Islam, B. and Baharudin, Z. and Raza, Q. and Nallagownden, P. (2013) Hybrid and integrated intelligent system for load demand prediction. In: UNSPECIFIED.
Full text not available from this repository.Abstract
Artificial neural networks (ANN) are receiving a lot of attention because of their nonlinear mapping ability in the field of short term load forecast (STLF). ANN based STLF model commonly use back propagation algorithm, that may not converge properly, that affects the forecast accuracy. A hybrid approach, based on artificial neural network (ANN) and genetic algorithm (GA) that combines the advantages of each technique is proposed in this research. Genetic algorithm is implemented for the optimization of the architecture of feedforward neural network and selection of its initial weight values. Error back propagation algorithm for the training of the optimized neural network will be implemented. The second stage of this research is related with the complete training of the neural network based on genetic algorithm, using genetic manipulation of chromosomes. The results show that this approach produced better output in terms of enhanced forecast accuracy. © 2013 IEEE.
Item Type: | Conference or Workshop Item (UNSPECIFIED) |
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Additional Information: | cited By 3; Conference of 2013 IEEE 7th International Power Engineering and Optimization Conference, PEOCO 2013 ; Conference Date: 3 June 2013 Through 4 June 2013; Conference Code:98676 |
Uncontrolled Keywords: | Error back propagation algorithm; Forecast accuracy; Genetic manipulations; Initial weight values; Integrated intelligent systems; Multi-layer perceptron neural networks; Nonlinear mappings; Short term load forecast, Backpropagation; Feedforward neural networks; Forecasting; Intelligent systems; Optimization, Genetic algorithms |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 09 Nov 2023 15:51 |
Last Modified: | 09 Nov 2023 15:51 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/3504 |