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Wind power day-ahead prediction with cluster analysis of NWP

Author

Listed:
  • Dong, Lei
  • Wang, Lijie
  • Khahro, Shahnawaz Farhan
  • Gao, Shuang
  • Liao, Xiaozhong

Abstract

The selection of training data for establishing a model directly affects the prediction precision. Wind power has the characteristic of daily similarity. The corresponding meteorological data also has the characteristic of daily similarity. This paper proposes a new model with cluster analysis of the numerical weather prediction information. The similar day with the predicted day is searched as training sample to a generalized regression neural network model. The numerical weather prediction data and actual wind power data from a wind farm are used in this study to test the model. The prediction results show that correct cluster analysis method is a useful tool in day-ahead wind power prediction.

Suggested Citation

  • Dong, Lei & Wang, Lijie & Khahro, Shahnawaz Farhan & Gao, Shuang & Liao, Xiaozhong, 2016. "Wind power day-ahead prediction with cluster analysis of NWP," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1206-1212.
    • Handle: RePEc:eee:rensus:v:60:y:2016:i:c:p:1206-1212
    DOI: 10.1016/j.rser.2016.01.106
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    References listed on IDEAS

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