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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/75

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contributor.authorDash, P K-
contributor.authorDash, S-
contributor.authorRahman, S-
date.accessioned2005-06-29T05:09:11Z-
date.available2005-06-29T05:09:11Z-
date.issued1993-
identifier.citationProceedings of the Second International Forum on Applications of Neural Networks to Power Systems, ANNPS '93.,19-22 April 1993, Yokohama, P 432-437en
identifier.urihttp://hdl.handle.net/2080/75-
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 IEEE.en
description.abstractA hybrid neural network-fuzzy expert system is developed to forecast one hour to forty-eight hour ahead electric load accurately. The fuzzy membership values of load and other weather variables are the inputs to the neural network and the output comprises the membership value of the predicted load. An adaptive fuzzy correction scheme is used to forecast the final load by using a fuzzy rule base and fuzzy inference mechanism. The paper also presents a fuzzy pattern classification approach for identifying the day-type from the historical load database to be used for training the neural network. Extensive studies have been performed for all seasons, although the results for a typical winter day are given in the paper to demonstrate the powerfulness of this techniqueen
format.extent498524 bytes-
format.mimetypeapplication/pdf-
language.isoen-
publisherIEEEen
subjectexpert systemsen
subjectinference mechanismsen
subjectload forecastingen
subjectneural netsen
titleA fuzzy adaptive correction scheme for short term load forecasting using fuzzy layered neural networken
typeArticleen
Appears in Collections:Conference Papers

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