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

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@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 Heng Wai Leong

Citations