DocumentCode :
3499615
Title :
Application of Wavelet Neutral Network and Rough Set Theory to Forecast Mid-Long-Term Electric Power Load
Author :
Ji, Zhigang ; Zhang, Peijun ; Zhao, Zhiwei
Author_Institution :
Dept. of the Libr., Hebei Univ. of Eng., Handan
Volume :
1
fYear :
2009
fDate :
7-8 March 2009
Firstpage :
1104
Lastpage :
1108
Abstract :
A new machine learning method-wavelet neutral network was introduced and some of its characteristics were discussed. Rough set and WNN are combined to establish a rough set-based data pre-processing wavelet network model. It effectively overcome the wavelet network does not distinguish importance of property of samples and slow defect in a large number of data processing operations. After linearly scaling and rough sets theory, the data that affect the mid-long-term electric power load were trained by the tools of WNN.
Keywords :
learning (artificial intelligence); load forecasting; power engineering computing; rough set theory; electric power load; load forecasting; machine learning; rough set theory; wavelet neutral network; Computer science education; Continuous wavelet transforms; Demand forecasting; Educational technology; Fourier transforms; Frequency; Load forecasting; Power engineering and energy; Power engineering education; Set theory; Rough set; Wavelet neutral network; electric power load;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Education Technology and Computer Science, 2009. ETCS '09. First International Workshop on
Conference_Location :
Wuhan, Hubei
Print_ISBN :
978-1-4244-3581-4
Type :
conf
DOI :
10.1109/ETCS.2009.252
Filename :
4958956
Link To Document :
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