An Application of Genetic Programming for Power System Planning and Operation

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

@Article{Behera:2012:ACEEijcsi,
  author =       "R. Behera and B. B. Pati and B. P. Panigrahi and 
                 S. Misra",
  title =        "An Application of Genetic Programming for Power System
                 Planning and Operation",
  journal =      "ACEEE International Journal on Control System and
                 Instrumentation",
  year =         "2012",
  volume =       "3",
  number =       "2",
  pages =        "15--20",
  month =        mar,
  note =         "Special Issue",
  keywords =     "genetic algorithms, genetic programming Computer Aided
                 Engineering, Mutation, Fitness Function",
  ISSN =         "2158-0006",
  URL =          "http://searchdl.org/index.php/journals/journalList/1",
  searchdl =     "ID: 01.IJCSI.3.2.59",
  bibsource =    "OAI-PMH server at hal.archives-ouvertes.fr",
  language =     "ENG",
  oai =          "oai:hal.archives-ouvertes.fr:hal-00741655",
  broken =       "http://hal.archives-ouvertes.fr/hal-00741655",
  URL =          "http://hal.archives-ouvertes.fr/docs/00/74/16/55/PDF/59.pdf",
  size =         "6 pages",
  abstract =     "This work incorporates the identification of model in
                 functional form using curve fitting and genetic
                 programming technique which can forecast present and
                 future load requirement. Approximating an unknown
                 function with sample data is an important practical
                 problem. In order to forecast an unknown function using
                 a finite set of sample data, a function is constructed
                 to fit sample data points. This process is called curve
                 fitting. There are several methods of curve fitting.
                 Interpolation is a special case of curve fitting where
                 an exact fit of the existing data points is expected.
                 Once a model is generated, acceptability of the model
                 must be tested. There are several measures to test the
                 goodness of a model. Sum of absolute difference, mean
                 absolute error, mean absolute percentage error, sum of
                 squares due to error (SSE), mean squared error and root
                 mean squared errors can be used to evaluate models.
                 Minimising the squares of vertical distance of the
                 points in a curve (SSE) is one of the most widely used
                 method .Two of the methods has been presented namely
                 Curve fitting technique and Genetic Programming and
                 they have been compared based on (SSE)sum of squares
                 due to error.",
  notes =        "http://ijcsi.theaceee.org/",
}

Genetic Programming entries for R Behera Bibhuti Bhusan Pati Bibhu Prasad Panigrahi S Misra

Citations