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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/100

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contributor.authorDash, P K-
contributor.authorMishra, S-
contributor.authorDash, S-
contributor.authorLiew, A C-
identifier.citationIEEE Power Engineering Society Winter Meeting, 23-27 Jan. 2000, P 1011 - 1016 vol.2en
descriptionPersonal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en
description.abstractIn this paper a self-organizing fuzzy-neural network with a new learning mechanism and rule optimization using genetic algorithm (GA) is proposed for load forecasting. The number of rules in the inferencing layer is optimized using a genetic algorithm and an appropriate fitness function. We devise a learning algorithm for updating the connecting weights as well as the structure of the membership functions of the network. The proposed algorithm exploits the notion of error back propagation. The network weights are initialized with random weights instead of any preselected ones. The performance of the network is validated by extensive simulation results using practical data ranging over a period of two years. The optimized fuzzy neural network provides an accurate prediction of electrical load in a time frame varying from 24 to 168 hours ahead. The algorithm is adaptive and performs much better than the existing ANN techniques used for load forecastingen
format.extent546886 bytes-
subjectfuzzy neural netsen
subjectinference mechanismsen
subjectload forecastingen
titleGenetic optimization of a self organizing fuzzy-neural network for load forecastingen
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