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dc.contributor.authorDash, P K-
dc.contributor.authorLiew, A C-
dc.contributor.authorRahman, S-
dc.identifier.citationIEE Proceedings-Generation, Transmission and Distribution, Vol 143, Iss 1, P 106-114en
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 IEE.en
dc.description.abstractA hybrid neural network fuzzy expert system is developed to forecast short-term electric load accurately. The fuzzy membership values of the load and other weather variables are the inputs to the neural network, and the output comprises the membership values 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. Extensive studies have been performed for all seasons, and a few examples are presented in the paper, average, peak and hourly load forecastsen
dc.format.extent873522 bytes-
dc.subjectexpert systemsen
dc.subjectfuzzy neural netsen
dc.subjectload forecastingen
dc.titleFuzzy neural network and fuzzy expert system for load forecastingen
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