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2014 (Vol. 7, Issue: 13)
Article Information:

A Hybrid Neural Network and Genetic Algorithm Based Model for Short Term Load Forecast

B. Islam, Z. Baharudin, Q. Raza and P. Nallagownden
Corresponding Author:  B. Islam 

Key words:  Artificial neural network, genetic algorithm, levenberg-marquardt, short term load forecast, , ,
Vol. 7 , (13): 2667-2673
Submitted Accepted Published
July 17, 2013 August 08, 2013 April 05, 2014
Abstract:

Aim of this research is to develop a hybrid prediction model based on Artificial Neural Network (ANN) and Genetic Algorithm (GA) that integrates the benefits of both techniques to increase the electrical load forecast accuracy. Precise Short Term Load Forecast (STLF) is of critical importance for the secure and reliable operation of power systems. ANNs are largely implemented in this domain due to their nonlinear mapping nature. The ANN architecture optimization, the initial weight values of the neurons, selection of training algorithm and critical analysis and selection of the most appropriate input parameters are some important consideration for STLF. Levenberg-Marquardt (LM) algorithm for the training of the neural network is implemented in the first stage. The second stage is based on a hybrid model which combines the ANN and GA.
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  Cite this Reference:
B. Islam, Z. Baharudin, Q. Raza and P. Nallagownden, 2014. A Hybrid Neural Network and Genetic Algorithm Based Model for Short Term Load Forecast.  Research Journal of Applied Sciences, Engineering and Technology, 7(13): 2667-2673.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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