Accurately predicting wind power is of great significance for improving the efficiency and safety of the power system, while the intermittence and randomness of wind energy make it difficult to predict wind power accurately. Therefore, an improved wind power prediction model based on Informer, namely PCI-Informer (PATCH-CNN-IRFFN-Informer) is proposed. The sequence data is divided into subsequence-level patches for feature extraction and integration, which improves the model’s ability to process sequence data and its effectiveness. Multiple-scale causal convolution self-attention mechanism is used to achieve multi-scale local feature fusion, which enhances the model’s understanding and modeling ability of local information. The inverse residual feedforward network (IRFFN) is introduced to enhance the model’s ability to extract and preserve local structural information. Experiment verification is conducted using data from a wind farm, and the results show that compared with mainstream prediction models, the PCI-Informer model achieves better prediction performance at different prediction time steps, with an average reduction of 11.1% in MAE compared with the Informer model, effectively improving the short-term wind power prediction accuracy.