Short-term load forecasting via ARMA model identification including non-Gaussian process considerations

被引：489

作者：

Huang, SJ ^{[1
]}

Shih, KR ^{[1
]}

机构：

[1] Natl Cheng Kung Univ, Dept Elect Engn, Tainan 70101, Taiwan

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2003年 / 18卷 / 02期

关键词：

bispectrum; cumulant; non-Gaussian process; short-term load forecast;

D O I：

10.1109/TPWRS.2003.811010

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, the short-term load forecast by use of autoregremive moving average (ARMA) model including non-Gaussian process considerations is proposed. In the proposed method, the concept of cumulant and bispectrum are embedded into the ARMA model in order to facilitate Gaussian and non-Gaussian process. With embodiment of a Gaussianity verification procedure, the forecasted model is identified more appropriately. Therefore, the performance of ARMA model is better ensured, improving the load forecast accuracy significantly. The proposed method has been applied on a practical system and the results are compared with other published techniques.

引用

页码：673 / 679

页数：7

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