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
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;
Conference_Titel :
Environmental Science and Information Application Technology (ESIAT), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-7387-8
DOI :
10.1109/ESIAT.2010.5567360
