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Multi-Step Wind Speed Forecasting Based On Ensemble Empirical Mode Decomposition, Long Short Term Memory Network and Error Correction Strategy

Author

Listed:
  • Yuansheng Huang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Lei Yang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Shijian Liu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

  • Guangli Wang

    (State Grid Jibei Electric Power Company Engineering Management Company, Beijing 100070, China)

Abstract

It is of great significance for wind power plant to construct an accurate multi-step wind speed prediction model, especially considering its operations and grid integration. By integrating with a data pre-processing measure, a parameter optimization algorithm and error correction strategy, a novel forecasting method for multi-step wind speed in short period is put forward in this article. In the suggested measure, the EEMD (Ensemble Empirical Mode Decomposition) is applied to extract a series of IMFs (intrinsic mode functions) from the initial wind data sequence; the LSTM (Long Short Term Memory) measure is executed as the major forecasting method for each IMF; the GRNN (general regression neural network) is executed as the secondary forecasting method to forecast error sequences for each IMF; and the BSO (Brain Storm Optimization) is employed to optimize the parameter for GRNN during the training process. To verify the validity of the suggested EEMD-LSTM-GRNN-BSO model, eight models were applied on three different wind speed sequences. The calculation outcomes reveal that: (1) the EEMD is able to boost the wind speed prediction capacity and robustness of the LSTM approach effectively; (2) the BSO based parameter optimization method is effective in finding the optimal parameter for GRNN and improving the forecasting performance for the EEMD-LSTM-GRNN model; (3) the error correction method based on the optimized GRNN promotes the forecasting accuracy of the EEMD-LSTM model significantly; and (4) compared with all models involved, the proposed EEMD-LSTM-GRNN-BSO model is proved to have the best performance in predicting the short-term wind speed sequence.

Suggested Citation

  • Yuansheng Huang & Lei Yang & Shijian Liu & Guangli Wang, 2019. "Multi-Step Wind Speed Forecasting Based On Ensemble Empirical Mode Decomposition, Long Short Term Memory Network and Error Correction Strategy," Energies, MDPI, vol. 12(10), pages 1-22, May.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1822-:d:230867
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    Cited by:

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    2. Ding, Lin & Bai, Yulong & Liu, Ming-De & Fan, Man-Hong & Yang, Jie, 2022. "Predicting short wind speed with a hybrid model based on a piecewise error correction method and Elman neural network," Energy, Elsevier, vol. 244(PA).
    3. Xin Zhao & Haikun Wei & Chenxi Li & Kanjian Zhang, 2020. "A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed," Energies, MDPI, vol. 13(7), pages 1-15, April.
    4. Xianxu Huo & Ke Xu & Ruixin Liu & Xi Chen & Zhanchun Li & Haiyun Yan, 2019. "A Structure-Reconfigurable Soft-Switching DC-DC Converter for Wide-Range Applications," Energies, MDPI, vol. 12(15), pages 1-25, July.
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    6. Qin Chen & Yan Chen & Xingzhi Bai, 2020. "Deterministic and Interval Wind Speed Prediction Method in Offshore Wind Farm Considering the Randomness of Wind," Energies, MDPI, vol. 13(21), pages 1-23, October.

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