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
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.
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;
Conference_Titel :
Natural Computation (ICNC), 2011 Seventh International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9950-2
DOI :
10.1109/ICNC.2011.6022118
