A spatiotemporal wind power prediction based on wavelet decomposition, feature selection, and localized prediction

N. Safari, Y. Chen, B. Khorramdel, L. P. Mao, C. Y. Chung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)

Abstract

Wind power possesses a high level of non-linearity and non-stationarity which are the main barriers to developing an accurate wind power prediction (WPP). In this regard, a multiresolution wavelet decomposition (WD), based on discrete wavelet transform, is employed to decompose the wind power time series (TS) into several components. Afterward, in a feature selection (FS) stage, which benefits from the spatiotemporal relation among the wind farms, the double input symmetrical relevance (DISR) has been adopted to find the most suitable features in predicting each component. Then, to have a high-accuracy prediction with an affordable computation time, localized prediction engines have been used to predict each component. The final WPP value is obtained by superposition of all the predicted values corresponding to components. The proposed spatiotemporal WPP is evaluated using the wind power generation historical data in Saskatchewan, Canada. The performance of the proposed WPP is compared with other well-developed and widely-used WPP models. Various evaluation indices have been utilized for conducting the performance evaluation.

Original languageEnglish
Title of host publication2017 IEEE Electrical Power and Energy Conference, EPEC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538608173
DOIs
Publication statusPublished - 2 Jul 2017
Externally publishedYes
Event2017 IEEE Electrical Power and Energy Conference, EPEC 2017 - Saskatoon, Canada
Duration: 22 Oct 201725 Oct 2017

Publication series

Name2017 IEEE Electrical Power and Energy Conference, EPEC 2017
Volume2017-October

Conference

Conference2017 IEEE Electrical Power and Energy Conference, EPEC 2017
Country/TerritoryCanada
CitySaskatoon
Period22/10/1725/10/17

Keywords

  • Prediction
  • time series
  • wavelet kernel
  • wind power generation

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology

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