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@ARTICLE{Hilger:1021646,
      author       = {Hilger, Hannes and Witthaut, Dirk and Dahmen, Manuel and
                      Gorjao, Leonardo Rydin and Trebbien, Julius and Cramer,
                      Eike},
      title        = {{M}ultivariate {S}cenario {G}eneration of {D}ay-{A}head
                      {E}lectricity {P}rices using {N}ormalizing {F}lows},
      publisher    = {arXiv},
      reportid     = {FZJ-2024-00902},
      year         = {2023},
      abstract     = {Trading on electricity markets requires accurate
                      information about the realization of electricity prices and
                      the uncertainty attached to the predictions. We present a
                      probabilistic forecasting approach for day-ahead electricity
                      prices using the fully data-driven deep generative model
                      called normalizing flows. Our modeling approach generates
                      full-day scenarios of day-ahead electricity prices based on
                      conditional features such as residual load forecasts.
                      Furthermore, we propose extended feature sets of prior
                      realizations and a periodic retraining scheme that allows
                      the normalizing flow to adapt to the changing conditions of
                      modern electricity markets. In particular, we investigate
                      the impact of the energy crisis ensuing from the Russian
                      invasion of Ukraine. Our results highlight that the
                      normalizing flow generates high-quality scenarios that
                      reproduce the true price distribution and yield highly
                      accurate forecasts. Additionally, our analysis highlights
                      how our improvements towards adaptations in changing regimes
                      allow the normalizing flow to adapt to changing market
                      conditions and enables continued sampling of high-quality
                      day-ahead price scenarios.},
      keywords     = {Machine Learning (cs.LG) (Other) / FOS: Computer and
                      information sciences (Other)},
      cin          = {IEK-10},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {1121 - Digitalization and Systems Technology for
                      Flexibility Solutions (POF4-112)},
      pid          = {G:(DE-HGF)POF4-1121},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/ARXIV.2311.14033},
      url          = {https://juser.fz-juelich.de/record/1021646},
}