An adaptive neural-wavelet model for short term load forecasting

Zhang, B. L. and Dong, Z. Y. (2001) An adaptive neural-wavelet model for short term load forecasting. Electric Power Systems Research, 59 2: 121-129. doi:10.1016/S0378-7796(01)00138-9

Author Zhang, B. L.
Dong, Z. Y.
Title An adaptive neural-wavelet model for short term load forecasting
Journal name Electric Power Systems Research   Check publisher's open access policy
ISSN 0378-7796
Publication date 2001
Sub-type Article (original research)
DOI 10.1016/S0378-7796(01)00138-9
Volume 59
Issue 2
Start page 121
End page 129
Total pages 9
Editor B.D. Russell
G. Andersson
A.K. David
J.D. Morgan
Place of publication Lausanne
Publisher Elsevier Science
Collection year 2001
Language eng
Subject C1
290901 Electrical Engineering
660301 Electricity transmission
Abstract This paper proposed a novel model for short term load forecast in the competitive electricity market. The prior electricity demand data are treated as time series. The forecast model is based on wavelet multi-resolution decomposition by autocorrelation shell representation and neural networks (multilayer perceptrons, or MLPs) modeling of wavelet coefficients. To minimize the influence of noisy low level coefficients, we applied the practical Bayesian method Automatic Relevance Determination (ARD) model to choose the size of MLPs, which are then trained to provide forecasts. The individual wavelet domain forecasts are recombined to form the accurate overall forecast. The proposed method is tested using Queensland electricity demand data from the Australian National Electricity Market. (C) 2001 Elsevier Science B.V. All rights reserved.
Keyword Engineering, Electrical & Electronic
Neural Network
Time Series
Adaptive Learning
Load Forecast
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 89 times in Thomson Reuters Web of Science Article | Citations
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Created: Tue, 14 Aug 2007, 15:26:33 EST