Short-term load forecasting of power systems by gene expression programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{journals/nca/HosseiniG12,
  author =       "Seyyed Soheil {Sadat Hosseini} and 
                 Amir Hossein Gandomi",
  title =        "Short-term load forecasting of power systems by gene
                 expression programming",
  journal =      "Neural Computing and Applications",
  year =         "2012",
  number =       "2",
  volume =       "21",
  pages =        "377--389",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  ISSN =         "0941-0643",
  DOI =          "doi:10.1007/s00521-010-0444-y",
  size =         "Special Issue on Theory and applications of swarm
                 intelligence",
  abstract =     "Short-term load forecasting is a popular topic in the
                 electric power industry due to its essentiality in
                 energy system planning and operation. Load forecasting
                 is important in deregulated power systems since an
                 improvement of a few percentages in the prediction
                 accuracy will bring benefits worth of millions of
                 dollars. In this study, a promising variant of genetic
                 programming, namely gene expression programming (GEP),
                 is used to improve the accuracy and enhance the
                 robustness of load forecasting results. With the use of
                 the GEP technique, accurate relationships were obtained
                 to correlate the peak and total loads to average,
                 maximum and lowest temperatures of day. The presented
                 model is applied to forecast short-term load using the
                 actual data from a North American electric utility. A
                 multiple least squares regression analysis was
                 performed using the same variables and same data sets
                 to benchmark the GEP models. For more verification, a
                 subsequent parametric study was also carried out. The
                 observed agreement between the predicted and measured
                 peak and total load values indicates that the proposed
                 correlations are capable of effectively forecasting the
                 short-term load. The GEP-based formulae are relatively
                 short, simple and particularly valuable for providing
                 an analysis tool accessible to practising engineers.",
  affiliation =  "Tafresh University, Tafresh, Iran",
  bibdate =      "2012-02-24",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/nca/nca21.html#HosseiniG12",
}

Genetic Programming entries for S S Sadat Hosseini A H Gandomi

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