DocumentCode :
1713882
Title :
Multiple RBF-NN model for electrical load prediction based on anti-aliasing wavelet transform
Author :
Luo Zhongliang ; Chen Zhiming ; Huang Xiaohong ; Luo Fei
Author_Institution :
Huizhou Univ., Huizhou, China
fYear :
2013
Firstpage :
3310
Lastpage :
3313
Abstract :
Electrical load prediction is very important for scheduling and administration of electricity power system. In order to improve the short-term electrical load forecasting accuracy, a method based on multiple RBF neural networks is proposed in this paper. The wavelet transform method is firstly used to decompose the electrical load into different parts, corresponding to different influencing factors. In addition, an anti-aliasing method is designed to eliminate the frequency aliasing during wavelet decomposition and reconstruction. Then a compound model with three sub RBF neural networks is constructed, each for a part of the decomposed electrical load. Utilizing the historical data, the model is trained and prediction is carried out. Simulation results show that the proposed multiple RBF neural network method can achieve good performance, the mean absolute percentage error decreases from 3.86 by ANN method to 1.7 by the proposed method.
Keywords :
antialiasing; load forecasting; neural nets; power engineering computing; radial basis function networks; scheduling; signal reconstruction; wavelet transforms; ANN method; RBF neural network method; RBF-NN model; antialiasing method; compound model; electrical load decomposition; electrical load forecasting accuracy; electrical load prediction; electricity power system administration; electricity power system scheduling; mean absolute percentage error; wavelet decomposition; wavelet reconstruction; wavelet transform method; Artificial neural networks; Image reconstruction; Load forecasting; Load modeling; Wavelet transforms; RBF neural networks; electrical load prediction; multiple networks; wavelet transform;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2013 32nd Chinese
Conference_Location :
Xi´an
Type :
conf
Filename :
6639992
Link To Document :
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