Self-adaptive radial basis function neural network for short-term electricity price forecasting

Meng, K., Dong, Z.Y. and Wong, K.P. (2009) Self-adaptive radial basis function neural network for short-term electricity price forecasting. IET Generation, Transmission and Distribution, 3 4: 325-335. doi:10.1049/iet-gtd.2008.0328


Author Meng, K.
Dong, Z.Y.
Wong, K.P.
Title Self-adaptive radial basis function neural network for short-term electricity price forecasting
Journal name IET Generation, Transmission and Distribution   Check publisher's open access policy
ISSN 1751-8687
Publication date 2009-04
Sub-type Article (original research)
DOI 10.1049/iet-gtd.2008.0328
Volume 3
Issue 4
Start page 325
End page 335
Total pages 11
Place of publication United Kingdom
Publisher The Institution of Engineering and Technology
Language eng
Abstract Effective and reliable electricity price forecast is essential for market participants in setting up appropriate risk management plans in an electricity market. A reliable price prediction model based on an advanced self-adaptive radial basis function (RBF) neural network is presented. The proposed RBF neural network model is trained by fuzzy c-means and differential evolution is used to auto-configure the structure of networks and obtain the model parameters. With these techniques, the number of neurons, cluster centres and radii of the hidden layer, and the output weights can be automatically calculated efficiently. Meanwhile, the moving window wavelet de-noising technique is introduced to improve the network performance as well. This learning approach is proven to be effective by applying the RBF neural network in predicting of Mackey-Glass chaos time series and forecasting of the electricity regional reference price from the Queensland electricity market of the Australian National Electricity Market. © 2009 The Institution of Engineering and Technology.
Keyword Market
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Information Technology and Electrical Engineering Publications
 
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Created: Thu, 03 Sep 2009, 08:18:35 EST by Mr Andrew Martlew on behalf of School of Information Technol and Elec Engineering