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@INPROCEEDINGS{Ptz:1010204,
      author       = {Pütz, Sebastian and Kruse, Johannes and Witthaut, Dirk and
                      Hagenmeyer, Veit and Schäfer, Benjamin},
      title        = {{R}egulatory {C}hanges in {G}erman and {A}ustrian {P}ower
                      {S}ystems {E}xplored with {E}xplainable {A}rtificial
                      {I}ntelligence},
      publisher    = {ACM New York, NY, USA},
      reportid     = {FZJ-2023-03013},
      pages        = {26-31},
      year         = {2023},
      comment      = {Companion Proceedings of the 14th ACM International
                      Conference on Future Energy Systems - ACM New York, NY, USA,
                      2023. - ISBN 9798400702273 - doi:10.1145/3599733.3600247},
      booktitle     = {Companion Proceedings of the 14th ACM
                       International Conference on Future
                       Energy Systems - ACM New York, NY, USA,
                       2023. - ISBN 9798400702273 -
                       doi:10.1145/3599733.3600247},
      abstract     = {A stable supply of electrical energy is essential for the
                      functioning of our society. Therefore, energy and balancing
                      markets of power grids are strictly regulated. With changes
                      in technology, the economy and society, these regulations
                      are also constantly adapted. However, whether these
                      regulatory changes lead to the intended results is not easy
                      to assess. Could eXplainable Artificial Intelligence (XAI)
                      models distinguish regulatory settings and support the
                      understanding of the effects of these changes? In this
                      article, we explore two examples of regulatory changes: The
                      splitting of the German-Austrian bidding zone and changes in
                      the pricing schemes of the German balancing energy market.
                      We find that boosted tree models and feedforward neural
                      networks before and after a regulatory change differ in
                      their respective parametrizations. Using Shapley additive
                      explanations, we reveal model differences, e.g., in terms of
                      feature importance, and identify key features of these
                      distinct models. With this study, we demonstrate how XAI can
                      be applied to investigate system changes in power systems.},
      month         = {Jun},
      date          = {2023-06-20},
      organization  = {e-Energy '23: The 14th ACM
                       International Conference on Future
                       Energy Systems, Orlando FL (USA), 20
                       Jun 2023 - 23 Jun 2023},
      cin          = {IEK-10},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {1122 - Design, Operation and Digitalization of the Future
                      Energy Grids (POF4-112) / HDS LEE - Helmholtz School for
                      Data Science in Life, Earth and Energy (HDS LEE)
                      (HDS-LEE-20190612)},
      pid          = {G:(DE-HGF)POF4-1122 / G:(DE-Juel1)HDS-LEE-20190612},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      UT           = {WOS:001058264100004},
      doi          = {10.1145/3599733.3600247},
      url          = {https://juser.fz-juelich.de/record/1010204},
}