Bus load short-term forecast based on LSSVM and Markov chain

被引：0
|
作者：
Li, Guang-Zhen [1 ]
Liu, Wen-Ying [1 ]
机构：
[1] School of Electric and Electronic Engineering, North China Electric Power University, Beijing 102206, China
来源：
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2010年 / 38卷 / 11期
关键词：
Markov processes - Forecasting - Learning algorithms - Least squares approximations - Chains - Support vector machines;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
A hybrid method is proposed for bus load short-term forecast. It firstly adopts the least squares support vector machines(LSSVM)method to forecast bus load and puts forward a generalized grid-search algorithm to optimize the selection of model parameter. Then regarding the series of history forecast error as a process of Markov chain, it adopts the Markov chain method to forecast the potential forecast error produced by LSSVM model. At last, it adopts the results from Markov Chain Method to modify the results from LSSVM and get the final forecast load. The case study in the end of this paper proves that the proposed hybrid method is available to satisfactory forecast accuracy.
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页码：55 / 59
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