Electrical load forecasting using fast learning recurrent neural networks

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

@InProceedings{Khan:2013:IJCNN,
  author =       "Gul Muhammad Khan and Atif Rashid Khattak and 
                 Faheem Zafari and Sahibzada Ali Mahmud",
  title =        "Electrical load forecasting using fast learning
                 recurrent neural networks",
  booktitle =    "International Joint Conference on Neural Networks
                 (IJCNN 2013)",
  year =         "2013",
  month =        "4-9 " # aug,
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming, Load Forecasting, Neural Networks,
                 Neuro-evolution, Recurrent Neural Networks, Time Series
                 Prediction",
  DOI =          "doi:10.1109/IJCNN.2013.6706998",
  ISSN =         "2161-4393",
  abstract =     "A new recurrent neural network model which has the
                 ability to learn quickly is explored to devise a load
                 forecasting and management model for the highly
                 fluctuating load of London. Load forecasting plays an
                 significant role in determining the future load
                 requirements as well as the growth in the electricity
                 demand, which is essential for the proper development
                 of electricity infrastructure. The newly developed
                 neuroevolutionary technique called Recurrent Cartesian
                 Genetic Programming evolved Artificial Neural Networks
                 (RCGPANN) has been used to develop a peak load
                 forecasting model that can predict load patterns for a
                 complete year as well as for various seasons in
                 advance. The performance of the model is evaluated
                 using the load patterns of London for a period of four
                 years. The experimental results demonstrate the
                 superiority of the proposed model to the contemporary
                 methods presented to date.",
  notes =        "Also known as \cite{6706998}",
}

Genetic Programming entries for Gul Muhammad Khan Atif Rashid Khattak Faheem Zafari Sahibzada Ali Mahmud

Citations