Short-horizon prediction of wind power : A data-driven approach

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

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Author(s)

Detail(s)

Original languageEnglish
Article number5451084
Pages (from-to)1112-1122
Journal / PublicationIEEE Transactions on Energy Conversion
Volume25
Issue number4
Publication statusPublished - Dec 2010
Externally publishedYes

Abstract

This paper discusses short-horizon prediction of wind speed and power using wind turbine data collected at 10 s intervals. A time-series model approach to examine wind behavior is studied. Both exponential smoothing and data-driven models are developed for wind prediction. Power prediction models are established, which are based on the most effective wind prediction model. Comparative analysis of the power predicting models is discussed. Computational results demonstrate performance advantages provided by the data-driven approach. All computations reported in the paper are based on the data collected at a large wind farm. © 2006 IEEE.

Research Area(s)

  • Data mining, evolutionary strategy (ES) algorithm, exponential smoothing, neural networks (NNs), power prediction, time-series model, wind speed prediction