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@ARTICLE{Khnen:877680,
      author       = {Köhnen, Clara Sophie and Priesmann, Jan and Nolting, Lars
                      and Kotzur, Leander and Robinius, Martin and Praktiknjo,
                      Aaron},
      title        = {{T}he potential of deep learning to reduce complexity in
                      energy system modeling},
      journal      = {International journal of energy research},
      volume       = {46},
      number       = {4},
      issn         = {0363-907X},
      address      = {London [u.a.]},
      publisher    = {Wiley-Intersience},
      reportid     = {FZJ-2020-02390},
      pages        = {4550-4571},
      year         = {2022},
      abstract     = {In order to cope with increasing complexity in energy
                      systems due to rapid changes and uncertain future
                      developments, the evaluation of multiple scenarios is
                      essential for sound scientific system analyses. Hence,
                      efficient modeling approaches and complexity reductions are
                      urgently required. However, there is a lack of scientific
                      analyses going beyond the scope of traditional energy system
                      modeling. For this reason, we investigate the potential of
                      metamodels to reduce the complexity of energy system
                      modeling. In our explorative study, we investigate their
                      potential and limits for applications in the fields of
                      electricity dispatch and design optimization for heating
                      systems. We first select a suitable metamodeling approach by
                      conducting pre-tests on a small scale. Based on this, we
                      selected artificial neural networks due to their good
                      performance compared to other approaches and the multiple
                      possibilities of network topologies and hyperparameter
                      settings. As for the dispatch model, we show that a high
                      accuracy of price replication can be achieved while
                      substantially reducing the runtimes per investigated
                      scenario (from 2 hours on average down to less than
                      30 seconds). With the design optimization model, we find
                      double-edged results: while we also achieve a substantial
                      reduction of runtime in this case (from ~0.8 hours to less
                      than 30 seconds), the simultaneous forecasting of several
                      interdependent variables proved to be problematic and the
                      accuracy of the metamodel shows to be insufficient in many
                      cases. Overall, we demonstrate that metamodeling is a
                      suitable approach to complemement traditional energy system
                      modeling rather than to replace them: the loss of
                      traceability in (black-box) metamodels indicates the
                      importance of hybrid solutions that combine fundamental
                      models with metamodels.},
      cin          = {IEK-3},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IEK-3-20101013},
      pnm          = {134 - Electrolysis and Hydrogen (POF3-134) / 1111 -
                      Effective System Transformation Pathways (POF4-111) / 1112 -
                      Societally Feasible Transformation Pathways (POF4-111)},
      pid          = {G:(DE-HGF)POF3-134 / G:(DE-HGF)POF4-1111 /
                      G:(DE-HGF)POF4-1112},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000721030000001},
      doi          = {10.1002/er.7448},
      url          = {https://juser.fz-juelich.de/record/877680},
}