• DocumentCode
    2616834
  • Title

    Clustering bus load curves

  • Author

    Salgado, Ricardo Menezes ; Ohishi, Takaaki ; Ballini, Rosangela

  • Author_Institution
    UNICAMP, Campinas, Brazil
  • fYear
    2004
  • fDate
    10-13 Oct. 2004
  • Firstpage
    1251
  • Abstract
    This paper proposes the clustering of a set of busses through a fuzzy c-means clustering approach. The utilization of fuzzy techniques in a clustering problem aims the attainment of a partition fuzzy in the data set, allowing degrees of relationship between different elements of the set, this way, an element can belong to more than one group with different membership value. In this paper the clustering algorithm aims at exploring data characteristics and determining groups composed by busses with similar bus daily load. Results show the efficiency of the clustering method, where the data was classified into distinct groups such as: commercial, residential and industrial consumption profiles.
  • Keywords
    busbars; fuzzy reasoning; bus load curves; data set; fuzzy c-means clustering; industrial consumption profiles; Autoregressive processes; Clustering algorithms; Clustering methods; Data analysis; Data mining; Databases; Fuzzy sets; Neural networks; Partitioning algorithms; Signal processing algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power Systems Conference and Exposition, 2004. IEEE PES
  • Print_ISBN
    0-7803-8718-X
  • Type

    conf

  • DOI
    10.1109/PSCE.2004.1397652
  • Filename
    1397652