Electrical load forecasting using fast learning recurrent neural networks

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@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