Wind power prediction is mainly divided into short-term series prediction and medium-long-term series prediction [7]. Taking the time resolution of 15 min as an example, the short-term mode mainly predicts the wind power changes in the next few hours, while the medium-long-term mode predicts the wind power changes in the next few days [8]. Wind power prediction methods are mainly composed of physical methods, mathematical methods, and artificial intelligence methods. Physical methods use the aerodynamic factors of terrain and weather to establish the corresponding physical model, combining the real-time weather information [9; 10; 11]. Although this method has the highest accuracy in theory, its high complexity and poor robustness limit its application in practical engineering. Mathematical methods mainly use mathematical statistical tools to make mathematical analysis and regression predictions of collected data [12; 13]. However, this approach lacks universality, as it only attains average levels of accuracy and heavily relies on mathematical computations. With the rise of deep learning, artificial intelligence prediction methods based on neural network have attracted the attention of experts worldwide. The introduction and deployment of smart grid technology facilitate the acquisition of vast quantities of data and information. The abundance of such data ensures the accuracy and generalizability of artificial intelligence methodologies [14; 15]. Relying on a large number of network structures, deep learning is designed to conduct deep feature mining on historical data, find out hidden and universal key information, and then realize prediction through feature decoding [16]. Reference [17] used Arima for wind power prediction. It has the advantages of simple structure and easy fitting, but it has difficulties in dealing with non-stationary stochastic process. Moreover, this method has poor performance in handling multi-feature data sources. Reference [18] used Variational Modal Decomposition (VMD) to decompose wind power into fluctuating and random components, reducing the randomness of power, and uses Long Short-Term Memory (LSTM) for power prediction separately. Although good results have been achieved in short-term forecasting, when confronted with the long-term prediction task, significant computational burdens arise, making it susceptible to memory decay or forgetting. Reference [19] utilized an attention mechanism to avoid feature loss in the encoding process of the LTSTM algorithm. However, the information from multi-feature data sources is not fully utilized, and there is still the limitation of Receptive field. In addition, this method does not address the drawbacks of memory decline in medium-long-term prediction tasks. Reference [20] improved the power prediction accuracy through Feature engineering technology and a gradient lifting tree algorithm based on the Numerical Weather Prediction (NWP) system. Although this method can utilize NWP-related data, it cannot achieve efficient feature mining. The lack of attention mechanism also makes it difficult for the model to converge. Reference [21] considered wind speed correlation and used Gaussian regression for data correction to achieve power prediction. Although this can improve the accuracy of prediction, there are drawbacks such as high computational complexity, high sampling requirements, and difficulty in parameter selection. Reference [22] used fusion models for multi-model prediction using the Stacking framework. This method only integrates multiple models with weights and lacks theoretical innovation. In addition, Data breach and over fitting are serious problems in cross-training. In summary, traditional prediction models are limited by the strong randomness and volatility in the data themselves, which results in poor performance in medium- to long-term wind power prediction tasks. Based on the above motivations, we propose a prediction model based on a Gated Transformer. In Section 2, data analysis and processing is completed.