Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/62644
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dc.titlePractical implementation of a hybrid fuzzy neural network for one-day-ahead load forecasting
dc.contributor.authorSrinivasan, D.
dc.contributor.authorTan, S.S.
dc.contributor.authorChang, C.S.
dc.contributor.authorChan, E.K.
dc.date.accessioned2014-06-17T06:53:20Z
dc.date.available2014-06-17T06:53:20Z
dc.date.issued1998
dc.identifier.citationSrinivasan, D.,Tan, S.S.,Chang, C.S.,Chan, E.K. (1998). Practical implementation of a hybrid fuzzy neural network for one-day-ahead load forecasting. IEE Proceedings: Generation, Transmission and Distribution 145 (6) : 687-692. ScholarBank@NUS Repository.
dc.identifier.issn13502360
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/62644
dc.description.abstractThe paper presents the development and practical implementation of a hybrid short-term electrical load forecasting model for a power system control centre. This hybrid architecture incorporates a Kohonen self-organising feature map with unsupervised learning for classification of daily load patterns, a supervised backpropagation neural network for mapping the temperature/load relationship, and a fuzzy expert system for postprocessing of neural network outputs. This load forecaster requires minimum operator intervention and can be trained adaptively on-line. The developed model has been tested extensively in the actual operating environment and has been shown to outperform the existing regression-based model. © IEE, 1998.
dc.sourceScopus
dc.subjectFuzzy neural networks
dc.subjectLoad forecasting
dc.typeArticle
dc.contributor.departmentELECTRICAL ENGINEERING
dc.description.sourcetitleIEE Proceedings: Generation, Transmission and Distribution
dc.description.volume145
dc.description.issue6
dc.description.page687-692
dc.description.codenIGTDE
dc.identifier.isiutNOT_IN_WOS
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