Journal of Northeastern University Natural Science ›› 2016, Vol. 37 ›› Issue (2): 153-156.DOI: 10.12068/j.issn.1005-3026.2016.02.001

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A Parameter Self-tuning Model for Wind Power Prediction

ZHAI Jun-chang1, GE Yan-feng2, LIANG Peng3, GAO Li-qun1   

  1. 1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China; 2. Liaoning Electric Power Company Limited,Shenyang 110006,China; 3. Jinzhou City Power Supply Company, State Grid Liaoning Electric Power Company Limited, Jinzhou 121000, China.
  • Received:2014-12-12 Revised:2014-12-12 Online:2016-02-15 Published:2016-02-18
  • Contact: ZHAI Jun-chang
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Abstract: To improve the prediction accuracy of wind power, a parameter self-tuning model is proposed. The sliding window weighted recursive least square (SWWRLS) method is used to establish the wind power prediction model, which emphasizes most recent data in the prediction, ruling out the interference of historical data. Less memory space is used in the weighted recursive model, and the adaptability of the prediction model to the external environment data is increased. Finally, the simulation is carried out on the real historical data of the wind farm in Liaoning with support vector machine, Kalman filter and SWWRLS methods, separately, and the simulation results show the superiority of the proposed method.

Key words: wind power prediction, least square method, sliding window, support vector machine, Kalman filter

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