Short term daily average and peak load predications using a hybrid intelligent approach

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/91

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DC Field	Value	Language
dc.contributor.author	Dash, P K	-
dc.contributor.author	Satpathy, H P	-
dc.contributor.author	Rahman, S	-
dc.date.accessioned	2005-07-02T05:39:19Z	-
dc.date.available	2005-07-02T05:39:19Z	-
dc.date.issued	1995	-
dc.identifier.citation	International Conference on Energy Management and Power Delivery(EMPD '95), 21-23 Nov. 1995, P 565 - 570 vol.2	en
dc.identifier.uri	http://hdl.handle.net/2080/91	-
dc.description	Personal 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
dc.description.abstract	A fuzzy neural network based on the multilayer perceptron and capable of fuzzy classification of patterns is presented in this paper. A hybrid learning algorithm consisting of unsupervised and supervised learning phases is used for training the network. In the supervised learning phase linear Kalman filter equations are used for tuning the weights and membership functions. Extensive tests have been performed on a two-year-utility data for generation of peak and average load profiles for 24- and 168-hours ahead time frames and results for winter and summer months are given to confirm the effectiveness of the new approach	en
dc.format.extent	608394 bytes	-
dc.format.mimetype	application/pdf	-
dc.language.iso	en	-
dc.publisher	IEEE	en
dc.subject	Kalman filters	en
dc.subject	fuzzy neural nets	en
dc.subject	load forecasting	en
dc.subject	multilayer perceptrons	en
dc.title	Short term daily average and peak load predications using a hybrid intelligent approach	en
dc.type	Article	en
Appears in Collections:	Conference Papers

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