Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting

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Benaouda D.
Murtagh F.
Starck J.-L.
Renaud O.
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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 � 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.
Autoregression , General regression neural network , Load forecast , Multi-layer perceptron , Recurrent neural network , Resolution , Scale , Time series , Wavelet transform , Multilayer neural networks , Recurrent neural networks , Time series analysis , Wavelet transforms , article , artificial neural network , Australia , decomposition , electricity , forecasting , information processing , model , power supply , prediction , priority journal , signal processing , General regression neural network , Multiple resolution decomposition , Multiscale autoregressive method , Electric loads