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Journal of Shanghai Jiaotong University(Science) >

2024 , Vol. 29 >Issue 2: 297 - 308

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

Energy and Power Engineering

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

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  • (School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China)

Received date: 2020-11-12

  Accepted date: 2021-04-02

  Online published: 2024-03-28

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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 highfrequency 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.

Key words： wind speed prediction, empirical wavelet transform, deep long short term memory network, Elman neural network, error correction strategy

Cite this article

ZHU Changsheng(朱昶胜), ZHU Lina∗ (朱丽娜) . Wind Speed Short-Term Prediction Based on Empirical Wavelet Transform, Recurrent Neural Network and Error Correction[J]. Journal of Shanghai Jiaotong University(Science), 2024 , 29(2) : 297 -308 . DOI: 10.1007/s12204-022-2477-7

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