A Hybrid Feature Selection and Generation Algorithm for Electricity Load Prediction Using Grammatical Evolution

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@InProceedings{conf/icmla/SilvaNDL13,
  author =       "Anthony Mihirana {De Silva} and Farzad Noorian and 
                 Richard I. A. Davis and Philip H. W. Leong",
  title =        "A Hybrid Feature Selection and Generation Algorithm
                 for Electricity Load Prediction Using Grammatical
                 Evolution",
  publisher =    "IEEE",
  year =         "2013",
  volume =       "2",
  pages =        "211--217",
  address =      "Miami, FL, USA",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  bibdate =      "2014-04-21",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/icmla/icmla2013-2.html#SilvaNDL13",
  booktitle =    "ICMLA (2)",
  isbn13 =       "978-0-7695-5144-9",
  URL =          "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6784147",
  DOI =          "doi:10.1109/ICMLA.2013.125",
  size =         "7 pages",
  abstract =     "Accurate load prediction plays a major role in
                 devising effective power system control strategies.
                 Successful prediction systems often use machine
                 learning (ML) methods. The success of ML methods, among
                 other things, depends on a suitable choice of input
                 features which are usually selected by domain-experts.
                 In this paper, we propose a novel systematic way of
                 generating and selecting better features for daily peak
                 electricity load prediction using kernel methods.
                 Grammatical evolution is used to evolve an initial
                 population of well performing individuals, which are
                 subsequently mapped to feature subsets derived from
                 wavelets and technical indicator type formulae used in
                 finance. It is shown that the generated features can
                 improve results, while requiring no domain-specific
                 knowledge. The proposed method is focused on feature
                 generation and can be applied to a wide range of ML
                 architectures and applications.",
}

Genetic Programming entries for Anthony Mihirana De Silva Farzad Noorian Richard I A Davis Philip H W Leong

Citations