Research on hybrid ARIMA and support vector machine model in short term load forecasting

被引：0
|
作者：
He, Yujun [1 ]
Zhu, Youchan [2 ]
Duan, Dongxing [1 ]
机构：
[1] North China Elect Power Univ, Dept Elect & Commun Engn, Hebei 071003, Peoples R China
[2] North China Elect Power Univ, Ctr Informat & Network Management, Hebei 071003, Peoples R China
来源：
ISDA 2006: SIXTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, VOL 1 | 2006年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In power system, due to the complexity of the historical load data and the randomness of a lot of uncertain factors influence, the observed historical data showed linear and nonlinear characteristics. As we all known, the autoregressive integrated moving average (ARIMA) is one of the popular linear models in time series forecasting, and the SVM model is the recent research trend successfully used in solving nonlinear regression and time series. problem. So in this paper, a hybrid methodology that combines both ARIAM and SVM model is presented to take. advantage of the unique strength of ARIAM and SVM models in linear and nonlinear modeling. The linear pattern of the historical load data can be dealt with ARIMA, and the nonlinear part with SVM model. The effectiveness of the model has been tested using Hebei province daily load data with satisfactory results. The experimental results showed that the hybrid model can effectively improve the forecasting accuracy achieved by either of the models used separately.
引用
收藏
页码：804 / 808
页数：5
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