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dc.contributor.authorDash, P K-
dc.contributor.authorRamakrishna, G-
dc.contributor.authorLiew, A C-
dc.contributor.authorRahman, S-
dc.identifier.citationIEE Proceedings-Generation, Transmission and Distribution, Vol 142, Iss 5, P 535-544en
dc.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 IEEen
dc.description.abstractThree computing models, based on the multilayer perceptron and capable of fuzzy classification of patterns, are presented. The first type of fuzzy neural network uses the membership values of the linguistic properties of the past load and weather parameters and the output of the network is defined as fuzzy-class-membership values of the forecast load. The backpropagation algorithm is used to train the network. The second and third types of fuzzy neural network are developed based on the fact that any fuzzy expert system can be represented in the form of a feedforward neural network. These two types of fuzzy-neural-network model can be trained to develop fuzzy-logic rules and find optimal input/output membership values. A hybrid learning algorithm consisting of unsupervised and supervised learning phases is used to train the two models. Extensive tests have been performed on two-years of utility data for generation of peak and average load profiles 24 hours and 168 hours ahead, and results for typical winter and summer months are given to confirm the effectiveness of the three modelsen
dc.format.extent749520 bytes-
dc.subjectfeedforward neural netsen
dc.subjectfuzzy neural netsen
dc.subjectload forecastingen
dc.titleFuzzy neural networks for time-series forecasting of electric loaden
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