Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting

D. Benaouda, F. Murtagh, J. L. Starck, O. Renaud

Research output: Contribution to journalArticle

84 Citations (Scopus)

Abstract

We 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 à 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).

Original languageEnglish
Pages (from-to)139-154
Number of pages16
JournalNeurocomputing
Volume70
Issue number1-3
Early online date6 Jun 2006
DOIs
Publication statusPublished - 1 Dec 2006
Externally publishedYes

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