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Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting

Benaouda, D.
Murtagh, F.
Starck, J.-L.
Published in Neurocomputing. 2006, vol. 70, no. 1-3, p. 139-154
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).
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Research group Méthodologie et analyse des données (MAD)
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BENAOUDA, D. et al. Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting. In: Neurocomputing, 2006, vol. 70, n° 1-3, p. 139-154. doi: 10.1016/j.neucom.2006.04.005 https://archive-ouverte.unige.ch/unige:17371

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Deposited on : 2011-11-09

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