Load Identification of Non-intrusive Load-monitoring System in Smart Home

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Chang:2010:WSEAS,
  author =       "Hsueh-Hsien Chang",
  title =        "Load Identification of Non-intrusive Load-monitoring
                 System in Smart Home",
  journal =      "WSEAS Transactions on Systems",
  year =         "2010",
  volume =       "9",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, load
                 identification, artificial neural networks,
                 non-intrusive load monitoring, turn-on transient energy
                 analysis, smart home",
  ISSN =         "1109-2777",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.455.3952",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.455.3952",
  URL =          "http://www.worldses.org/journals/systems/systems-2010.htm",
  URL =          "http://www.wseas.us/e-library/transactions/systems/2010/42-415.pdf",
  abstract =     "In response to the governmental policy of saving
                 energy sources and reducing CO2, and carry out the
                 resident quality of local; this paper proposes a new
                 method for a non-intrusive load-monitoring (NILM)
                 system in smart home to implement the load
                 identification of electric equipments and establish the
                 electric demand management. Non-intrusive
                 load-monitoring techniques were often based on power
                 signatures in the past, these techniques are necessary
                 to be improved for the results of reliability and
                 accuracy of recognition. By using neural network (NN)
                 in combination with genetic programming (GP) and
                 turn-on transient energy analysis, this study attempts
                 to identify load demands and improve recognition
                 accuracy of non-intrusive load-monitoring results. The
                 turn-on transient energy signature can improve the
                 efficiency of load identification and computational
                 time under multiple operations.",
}

Genetic Programming entries for Hsueh-Hsien Chang

Citations