Evolutionary Nonlinear Model Output Statistics for Wind Speed Prediction using Genetic Programming

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

@InProceedings{Seo:2015:ECTA,
  author =       "Kisung Seo and Byeongyong Hyeon",
  title =        "Evolutionary Nonlinear Model Output Statistics for
                 Wind Speed Prediction using Genetic Programming",
  booktitle =    "Proceedings of the 7th International Joint Conference
                 on Computational Intelligence, ECTA 2015",
  year =         "2015",
  editor =       "Agostinho Rosa and Juan Julian Merelo and 
                 Antonio Dourado and Jose M. Cadenas and Kurosh Madani and 
                 Antonio Ruano and Joaquim Filipe",
  pages =        "287--292",
  address =      "Lisbon, Portugal",
  month =        "12-14 " # nov,
  organisation = "INSTICC - Institute for Systems and Technologies of
                 Information, Control and Communication, IFAC -
                 International Federation of Automatic Control, IEEE SMC
                 - IEEE Systems, Man and Cybernetics Society",
  publisher =    "SCITEPRESS - Science and Technology Publications",
  keywords =     "genetic algorithms, genetic programming, Wind Speed
                 Prediction, Nonlinear MOS",
  isbn13 =       "978-9-8975-8165-6",
  URL =          "http://ieeexplore.ieee.org/document/7529335/",
  size =         "6 pages",
  abstract =     "Wind speed fluctuates heavily and affects a smaller
                 locality than other weather elements. Wind speed is
                 heavily fluctuated and quite local than other weather
                 elements. It is difficult to improve the accuracy of
                 prediction only in a numerical prediction model. An MOS
                 (Model Output Statistics) technique is used to correct
                 the systematic errors of the model using a statistical
                 data analysis. Most previous MOS (Model Output
                 Statistics) used a linear regression model, but they
                 are hard to solve nonlinear natures of the weather
                 prediction. In order to solve the problem of a linear
                 MOS, a nonlinear compensation technique based on
                 evolutionary computation is introduced as a new
                 attempt. We suggest a nonlinear regression method using
                 GP (Genetic Programming) based symbolic regression to
                 generate an open-ended nonlinear MOS. The new nonlinear
                 MOS can express not only nonlinearity much more
                 extensively by involving all mathematical functions,
                 including transcendental functions, but also unlimited
                 orders with a dynamic selection of predictors due to
                 the flexible tree structure of GP. We evaluate the
                 accuracy of the estimation by GP based nonlinear MOS
                 for the three days wind speed prediction for Korean
                 regions. The training period of 2007- 2009, 2011 year
                 is used, the data of 2012 year is for verification, and
                 2013 year is adopted for test. This method is then
                 compared to the linear MOS and shows superior
                 results.",
  notes =        "

                 Contact: Ana Margarida
                 Guerreiro

                 aguerreiro@insticc.org

                 IJCCI Also known as \cite{7529335}",
}

Genetic Programming entries for Kisung Seo Byeongyong Hyeon

Citations