  As an important clean and renewable kind of energy, wind power plays an
important role in coping with energy crisis and environmental pollution.
However, the volatility and intermittency of wind speed restrict the
development of wind power. To improve the utilization of wind power, this study
proposes a new wind speed prediction model based on data noise reduction
technology, temporal convolutional network (TCN), and gated recurrent unit
(GRU). Firstly, an adaptive data noise reduction algorithm P-SSA is proposed
based on singular spectrum analysis (SSA) and Pearson correlation coefficient.
The original wind speed is decomposed into multiple subsequences by SSA and
then reconstructed. When the Pearson correlation coefficient between the
reconstructed sequence and the original sequence is greater than 0.99, other
noise subsequences are deleted to complete the data denoising. Then, the
receptive field of the samples is expanded through the causal convolution and
dilated convolution of TCN, and the characteristics of wind speed change are
extracted. Then, the time feature information of the sequence is extracted by
GRU, and then the wind speed is predicted to form the wind speed sequence
prediction model of P-SSA-TCN-GRU. The proposed model was validated on three
wind farms in Shandong Province. The experimental results show that the
prediction performance of the proposed model is better than that of the
traditional model and other models based on TCN, and the wind speed prediction
of wind farms with high precision and strong stability is realized. The wind
speed predictions of this model have the potential to become the data that
support the operation and management of wind farms. The code is available at
https://github.com/JethroJames/Wind-Speed-Forecast-TCN_GRU
