Short term probabilistic forecasting of the magnitude and timing of extreme load

Xinyu Chen, Chongqing Kang, Minjie Chen

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

As the foundation of system daily scheduling and operations, current deterministic forecasting algorithm of the magnitude and timing of extreme load is not satisfactory. Probabilistic forecasting is an effective way to reduce the risk of inaccurate forecasting of extreme load. This paper took peak load as an example, analyzed the multi sub-peaks characteristic of load curve, studied the statistical features of the peak load magnitude and timing, established the regression model between peak load occurrence time and sunset time; based on sorted statistics of peak load daily increments by weeks and seasons, the paper forecasted the probabilistic density functions (PDF) of sub-peak load magnitudes, calculated the PDF of the peak load magnitude via sequence operation theories, forecasted the timing PDF employing total probability formula. Method proposed in this paper has been applied to a city in North China and the results prove the effectiveness of this method.

Original languageEnglish (US)
Pages (from-to)64-72
Number of pages9
JournalZhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering
Volume31
Issue number22
StatePublished - Aug 5 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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