A Linear Genetic Programming Approach for Modeling Electricity Demand Prediction in Victoria

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

@InProceedings{bhattacharya:2001:HIS,
  title =        "A Linear Genetic Programming Approach for Modeling
                 Electricity Demand Prediction in Victoria",
  author =       "Maumita Bhattacharya and Ajith Abraham and 
                 Baikunth Nath",
  editor =       "Ajith Abraham and Mario Koppen",
  booktitle =    "2001 International Workshop on Hybrid Intelligent
                 Systems",
  series =       "LNCS",
  pages =        "379--394",
  publisher =    "Springer-Verlag",
  address =      "Adelaide, Australia",
  publisher_address = "Berlin",
  month =        "11-12 " # dec,
  year =         "2001",
  email =        "maumita.bhattacharya@infotech.monash.edu.au,
                 ajith.abraham@infotech.monash.edu.au,
                 b.nath@infotech.monash.edu.au",
  keywords =     "genetic algorithms, genetic programming, Linear
                 genetic programming, neuro-fuzzy, neural networks,
                 forecasting, electricity demand",
  broken =       "http://www-mugc.cc.monash.edu.au/~abrahamp/172.pdf",
  URL =          "http://www.springer.de/cgi-bin/search_book.pl?isbn=3-7908-1480-6",
  URL =          "http://citeseer.ist.psu.edu/510872.html",
  ISBN =         "3-7908-1480-6",
  abstract =     "Genetic programming (GP), a relatively young and
                 growing branch of evolutionary computation is gradually
                 proving to be a promising method of modelling complex
                 prediction and classification problems. This paper
                 evaluates the suitability of a linear genetic
                 programming (LGP) technique to predict electricity
                 demand in the State of Victoria, Australia, while
                 comparing its performance with two other popular soft
                 computing techniques. The forecast accuracy is compared
                 with the actual energy demand. To evaluate, we
                 considered load demand patterns for ten consecutive
                 months taken every 30 minutes for training the
                 different prediction models. Test results show that
                 while the linear genetic programming method delivered
                 satisfactory results, the neuro fuzzy system performed
                 best for this particular application problem, in terms
                 of accuracy and computation time, as compared to LGP
                 and neural networks.",
  notes =        "HIS01

                 Possibly also of interest Applied Soft Computing Volume
                 1, Issue 2 , August 2001, Pages 127-138
                 doi:10.1016/S1568-4946(01)00013-8",
}

Genetic Programming entries for Maumita Bhattacharya Ajith Abraham Baikunth Nath

Citations