DocumentCode :
2336580
Title :
Short term load forecasting model using support vector machine based on artificial neural network
Author :
Niu, Dong-xiao ; Qiang Wanq ; Li, Jin-Chao
Author_Institution :
Sch. of Bus. Adm., North China Electr. Power Univ., Baoding, China
Volume :
7
fYear :
2005
fDate :
18-21 Aug. 2005
Firstpage :
4260
Abstract :
A new sample preprocessing method is put forward in this paper. Firstly, the data points are classified into three types as the following: the high load type, the medium load type and the low load type; then, the artificial neural network is adopted to forecast the load type of the predict point; finally a support vector machine forecasting model is created on the basis of data points whose load type is the same as the predict point. It is the first time for artificial neural network to be combined with support vector machine in short term load forecasting. The practical examples show that the model established in this paper is better than other methods in forecasting accuracy and computing speed.
Keywords :
forecasting theory; load forecasting; neural nets; pattern classification; support vector machines; artificial neural network; data point classification; load type classification; sample preprocessing; short term load forecasting; support vector machine; Artificial intelligence; Artificial neural networks; Economic forecasting; Load forecasting; Load modeling; Machine learning algorithms; Neural networks; Predictive models; Support vector machine classification; Support vector machines; Short term load forecasting; artificial neural network; load type classification; support vector machine;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
Type :
conf
DOI :
10.1109/ICMLC.2005.1527685
Filename :
1527685
Link To Document :
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