Direct Interval Forecasting of Wind Power

Can Wan, Zhao Xu, Pierre Pinson

Research output: Contribution to journalJournal articleResearchpeer-review


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
Issue number4
Pages (from-to)4877-4878
Publication statusPublished - 2013


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

Fingerprint Dive into the research topics of 'Direct Interval Forecasting of Wind Power'. Together they form a unique fingerprint.

Cite this