DSpace@nitr >
National Institue of Technology- Rourkela >
Conference Papers >

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

Title: Genetic optimization of a self organizing fuzzy-neural network for load forecasting
Authors: Dash, P K
Mishra, S
Dash, S
Liew, A C
Keywords: backpropagation
fuzzy neural nets
inference mechanisms
load forecasting
Issue Date: 2000
Publisher: IEEE
Citation: IEEE Power Engineering Society Winter Meeting, 23-27 Jan. 2000, P 1011 - 1016 vol.2
Abstract: In 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 forecasting
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.
URI: http://hdl.handle.net/2080/100
Appears in Collections:Conference Papers

Files in This Item:

File Description SizeFormat
pkd2000co4.pdf534KbAdobe PDFView/Open

Show full item record

All items in DSpace are protected by copyright, with all rights reserved.

 

Powered by DSpace Feedback