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dc.contributor.authorDash, P K-
dc.contributor.authorLiew, A C-
dc.contributor.authorRahman, S-
dc.contributor.authorRamakrishna, G-
dc.identifier.citationExpert Systems with Applications, Vol 9, Iss 3, P 407-421en
dc.descriptionCopyright of this article belongs to Elsevier Science Ltd.en
dc.description.abstractThis paper presents the development of a hybrid neural network to model a fuzzy expert system for time series forecasting of electric load. The hybrid neural network is trained to develop fuzzy logic rules and find optimal input/output membership values of load and weather parameters. A hybrid learning algorithm consisting of unsupervised and supervised learning phases is used for training the fuzzified neural network. In the supervised learning phase, both back propagation and linear Kalman filter algorithms are used for the adjustment of weights and membership functions. Extensive tests have been performed on a 2-year utility data for the generation of peak and average load profiles in 24 h, 48 h, and 168 h ahead time frame during summer and winter seasons. From the simulation results, it is observed that the fuzzy expert system using the Kalman filter-based algorithm gives faster convergence and more accurate prediction of a load time series.en
dc.format.extent1204501 bytes-
dc.subjectHybrid Neural Networken
dc.subjectfuzzy expert systemen
dc.subjecttime series forecastingen
dc.titleBuilding a fuzzy expert system for electric load forecasting using a hybrid neural networken
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