Very Short Term Load Forecasting Using Cartesian Genetic Programming Evolved Recurrent Neural Networks (CGPRNN)

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Khan:2013:ICMLA,

• author = "Gul Muhammad Khan and Faheem Zafari and S. Ali Mahmud",
• title = "Very Short Term Load Forecasting Using Cartesian Genetic Programming Evolved Recurrent Neural Networks (CGPRNN)",
• booktitle = "12th International Conference on Machine Learning and Applications (ICMLA 2013)",
• year = "2013",
• month = "4-7 " # dec,
• volume = "2",
• pages = "152--155",
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, Cartesian Genetic Programming evolved Recurrent Neural Network (CGPRNN), Very Short Term Load forecast (VSTLF)",
• DOI = "doi:10.1109/ICMLA.2013.181",
• abstract = "Forecasting the electrical load requirements is an important research objective for maintaining a balance between the demand and generation of electricity. This paper uses a neuro-evolutionary technique known as Cartesian Genetic Programming evolved Recurrent Neural Network (CGPRNN) to develop a load forecasting model for very short term of half an hour. The network is trained using historical data of one month on half hourly basis to predict the next half hour load based on the 12 and 24 hours data history. The results demonstrate that CGPRNN is superior to other networks in very short term load forecasting in terms of its accuracy achieving 99.57 percent. The model was developed and evaluated on the data collected from the UK Grid station.",
• notes = "Also known as \cite{6786098}",

}

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

Citations