## 6 Conclusions  
In this paper, a wind power prediction model based on a Gated Transformer is proposed. Firstly, the characteristics of wind power data are analyzed, and NWP information is introduced as auxiliary information to support prediction. Then, through data preprocessing, the data quality is improved. Secondly, the classical Transformer model is optimized by using a gating unit optimization, dilated convolution feature fusion, multi-head attention focusing, and Decoder input guidance. Finally, the effectiveness and reliability of the scheme are verified by a large number of comparative experiments. The main contributions of this paper can be summarized as follows:  
1. The correlation between wind power data and NWP data is analyzed, which qualitatively explains the deficiency of feature extraction methods which only use historical data and demonstrates the necessity of introducing NWP data. Through data preprocessing, the data quality is improved;
2. Optimization of the classic Transformer structure is performed. The gating residual element is added to improve the feature focusing level between modules in series. By adding the dilated convolution module, multi-dimensional feature fusion is realized in a larger receptive field. The Encoder and Decoder input modules are redesigned, and historical data are used as guiding data to improve the prediction accuracy of the model;
3. The actual wind farm data are taken as an example to carry out simulation verification. By comparing the proposed scheme with the classical deep learning models, the superiority of the proposed scheme in MAE and MSE evaluation criteria is testified. After introducing NWP data information, the average accuracy of all prediction models in medium- to long-term prediction tasks can be improved by 117%. This verifies the necessity of introducing auxiliary information. For the same data source, GatedTransformer still shows an accuracy improvement of about 11%. This also verifies the superiority of the proposed method. In addition, the accuracy of prediction is also proven when the model uses input and output over different time scales.  
This paper has made certain progress in the accuracy of medium-long-term prediction of wind power. However, this method has high requirements for the data quality of NWP. In addition, due to limitations in data sources, high-quality and supervised wind power datasets are not abundant enough, posing challenges to model training. The next work will mainly focus on combining the self-supervised model with the Gated Transformer to improve the training quality of the model through Pre-task.  
Conceptualization, Q.H. and Y.W.; methodology, Q.H. and Y.W.; software, S.-K.I.; validation, Q.H.; formal analysis, S.-K.I.; investigation, S.-K.I. and X.Y.; resources, Y.W.; data curation, Q.H.; writing--original draft preparation, Q.H.; writing--review and editing, Q.H. and Y.W.; visualization, Q.H. and Y.W.; supervision, Y.W.; project administration, Y.W. and X.Y.; funding acquisition, Y.W. and S.-K.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.