DocumentCode :
3494954
Title :
Improved Markov Residual Error to Long-Medium Power Load Forecast Based on SVM Method
Author :
Wei, Li ; Zhang Zhen-Gang ; Ning, Yan ; Jia-liang, Lv
Author_Institution :
Dept. of Econ. & Manage., North China Electr. Power Univ., Baoding
Volume :
1
fYear :
2009
fDate :
7-8 March 2009
Firstpage :
128
Lastpage :
132
Abstract :
The characteristics of small sample, stochastic growth and nonlinear wave are often combined with long-medium power load forecast series; SVM model could reflect the relationship between growing characteristics and nonlinear characteristics to the series effectively and make fitting calculation, on the other hand, Markov could well reflect randomness that produced by the system involve with many complex factors. Through establishment of a forecast model based on SVM algorithm, the series of historical load variables is rolling forecasted; an improved Markov error correction algorithm is introduced to modify the values forecasted by SVM, in order to make the increase of total forecasting precision to a maximum extent, a transfer matrix that make the forecast values to high stability and high accuracy is obtained. It is proved that the presented forecast method is superior obviously to traditional methods through empirical study, and it can be used generally.
Keywords :
Markov processes; load forecasting; matrix algebra; power engineering computing; support vector machines; SVM method; complex factors; improved Markov error correction algorithm; improved Markov residual error; long-medium power load forecast series; nonlinear wave; stochastic growth; transfer matrix; Economic forecasting; Energy management; Input variables; Load forecasting; Power generation economics; Power supplies; Power system modeling; Predictive models; Support vector machines; Technology management; Markov; SVM; power load forecast; residual error;
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.2009.38
Filename :
4958741
Link To Document :
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