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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/681
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| Title: | Short Term Load Forecasting using Neural Network trained with Genetic Algorithm & Particle Swarm Optimization |
| Authors: | Mishra, Sanjib
Patra, S K |
| Issue Date: | 2008 |
| Publisher: | IEEE |
| Citation: | Ist international conference on emerging trends in engineering & technology will be held during July 16-18, 2008 at Nagpur, Maharastra |
| Abstract: | Short term load forecasting is very essential to the
operation of electricity companies. It enhances the
energy-efficient and reliable operation of power
system. Artificial neural networks have long been
proven as a very accurate non-linear mapper. ANN
based STLF models generally use Back propagation
algorithm which does not converge optimally &
requires much longer time for training, which makes it
difficult for real-time application. In this paper we
propose a smaller MLPNN trained by Genetic
algorithm & Particle swarm optimization. The GA
training gives better accuracy than BP training, where
as it takes much longer time. But the PSO training
approach converges much faster than both the BP and
GA, with a slight compromise in accuracy. This looks
to be very suitable for real-time implementation. |
| Description: | Copyright for the paper belongs to IEEE |
| URI: | http://hdl.handle.net/2080/681 |
| Appears in Collections: | Conference Papers
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| sanjib-conf-2008.pdf | | 227Kb | Adobe PDF | View/Open |
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