Publication:
Hybrid wavelet model for electricity pool-price forecasting in a deregulated electricity market

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dc.citedby6
dc.contributor.authorBenaouda D.en_US
dc.contributor.authorMurtagh F.en_US
dc.contributor.authorid15844746300en_US
dc.contributor.authorid7005746699en_US
dc.date.accessioned2023-12-28T08:57:44Z
dc.date.available2023-12-28T08:57:44Z
dc.date.issued2006
dc.description.abstractElectricity supply industry is in the process of deregulation in many countries including Australia. The purpose of deregulation is to give consumers free choices of their electricity supply. Thus, accurate electricity pool price forecasting can provide a set of vital predicted information that helps generation, transmission and retailer participating companies to bid strategically into a deregulated electricity market in order to maximize their profits. In this article, we propose a wavelet multiscale decomposition based autoregressive approach for the prediction of one-hour ahead and one-day ahead pool price based on historical electricity pool price and predicted 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 re-compute the wavelet transform (wavelet coefficients) of the full signal if the electricity pool price 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 autoregressive (AR), and multilayer perceptron (MLP) model. Experimental results are based on the New South Wales (Australia) electricity load and pool price data that is provided by the National Electricity Market Management Company (NEMMCO). � 2006 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1703198
dc.identifier.scopus2-s2.0-40849092833
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-40849092833&partnerID=40&md5=715c9107e796254a92f51817c76f47e8
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29807
dc.sourceScopus
dc.sourcetitleIEEE International Conference on Engineering of Intelligent Systems, ICEIS 2006
dc.subjectAutoregression
dc.subjectMulti-layer perceptron
dc.subjectResolution scale
dc.subjectTime-series
dc.subjectWavelet transform
dc.subjectEnergy policy
dc.subjectMarketing
dc.subjectMultilayer neural networks
dc.subjectRegression analysis
dc.subjectTime series analysis
dc.subjectWavelet transforms
dc.subjectAutoregression
dc.subjectElectricity markets
dc.subjectHybrid wavelet models
dc.subjectResolution scales
dc.subjectElectric load forecasting
dc.titleHybrid wavelet model for electricity pool-price forecasting in a deregulated electricity marketen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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