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High resolution wind speed forecasting based on wavelet decomposed phase space reconstruction and self-organizing map

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  • Hu, Rui
  • Hu, Weihao
  • Gökmen, Nuri
  • Li, Pengfei
  • Huang, Qi
  • Chen, Zhe

Abstract

Wind power has already brought lots of benefits for energy market and environment. To promote the utilization of wind energy further and achieve the ambitious goals of applying sustainable energy, the improvement of the performance of wind speed forecasting will play a significant role. The whole system will benefit from an accurate wind forecasting system with high resolution. This paper focused on short-term wind speed forecasting and proposed a method providing the wind speed forecasting with short-term horizon and high resolution. In this paper, a novel method based on the wavelet decomposition, the phase space reconstruction and the self-organizing map (SOM) artificial neural network was proposed. Study case was provided to demonstrate the performance of the proposed forecasting method. Comparative results from several classic forecasting methods were provided as well. Based on the outcomes of study cases and comparison results, it can be concluded that the proposed method performed well in short term forecasting horizon.

Suggested Citation

  • Hu, Rui & Hu, Weihao & Gökmen, Nuri & Li, Pengfei & Huang, Qi & Chen, Zhe, 2019. "High resolution wind speed forecasting based on wavelet decomposed phase space reconstruction and self-organizing map," Renewable Energy, Elsevier, vol. 140(C), pages 17-31.
    • Handle: RePEc:eee:renene:v:140:y:2019:i:c:p:17-31
    DOI: 10.1016/j.renene.2019.03.041
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    References listed on IDEAS

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    Cited by:

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    10. Wang, Han & Yan, Jie & Han, Shuang & Liu, Yongqian, 2020. "Switching strategy of the low wind speed wind turbine based on real-time wind process prediction for the integration of wind power and EVs," Renewable Energy, Elsevier, vol. 157(C), pages 256-272.
    11. Wang, Yun & Xu, Houhua & Song, Mengmeng & Zhang, Fan & Li, Yifen & Zhou, Shengchao & Zhang, Lingjun, 2023. "A convolutional Transformer-based truncated Gaussian density network with data denoising for wind speed forecasting," Applied Energy, Elsevier, vol. 333(C).
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