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Electricity load forecast using neural network trained from wavelet-transformed data

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dc.citedby9
dc.contributor.authorBenaouda D.en_US
dc.contributor.authorMurtagh F.en_US
dc.contributor.authorid15844746300en_US
dc.contributor.authorid7005746699en_US
dc.date.accessioned2023-12-28T08:57:40Z
dc.date.available2023-12-28T08:57:40Z
dc.date.issued2006
dc.description.abstractWith accurate electricity load forecasting important information is provided that helps to build up cost effective risk management plans for any electric utility such as electricity generators and retailers in the electricity market. In this article, we propose a wavelet based multilayer perceptron (MLPw) approach for the prediction of one-hour and one-day ahead load trained from Haar � trous wavelet-transformed historical electricity load data. We assess results produced by the MLPw method, with multiple resolution autoregressive (MAR), single resolution autoregressive (AR), multilayer perceptron (MLP), and the general regression neural network (GRNN) model. Experimental Results are based on the New South Wales (Australia) electricity load data that is provided by the National Electricity Market Management Company (NEMMCO). � 2006 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1703163
dc.identifier.scopus2-s2.0-40849113353
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-40849113353&partnerID=40&md5=c8a647375d394a89ff64cd857b3a241e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29788
dc.sourceScopus
dc.sourcetitleIEEE International Conference on Engineering of Intelligent Systems, ICEIS 2006
dc.subjectMulti-layer perceptron
dc.subjectResolution scale
dc.subjectWavelet transforms
dc.subjectElectric utilities
dc.subjectMultilayer neural networks
dc.subjectRisk management
dc.subjectWavelet transforms
dc.subjectElectricity load forecast
dc.subjectGeneral regression neural network
dc.subjectMultiple resolution autoregressive
dc.subjectSingle resolution autoregressive
dc.subjectElectric loads
dc.titleElectricity load forecast using neural network trained from wavelet-transformed dataen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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