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A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model

Author

Listed:
  • Bingchun Liu

    (Research Institute of Circular Economy, Tianjin University of Technology, Tianjin 300384, China)

  • Shijie Zhao

    (Research Institute of Circular Economy, Tianjin University of Technology, Tianjin 300384, China)

  • Xiaogang Yu

    (Research Institute of Circular Economy, Tianjin University of Technology, Tianjin 300384, China)

  • Lei Zhang

    (Research Institute of Circular Economy, Tianjin University of Technology, Tianjin 300384, China)

  • Qingshan Wang

    (School of Humanities, Tianjin Agricultural University, Tianjin 300384, China)

Abstract

Wind power generation is one of the renewable energy generation methods which maintains good momentum of development at present. However, its extremely intense intermittences and uncertainties bring great challenges to wind power integration and the stable operation of wind power grids. To achieve accurate prediction of wind power generation in China, a hybrid prediction model based on the combination of Wavelet Decomposition (WD) and Long Short-Term Memory neural network (LSTM) is constructed. Firstly, the nonstationary time series is decomposed into multidimensional components by WD, which can effectively reduce the volatility of the original time series and make them more stable and predictable. Then, the components of the original time series after WD are used as input variables of LSTM to predict the national wind power generation. Forty points were used, 80% as training samples and 20% as testing samples. The experimental results show that the MAPE of WD-LSTM is 5.831, performing better than other models in predicting wind power generation in China. In addition, the WD-LSTM model was used to predict the wind power generation in China under different development trends in the next two years.

Suggested Citation

  • Bingchun Liu & Shijie Zhao & Xiaogang Yu & Lei Zhang & Qingshan Wang, 2020. "A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model," Energies, MDPI, vol. 13(18), pages 1-17, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4964-:d:417269
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    References listed on IDEAS

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