Probabilistic forecasting of wind power generation using extreme learning machine

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

442 Citations (Scopus)

Abstract

Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be accurate, leading to increased uncertainties and risks for system operation. This paper proposes an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrap methods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified with the best performance. Consequently, a new method for prediction intervals formulation based on the ELM and the pairs bootstrap is developed. Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results demonstrate that the proposed method is effective for probabilistic forecasting of wind power generation with a high potential for practical applications in power systems.
Original languageEnglish
Article number6665108
Pages (from-to)1033-1044
Number of pages12
JournalIEEE Transactions on Power Systems
Volume29
Issue number3
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • Bootstrap
  • extreme learning machine (ELM)
  • forecasting
  • prediction interval
  • wind power

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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