Direct interval forecasting of wind power

Can Wan, Zhao Xu, Pierre Pinson

Research output: Journal article publicationJournal articleAcademic researchpeer-review

86 Citations (Scopus)

Abstract

This letter proposes a novel approach to directly formulate the prediction intervals of wind power generation based on extreme learningmachine and particle swarm optimization, where prediction intervals are generated through direct optimization of both the coverage probability and sharpness, without the prior knowledge of forecasting errors. The proposed approach has been proved to be highly efficient and reliable through preliminary case studies using real-world wind farm data, indicating a high potential of practical application.
Original languageEnglish
Pages (from-to)4877-4878
Number of pages2
JournalIEEE Transactions on Power Systems
Volume28
Issue number4
DOIs
Publication statusPublished - 10 May 2013

Keywords

  • Extreme learning machine
  • Forecasting
  • Particle swarm optimization
  • Prediction interval
  • Wind power

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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