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As an important clean and renewable kind of energy, wind power plays animportant role in coping with energy crisis and environmental pollution.However, the volatility and intermittency of wind speed restrict thedevelopment of wind power. To improve the utilization of wind power, this studyproposes a new wind speed prediction model based on data noise reductiontechnology, temporal convolutional network (TCN), and gated recurrent unit(GRU). Firstly, an adaptive data noise reduction algorithm P-SSA is proposedbased on singular spectrum analysis (SSA) and Pearson correlation coefficient.The original wind speed is decomposed into multiple subsequences by SSA andthen reconstructed. When the Pearson correlation coefficient between thereconstructed sequence and the original sequence is greater than 0.99, othernoise subsequences are deleted to complete the data denoising. Then, thereceptive field of the samples is expanded through the causal convolution anddilated convolution of TCN, and the characteristics of wind speed change areextracted. Then, the time feature information of the sequence is extracted byGRU, and then the wind speed is predicted to form the wind speed sequenceprediction model of P-SSA-TCN-GRU. The proposed model was validated on threewind farms in Shandong Province. The experimental results show that theprediction performance of the proposed model is better than that of thetraditional model and other models based on TCN, and the wind speed predictionof wind farms with high precision and strong stability is realized. The windspeed predictions of this model have the potential to become the data thatsupport the operation and management of wind farms. The code is available atlink.

Ultra-short-term multi-step wind speed prediction for wind farms based on adaptive noise reduction technology and temporal convolutional network