Please use this identifier to cite or link to this item:
Title: Short term daily average and peak load predications using a hybrid intelligent approach
Authors: Dash, P K
Satpathy, H P
Rahman, S
Keywords: Kalman filters
fuzzy neural nets
load forecasting
multilayer perceptrons
Issue Date: 1995
Publisher: IEEE
Citation: International Conference on Energy Management and Power Delivery(EMPD '95), 21-23 Nov. 1995, P 565 - 570 vol.2
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
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.
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
pkd1995.pdf594.13 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.