DocumentCode
3364750
Title
The Error Correcting Markov Chains Study on Medium/Long Term Load Rolling Forecasting of SVM Based on Grey Relational Grade
Author
Niu, Dong-xiao ; Zhang, Yuan ; Lv, Jia-liang ; Jia, Jian-rong
Author_Institution
Sch. of Bus. Adm., NCEPU, Beijing
fYear
2008
fDate
4-6 Nov. 2008
Firstpage
233
Lastpage
238
Abstract
According to the low sample and multifactor impact for long-medium term power load forecasting, the grey relational grade was used in screening factors, the combined model in BP neural network and SVM was established, and the multivariate variables and history load variables were used to roll prediction. The combined predictive values are obviously better than single method. Empirical study showed that the method in this paper is superior to conventional method, so it is worth to be extended and applied.
Keywords
Markov processes; backpropagation; grey systems; load forecasting; neural nets; power engineering computing; support vector machines; BP neural network; SVM; error correcting Markov chains; grey relational grade; multivariate variables; power load rolling forecasting; Economic forecasting; Error correction; Load forecasting; Power generation economics; Power supplies; Predictive models; Research and development management; Risk management; Support vector machines; Uncertainty; Markov chains; Med-long Power load forecasting; SVM; grey relational grade;
fLanguage
English
Publisher
ieee
Conference_Titel
Risk Management & Engineering Management, 2008. ICRMEM '08. International Conference on
Conference_Location
Beijing
Print_ISBN
978-0-7695-3402-2
Type
conf
DOI
10.1109/ICRMEM.2008.31
Filename
4673232
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