Direct Interval Forecasting of Wind Power

Can Wan, Zhao Xu, Pierre Pinson

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

This letter proposes a novel approach to directly formulate the prediction intervals of wind power generation based on extreme learning machine 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
JournalI E E E Transactions on Power Systems
Volume28
Issue number4
Pages (from-to)4877-4878
ISSN0885-8950
DOIs
Publication statusPublished - 2013

Keywords

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

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