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@ARTICLE{Aghsaee:1024916,
      author       = {Aghsaee, Roya and Hecht, Christopher and Schwinger, Felix
                      and Figgener, Jan and Jarke, Matthias and Sauer, Dirk Uwe},
      title        = {{D}ata-{D}riven, {S}hort-{T}erm {P}rediction of {C}harging
                      {S}tation {O}ccupation},
      journal      = {Electricity},
      volume       = {4},
      number       = {2},
      issn         = {2673-4826},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2024-02566},
      pages        = {134 - 153},
      year         = {2023},
      abstract     = {Enhancing electric vehicle infrastructure by forecasting
                      the availability of charging stations can boost the
                      attractiveness of electric vehicles. The transportation
                      sector plays a crucial role in battling climate change. The
                      majority of available prediction algorithms either achieve
                      poor accuracy or predict the availability at certain points
                      in time in the future. Both of these situations are not
                      ideal and may potentially hinder the model’s applicability
                      to real-world situations. This paper provides a new model
                      for estimating the charging duration of charging events in
                      real time, which may be used to estimate the waiting time of
                      users at fully occupied charging stations. First, the
                      prediction is made using the random forest regressor (RF),
                      and then the prediction is enhanced utilizing the findings
                      of the RF model and real-time information of the currently
                      occurring charging events. We compare the proposed method
                      with the RF model, which is the approach’s foundational
                      model, and the best-performing prediction model of the light
                      gradient boosting machine (LightGBM). Here, we make use of
                      historical information of charging events gathered from 2079
                      charging stations across Germany’s 4602 fast-charging
                      connectors. To reduce data bias, we specifically simulate
                      prediction requests for $30\%$ of the charging events with
                      various characteristics that were not trained with the
                      model. Overall, the suggested method performs better than
                      both the RF and the LightGBM. In addition, the model’s
                      structure is adaptable and can incorporate real-time
                      information on charging events.},
      cin          = {IEK-12},
      ddc          = {621.3},
      cid          = {I:(DE-Juel1)IEK-12-20141217},
      pnm          = {1223 - Batteries in Application (POF4-122)},
      pid          = {G:(DE-HGF)POF4-1223},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001187490200001},
      doi          = {10.3390/electricity4020009},
      url          = {https://juser.fz-juelich.de/record/1024916},
}