Optimal prediction intervals of wind power generation

Can Wan, Zhao Xu, Pierre Pinson, Zhao Yang Dong, Kit Po Wong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

301 Citations (Scopus)


Accurate and reliable wind power forecasting is essential to power system operation. Given significant uncertainties involved in wind generation, probabilistic interval forecasting provides a unique solution to estimate and quantify the potential impacts and risks facing system operation with wind penetration beforehand. This paper proposes a novel hybrid intelligent algorithm approach to directly formulate optimal prediction intervals of wind power generation based on extreme learning machine and particle swarm optimization. Prediction intervals with associated confidence levels are generated through direct optimization of both the coverage probability and sharpness to ensure the quality. The proposed method does not involve the statistical inference or distribution assumption of forecasting errors needed in most existing methods. Case studies using real wind farm data from Australia have been conducted. Comparing with benchmarks applied, experimental results demonstrate the high efficiency and reliability of the developed approach. It is therefore convinced that the proposed method provides a new generalized framework for probabilistic wind power forecasting with high reliability and flexibility and has a high potential of practical applications in power systems.
Original languageEnglish
Article number6662465
Pages (from-to)1166-1174
Number of pages9
JournalIEEE Transactions on Power Systems
Issue number3
Publication statusPublished - 1 Jan 2014


  • Extreme learning machine
  • forecasts
  • particle swarm optimization
  • prediction intervals
  • wind power

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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