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

Shyh Jier Huang, Kuang Rong Shih

Research output: Contribution to journalArticle

381 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)673-679
Number of pages7
JournalIEEE Transactions on Power Systems
Volume18
Issue number2
DOIs
Publication statusPublished - 2003 May 1

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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