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

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

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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.

wind speed predictionempirical wavelet transformdeep long short term memory networkElman neural networkerror correction strategy

朱昶胜、朱丽娜

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

Gansu Province Soft Scientific Research ProjectsFunds for Distinguished Young Scientists of Lanzhou University of Technology,China

2015GS06516J201304

2024

10.1007/s12204-022-2477-7

上海交通大学学报(英文版)
上海交通大学

上海交通大学学报(英文版)

影响因子：0.151
ISSN：1007-1172
年,卷(期)：2024.29(2)
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