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Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting

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...

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Main Authors:	Benaouda D., Murtagh F., Starck J.-L., Renaud O.
Other Authors:	15844746300
Format:	Article
Published:	2023
Subjects:	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 Multiple resolution decomposition Multiscale autoregressive method Electric loads
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id	my.uniten.dspace-29784
record_format	dspace
spelling	my.uniten.dspace-297842023-12-28T16:57:40Z Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting Benaouda D. Murtagh F. Starck J.-L. Renaud O. 15844746300 7005746699 7005106453 6602832344 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 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. Final 2023-12-28T08:57:40Z 2023-12-28T08:57:40Z 2006 Article 10.1016/j.neucom.2006.04.005 2-s2.0-33750417685 https://www.scopus.com/inward/record.uri?eid=2-s2.0-33750417685&doi=10.1016%2fj.neucom.2006.04.005&partnerID=40&md5=ae2bff4ef5a2ab51abab8875af6c6b59 https://irepository.uniten.edu.my/handle/123456789/29784 70 01/03/2023 139 154 Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	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
spellingShingle	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 Benaouda D. Murtagh F. Starck J.-L. Renaud O. Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting
description	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.
author2	15844746300
author_facet	15844746300 Benaouda D. Murtagh F. Starck J.-L. Renaud O.
format	Article
author	Benaouda D. Murtagh F. Starck J.-L. Renaud O.
author_sort	Benaouda D.
title	Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting
title_short	Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting
title_full	Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting
title_fullStr	Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting
title_full_unstemmed	Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting
title_sort	wavelet-based nonlinear multiscale decomposition model for electricity load forecasting
publishDate	2023
_version_	1806423465326018560
score	13.211869

Wavelet-based nonlinear multiscale decomposition model for electricity load forecasting

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