Ultra-short-term wind power forecasting based on TCN-Wpsformer hybrid model

Aiming at the problem that the recurrent neural network based on gradient descent is hard to capture the long-term dependence relationship of wind power with a long time span,an ultra-short-term wind power forecasting method based on the hybrid model of temporal convolutional network(TCN) and window probability sparse Transformer(Wpsformer) is proposed. The time encoding containing the seasonal characteristic and the absolute positional encoding containing the positional information of original data are spliced,the TCN is introduced to extract the time segment features,the time segment features are integrated into the self-attention mechanism,and the relationship between time points is replaced by the relationship between time segments. The ultra-short-term wind power forecasting values are output in multiple stages through the Wpsformer model,compared with the original Transformer model,the Wpsformer model employs the window probability sparse self-attention mechanism to screen out the relative important time segment features for calculation while capture the long-term dependence relationship,which improves the forecasting accuracy and reduces the computational cost. The example results of Caodian wind farm show that the proposed model has obvious advantages in forecasting accuracy. The necessity of each module of the proposed model is verified by the ablation experiment. © 2024 Electric Power Automation Equipment Press. All rights reserved.