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@ARTICLE{Hoffmann:866628,
      author       = {Hoffmann, Maximilian and Kotzur, Leander and Stolten,
                      Detlef and Robinius, Martin},
      title        = {{A} {R}eview on {T}ime {S}eries {A}ggregation {M}ethods for
                      {E}nergy {S}ystem {M}odels},
      journal      = {Energies},
      volume       = {13},
      number       = {3},
      issn         = {1996-1073},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2019-05707},
      pages        = {641},
      year         = {2020},
      abstract     = {Due to the high degree of intermittency of renewable energy
                      sources (RES) and the growing interdependences amongst
                      formerly separated energy pathways, the modeling of adequate
                      energy systems is crucial to evaluate existing energy
                      systems and to forecast viable future ones. However, this
                      corresponds to the rising complexity of energy system models
                      (ESMs) and often results in computationally intractable
                      programs. To overcome this problem, time series aggregation
                      (TSA) is frequently used to reduce ESM complexity. As these
                      methods aim at the reduction of input data and preserving
                      the main information about the time series, but are not
                      based on mathematically equivalent transformations, the
                      performance of each method depends on the justifiability of
                      its assumptions. This review systematically categorizes the
                      TSA methods applied in 130 different publications to
                      highlight the underlying assumptions and to evaluate the
                      impact of these on the respective case studies. Moreover,
                      the review analyzes current trends in TSA and formulates
                      subjects for future research. This analysis reveals that the
                      future of TSA is clearly feature-based including clustering
                      and other machine learning techniques which are capable of
                      dealing with the growing amount of input data for ESMs.
                      Further, a growing number of publications focus on bounding
                      the TSA induced error of the ESM optimization result. Thus,
                      this study can be used as both an introduction to the topic
                      and for revealing remaining research gaps},
      cin          = {IEK-3},
      ddc          = {620},
      cid          = {I:(DE-Juel1)IEK-3-20101013},
      pnm          = {134 - Electrolysis and Hydrogen (POF3-134) / PhD no Grant -
                      Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF3-134 / G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000522489000134},
      doi          = {10.3390/en13030641},
      url          = {https://juser.fz-juelich.de/record/866628},
}