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Short-term prediction of wind power with a clustering approach

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  • Kusiak, Andrew
  • Li, Wenyan

Abstract

A clustering approach is presented for short-term prediction of power produced by a wind turbine at low wind speeds. Increased prediction accuracy of wind power to be produced at future time periods is often bounded by the prediction model complexity and computational time involved. In this paper, a trade-off between the two conflicting objectives is addressed. First, a set of the most relevant parameters (predictors) is selected using the underlying physics and pattern immersed in data. Five scenarios of the input space are created with the k-means clustering algorithm. The most promising clustering scenario is applied to produce a model for each clustered subspace. Computational results are compared and the benefits of cluster–specific (customized) models are discussed. The results show that the prediction accuracy is improved the input space is clustered and customized prediction models are developed.

Suggested Citation

  • Kusiak, Andrew & Li, Wenyan, 2010. "Short-term prediction of wind power with a clustering approach," Renewable Energy, Elsevier, vol. 35(10), pages 2362-2369.
    • Handle: RePEc:eee:renene:v:35:y:2010:i:10:p:2362-2369
    DOI: 10.1016/j.renene.2010.03.027
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    References listed on IDEAS

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    1. Carolin Mabel, M. & Fernandez, E., 2008. "Analysis of wind power generation and prediction using ANN: A case study," Renewable Energy, Elsevier, vol. 33(5), pages 986-992.
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    Cited by:

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    14. Goh, H.H. & Lee, S.W. & Chua, Q.S. & Goh, K.C. & Teo, K.T.K., 2016. "Wind energy assessment considering wind speed correlation in Malaysia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1389-1400.
    15. Bigdeli, Nooshin & Afshar, Karim & Gazafroudi, Amin Shokri & Ramandi, Mostafa Yousefi, 2013. "A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, Canada," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 20-29.
    16. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
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