose the method AutoMoG 3D (Automated
                      Model Generation) to decrease the effort for data-driven
                      generation of computationally efficient models while
                      retaining high model quality. AutoMoG 3D automatically
                      yields Mixed-Integer Linear Programming models of
                      multi-energy systems enabling efficient operational
                      optimization to global optimality using established solvers.
                      For each component, AutoMoG 3D performs a piecewise-affine
                      regression using hinging-hyperplane trees. Thereby,
                      components can be modeled with an arbitrary number of
                      independent variables. AutoMoG 3D iteratively increases
                      the number of affine regions. Thereby, AutoMoG 3D balances
                      the errors caused by each component in the overall model of
                      the multi-energy system. AutoMoG 3D is applied to model a
                      real-world pump system. Here, AutoMoG 3D drastically
                      decreases the effort for data-driven model generation and
                      provides an accurate and computationally efficient
                      optimization model.},
      cin          = {IEK-10},
      ddc          = {333.7},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000685032500001},
      doi          = {10.3389/fenrg.2021.719658},
      url          = {https://juser.fz-juelich.de/record/904187},
}