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Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting

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dc.citedby103
dc.contributor.authorBenaouda D.en_US
dc.contributor.authorMurtagh F.en_US
dc.contributor.authorStarck J.-L.en_US
dc.contributor.authorRenaud O.en_US
dc.contributor.authorid15844746300en_US
dc.contributor.authorid7005746699en_US
dc.contributor.authorid7005106453en_US
dc.contributor.authorid6602832344en_US
dc.date.accessioned2023-12-28T08:57:40Z
dc.date.available2023-12-28T08:57:40Z
dc.date.issued2006
dc.description.abstractWe propose a wavelet multiscale decomposition-based autoregressive approach for the prediction of 1-h ahead load based on historical electricity load data. This approach is based on a multiple resolution decomposition of the signal using the non-decimated or redundant Haar � trous wavelet transform whose advantage is taking into account the asymmetric nature of the time-varying data. There is an additional computational advantage in that there is no need to recompute the wavelet transform (wavelet coefficients) of the full signal if the electricity data (time series) is regularly updated. We assess results produced by this multiscale autoregressive (MAR) method, in both linear and non-linear variants, with single resolution autoregression (AR), multilayer perceptron (MLP), Elman recurrent neural network (ERN) and the general regression neural network (GRNN) models. Results are based on the New South Wales (Australia) electricity load data that is provided by the National Electricity Market Management Company (NEMMCO). � 2006 Elsevier B.V. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.neucom.2006.04.005
dc.identifier.epage154
dc.identifier.issue01/03/2023
dc.identifier.scopus2-s2.0-33750417685
dc.identifier.spage139
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-33750417685&doi=10.1016%2fj.neucom.2006.04.005&partnerID=40&md5=ae2bff4ef5a2ab51abab8875af6c6b59
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29784
dc.identifier.volume70
dc.pagecount15
dc.sourceScopus
dc.sourcetitleNeurocomputing
dc.subjectAutoregression
dc.subjectGeneral regression neural network
dc.subjectLoad forecast
dc.subjectMulti-layer perceptron
dc.subjectRecurrent neural network
dc.subjectResolution
dc.subjectScale
dc.subjectTime series
dc.subjectWavelet transform
dc.subjectMultilayer neural networks
dc.subjectRecurrent neural networks
dc.subjectTime series analysis
dc.subjectWavelet transforms
dc.subjectarticle
dc.subjectartificial neural network
dc.subjectAustralia
dc.subjectdecomposition
dc.subjectelectricity
dc.subjectforecasting
dc.subjectinformation processing
dc.subjectmodel
dc.subjectpower supply
dc.subjectprediction
dc.subjectpriority journal
dc.subjectsignal processing
dc.subjectGeneral regression neural network
dc.subjectMultiple resolution decomposition
dc.subjectMultiscale autoregressive method
dc.subjectElectric loads
dc.titleWavelet-based nonlinear multiscale decomposition model for electricity load forecastingen_US
dc.typeArticleen_US
dspace.entity.typePublication
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