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Wind Speed Short-Term Prediction Based on Empirical Wavelet Transform, Recurrent Neural Network and Error Correction

基于经验小波变换、 循环神经网络和误差校正的短期风速预测

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Abstract

Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind energy. However, owing to the stochastic and intermittent of wind speed, predicting wind speed accurately is difficult. A new hybrid deep learning model based on empirical wavelet transform, recurrent neural network and error correction for short-term wind speed prediction is proposed in this paper. The empirical wavelet transformation is applied to decompose the original wind speed series. The long short term memory network and the Elman neural network are adopted to predict low-frequency and high-frequency wind speed sub-layers respectively to balance the calculation efficiency and prediction accuracy. The error correction strategy based on deep long short term memory network is developed to modify the prediction errors. Four actual wind speed series are utilized to verify the effectiveness of the proposed model. The empirical results indicate that the method proposed in this paper has satisfactory performance in wind speed prediction.

摘要

准确预测风速对于保证风电系统的稳定运行和提高风能利用率至关重要. 然而, 由于风速的随机性和间歇性, 很难实现准确的风速预测. 本文提出一种基于经验小波变换、 循环神经网络和误差校正的新型混合式深度学习模型进行短期风速预测. 该模型采用经验小波变换对原始风速序列进行分解; 使用长短期记忆网络和Elman神经网络分别预测低频和高频分量, 以平衡计算效率和预测精度; 提出一种深度长短期记忆网络对预测误差进行修正. 使用4个风速序列验证了该模型的有效性. 实验结果表明, 该方法在风速预测方面具有良好的性能.

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Authors and Affiliations

  1. School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China

    Changsheng Zhu  (朱昶胜) & Lina Zhu  (朱丽娜)

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  1. Changsheng Zhu  (朱昶胜)
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  2. Lina Zhu  (朱丽娜)
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Corresponding author

Correspondence to Lina Zhu  (朱丽娜).

Additional information

Foundation item: the Gansu Province Soft Scientific Research Projects (No. 2015GS06516), and the Funds for Distinguished Young Scientists of Lanzhou University of Technology, China (No. J201304)

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Zhu, C., Zhu, L. Wind Speed Short-Term Prediction Based on Empirical Wavelet Transform, Recurrent Neural Network and Error Correction. J. Shanghai Jiaotong Univ. (Sci.) 29, 297–308 (2024). https://doi.org/10.1007/s12204-022-2477-7

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  • DOI: https://doi.org/10.1007/s12204-022-2477-7

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