SHORT-TERM PREDICTION FOR NON-STATIONARY WIND SPEEDS BY INTEGRATING THE ERROR CORRECTION AND RD-CNN-BILSTM NEURAL NETWORK COMBINED MODEL

Hu Peng; Yao Jiawen; Han Yan; Zhou Pinhan; Yang Fei; Zeng Youyi

doi:10.19912/j.0254-0096.tynxb.2024-1797

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (3) : 243-252. DOI: 10.19912/j.0254-0096.tynxb.2024-1797

SHORT-TERM PREDICTION FOR NON-STATIONARY WIND SPEEDS BY INTEGRATING THE ERROR CORRECTION AND RD-CNN-BILSTM NEURAL NETWORK COMBINED MODEL

Hu Peng¹, Yao Jiawen¹, Han Yan¹, Zhou Pinhan¹, Yang Fei^1,2, Zeng Youyi¹

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Abstract

To improve the accuracy of short-term prediction for non-stationary wind speeds, a rolling decomposition （RD） and prediction method was built by using a sliding window. By comparing the predictive results of various neural network models and incorporating the variational mode decomposition（VMD） algorithm, an RD-CNN-BiLSTM neural network combined model was proposed for predicting non-stationary wind speed sequences. Furthermore, addressing the predictive accuracy limitations of the neural network combined model, a short-term predictive approach for non-stationary wind speeds that integrates the error correction and RD-CNN-BiLSTM neural network combined model was proposed. The research results indicate that applying sliding windows to the actual wind speed data and carrying out a single prediction and extension, followed by decomposition, trimming, and reconstruction of the extended data, the impact of boundary effects can be effectively weakened and the information leakage can be prevented. The CNN-BiLSTM combined model outperforms traditional individual neural network models, such as autoregressive integrated moving average（ARIMA）, back propagation（BP）, long short-term memory（LSTM）, and bidirectional long short-term memory （BiLSTM） models, in terms of the predictive accuracy. It was found that the quadratic parabola error correction method is superior to the linear error correction method, particularly in high wind speed ranges, where the predictive accuracy improvement by the former method is especially significant. The predictive results from actual wind speed data at different time intervals further validate that the proposed approach of integrating error correction and RD-CNN-BiLSTM neural network combined model has high predictive accuracy and generalization in short-term prediction of non-stationary wind speeds.

Key words

non-stationary wind speed / short-term prediction / convolutional neural network / information leakage / sliding window / error correction

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Hu Peng, Yao Jiawen, Han Yan, Zhou Pinhan, Yang Fei, Zeng Youyi. SHORT-TERM PREDICTION FOR NON-STATIONARY WIND SPEEDS BY INTEGRATING THE ERROR CORRECTION AND RD-CNN-BILSTM NEURAL NETWORK COMBINED MODEL[J]. Acta Energiae Solaris Sinica. 2026, 47(3): 243-252 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1797

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