Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/730
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dc.contributor.authorMishra, S-
dc.contributor.authorPatra, S K-
dc.date.accessioned2008-10-06T09:20:54Z-
dc.date.available2008-10-06T09:20:54Z-
dc.date.issued2008-
dc.identifier.citationProceedings of the Conference on merging Trends in Engineering and Technology, 2008. ICETET '08, Nagpur, India P 606-611en
dc.identifier.urihttp://dx.doi.org/10.1109/ICETET.2008.94-
dc.identifier.urihttp://hdl.handle.net/2080/730-
dc.descriptionCopyright for the paper belongs to IEEEen
dc.description.abstractShort 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.en
dc.format.extent249453 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherIEEEen
dc.subjectbackpropagationen
dc.subjectgenetic algorithmsen
dc.subjectload forecastingen
dc.subjectneural netsen
dc.subjectparticle swarm optimisationen
dc.subjectpower engineering computingen
dc.titleShort Term Load Forecasting Using Neural Network Trained with Genetic Algorithm & Particle Swarm Optimizationen
dc.typeArticleen
Appears in Collections:Conference Papers

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