DocumentCode
527022
Title
Application of SVM based on rough set in electricity prices forecasting
Author
Wang, Ting ; Qin, Lijuan
Author_Institution
Dept. of Economic Manage., North China Electr. Power Univ., Baoding, China
Volume
2
fYear
2010
fDate
17-18 July 2010
Firstpage
317
Lastpage
320
Abstract
Price is a core element of the electricity market, Price forecasting is an important issue of great concern to all participants, in order to improve the accuracy of price forecasting, it introduces rough set and support vector machines for prediction models in the paper, integrates the advantages of each model. The experimental results prove this method of RS-SVM is to improve the prediction accuracy and of great prospect compare to the BP method.
Keywords
backpropagation; forecasting theory; power markets; prediction theory; rough set theory; support vector machines; BP method; RS-SVM; electricity market; electricity price forecasting; prediction models; rough set; support vector machines; Support vector machines; Electricity Markets; Electricity Price Forecasting; Rough Set; Support Vector Machine;
fLanguage
English
Publisher
ieee
Conference_Titel
Environmental Science and Information Application Technology (ESIAT), 2010 International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-7387-8
Type
conf
DOI
10.1109/ESIAT.2010.5567360
Filename
5567360
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