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@ARTICLE{Ferdowsi:866112,
      author       = {Ferdowsi, Mohsen and Benigni, Andrea and Monti, Antonello
                      and Ponci, Ferdinanda},
      title        = {{M}easurement {S}election for {D}ata-{D}riven {M}onitoring
                      of {D}istribution {S}ystems},
      journal      = {IEEE systems journal},
      volume       = {13},
      number       = {4},
      issn         = {1932-8184},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2019-05331},
      pages        = {4260 - 4268},
      year         = {2019},
      abstract     = {This article investigates the problem of measurement
                      selection for data-driven monitoring approaches. Several
                      approaches to input variable selection (IVS) are analyzed,
                      and a general procedure for finding the optimal order for
                      the selection of candidate measurements is presented. The
                      method is based on the extensions of partial correlation and
                      minimal redundancy maximum relevance criteria to support IVS
                      problems involving multiple outputs. This method can be used
                      to find the minimal set of measurements for achieving a
                      target estimation accuracy. The results demonstrate the
                      advantages and limits of the introduced method in comparison
                      to the other approaches discussed in this article.},
      cin          = {IEK-10},
      ddc          = {004},
      cid          = {I:(DE-Juel1)IEK-10-20170217},
      pnm          = {899 - ohne Topic (POF3-899)},
      pid          = {G:(DE-HGF)POF3-899},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000503182300064},
      doi          = {10.1109/JSYST.2019.2939500},
      url          = {https://juser.fz-juelich.de/record/866112},
}