DocumentCode
3343098
Title
Notice of Retraction
Short-term load forecasting based on chaos theory and RBF neural network
Author
Zhenzhen Yuan ; Shuang Liu ; Linyan Xue ; Xiu´e Yuan
Author_Institution
Sch. of Bus. & Adm., North China Electr. Power Univ., Baoding, China
Volume
1
fYear
2011
fDate
26-28 July 2011
Firstpage
526
Lastpage
529
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Power system load is a nonlinear time series, for the complexity and nonlinear of power systems loads, this paper combines the idea of chaos theory, make full use of data in the reconstruction phase space power load based on the load of forecast, due to the approximation capability of neural networks with superior predictive ability, the use of RBF neural network-based method and Matlab simulation, the simulation shows that such a prediction algorithm to obtain good results.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Power system load is a nonlinear time series, for the complexity and nonlinear of power systems loads, this paper combines the idea of chaos theory, make full use of data in the reconstruction phase space power load based on the load of forecast, due to the approximation capability of neural networks with superior predictive ability, the use of RBF neural network-based method and Matlab simulation, the simulation shows that such a prediction algorithm to obtain good results.
Keywords
digital simulation; load forecasting; mathematics computing; power engineering computing; radial basis function networks; time series; Matlab simulation; RBF neural network-based method; chaos theory; nonlinear time series; power system load; prediction algorithm; short-term load forecasting; Chaos; Educational institutions; Load forecasting; Load modeling; Neurons; Predictive models; RBF neural network; chaos; load forecasting;
fLanguage
English
Publisher
ieee
Conference_Titel
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location
Shanghai
ISSN
2157-9555
Print_ISBN
978-1-4244-9950-2
Type
conf
DOI
10.1109/ICNC.2011.6022118
Filename
6022118
Link To Document