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

Shyh Jier Huang, Kuang Rong Shih

研究成果: Article同行評審

430 引文 斯高帕斯(Scopus)

摘要

In this paper, the short-term load forecast by use of autoregressive 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.

原文English
頁(從 - 到)673-679
頁數7
期刊IEEE Transactions on Power Systems
18
發行號2
DOIs
出版狀態Published - 2003 五月

All Science Journal Classification (ASJC) codes

  • 能源工程與電力技術
  • 電氣與電子工程

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