Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/62208
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dc.titleForecasting daily load curves using a hybrid fuzzy-neural approach
dc.contributor.authorSrinivasan, D.
dc.contributor.authorLiew, A.C.
dc.contributor.authorChang, C.S.
dc.date.accessioned2014-06-17T06:48:34Z
dc.date.available2014-06-17T06:48:34Z
dc.date.issued1994-11
dc.identifier.citationSrinivasan, D., Liew, A.C., Chang, C.S. (1994-11). Forecasting daily load curves using a hybrid fuzzy-neural approach. IEE Proceedings: Generation, Transmission and Distribution 141 (6) : 561-567. ScholarBank@NUS Repository.
dc.identifier.issn13502360
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/62208
dc.description.abstractA new approach to electric load forecasting which combines the powers of neural network and fuzzy logic techniques is proposed. Expert knowledge represented by fuzzy rules is used for preprocessing input data fed to a neural network. The method effectively deals with trends and special events that occur annually. The fuzzy-neural network is trained on real data from a power system and evaluated for forecasting next-day load profiles based on forecast weather data and other parameters. Simulation results are presented to illustrate the performance and applicability of this approach. A comparison of results with other forecasting techniques establishes its superiority.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1049/ip-gtd:19941288
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentELECTRICAL ENGINEERING
dc.description.sourcetitleIEE Proceedings: Generation, Transmission and Distribution
dc.description.volume141
dc.description.issue6
dc.description.page561-567
dc.description.codenIGTDE
dc.identifier.isiutA1994PV49900003
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