% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Pasella:1031814,
      author       = {Pasella, Manuela and Benigni, Andrea and Cannas, Barbara
                      and Carta, Daniele and Muscas, Carlo and Pisano, Fabio},
      title        = {{O}n the {Q}uality of {P}seudo-{M}easurements for
                      {D}istribution {S}ystem {S}tate {E}stimation},
      publisher    = {IEEE},
      reportid     = {FZJ-2024-05833},
      pages        = {1-6},
      year         = {2024},
      abstract     = {With the increasing penetration of distributed en-ergy
                      resources, Smart Grids control applications are always more
                      dependent on monitoring data. Pseudo-measurements are used
                      in Distribution System State Estimation to allow estimating
                      the operating conditions of the system also when the number
                      of field measurements is limited. Since the accuracy of the
                      estimation depends on the quality of the
                      pseudo-measurements, in this paper the factors that affect
                      this quality are investigated and the performance of a
                      machine learning-based approach for pseudo-measurements
                      generation is evaluated. Starting from real data collected
                      from the Forschungszentrum Jülich campus, a dataset is
                      engineered, and the considered data coding approach is
                      presented. Finally, different neural models based on
                      multilayer perceptron are presented, and their performances
                      are compared with those of trivial alternatives.},
      month         = {Sep},
      date          = {2024-09-18},
      organization  = {2024 IEEE 14th International Workshop
                       on Applied Measurements for Power
                       Systems (AMPS), Caserta (Italy), 18 Sep
                       2024 - 20 Sep 2024},
      cin          = {ICE-1},
      cid          = {I:(DE-Juel1)ICE-1-20170217},
      pnm          = {1122 - Design, Operation and Digitalization of the Future
                      Energy Grids (POF4-112) / 1123 - Smart Areas and Research
                      Platforms (POF4-112)},
      pid          = {G:(DE-HGF)POF4-1122 / G:(DE-HGF)POF4-1123},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:001344552300003},
      doi          = {10.1109/AMPS62611.2024.10706662},
      url          = {https://juser.fz-juelich.de/record/1031814},
}