Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30797
Title: Electricity price forecasting with extreme learning machine and bootstrapping
Authors: Chen, X
Dong, ZY
Meng, K
Ku, Y
Wong, KP
Ngan, HW
Keywords: Bootstrapping
Extreme learning machine
Interval forecast
Price forecast
Issue Date: 2012
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on power systems, 2012, v. 27, no. 4, 6184354, p. 2055-2062 How to cite?
Journal: IEEE transactions on power systems 
Abstract: Artificial neural networks (ANNs) have been widely applied in electricity price forecasts due to their nonlinear modeling capabilities. However, it is well known that in general, traditional training methods for ANNs such as back-propagation (BP) approach are normally slow and it could be trapped into local optima. In this paper, a fast electricity market price forecast method is proposed based on a recently emerged learning method for single hidden layer feed-forward neural networks, the extreme learning machine (ELM), to overcome these drawbacks. The new approach also has improved price intervals forecast accuracy by incorporating bootstrapping method for uncertainty estimations. Case studies based on chaos time series and Australian National Electricity Market price series show that the proposed method can effectively capture the nonlinearity from the highly volatile price data series with much less computation time compared with other methods. The results show the great potential of this proposed approach for online accurate price forecasting for the spot market prices analysis.
URI: http://hdl.handle.net/10397/30797
ISSN: 0885-8950
EISSN: 1558-0679
DOI: 10.1109/TPWRS.2012.2190627
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

68
Last Week
0
Last month
5
Citations as of Jul 22, 2017

WEB OF SCIENCETM
Citations

57
Last Week
1
Last month
3
Citations as of Aug 14, 2017

Page view(s)

38
Last Week
3
Last month
Checked on Aug 14, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.