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