# KAN-Transformer Model for UltraShort-Term Wind Power Prediction Based on EWMA Data Processing
## 4 Conclusions  
In order to enhance grid dispatch flexibility, improve the absorption capacity of renewable energy, reduce wind curtailment, and strengthen grid stability, this paper proposes a \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MAE/MW** & **RMSE/MW** & **R\({}^{2}\)/\%** \\ \hline TCN-BiLSTM & 8.54 & 13.15 & 95.97 \\ TCN-BiGRU & 6.66 & 8.96 & 98.13 \\ LSTM & 5.57 & 8.56 & 98.29 \\ GRU & 6.24 & 8.36 & 98.37 \\ EWMA-KAN-Transformer & 4.38 & 7.37 & 98.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of test set evaluation metrics between the EWMA-KAN-Transformer model and LSTM and GRU series models. Figure 17: Comparison of prediction curves between the EWMA-KAN-Transformer model and LSTM and GRU series models. short-term wind power forecasting model based on the EWMA-KAN-Transformer. This paper addresses the issues of data noise and the limited ability of the model to capture nonlinear relationships due to the use of a simple linear layer in the final prediction step. Firstly, EWMA data processing is applied to reduce the impact of noise from the introduced multivariate features on the model's prediction accuracy. Secondly, the KAN (Kernelized Attention Network) is integrated into the Transformer model. By learning the parameters of cubic B-splines, non-linear unit functions are obtained. These non-linear unit functions are then summed to form a multi-dimensional continuous function, which is used to generate the final prediction, thus enhancing the model's ability to capture non-linear relationships. Finally, experimental analysis leads to the following conclusions. 1. The KAN-Transformer model reduced the MAE and RMSE by 0.41 MW and 0.49 MW, respectively, compared to the Transformer model. Additionally, the R\({}^{2}\) improved by 0.18%. This validates that incorporating the KAN network in the final layer of the Transformer model enhances its predictive accuracy by capturing nonlinear relationships. 2. By applying EWMA to the original wind power data to remove some of the noise, the EWMA-KAN-Transformer model reduced the MAE and RMSE by 0.08 MW and 0.35 MW, respectively, compared to the KAN-Transformer model. Additionally, the R\({}^{2}\) improved by 0.12%. This demonstrates that when incorporating multiple variables, using EWMA to remove noise can mitigate the impact of noise in wind power data on the model's predictive accuracy. 3. When comparing the proposed model with three types of models, the EWMA-KAN-Transformer model demonstrated the highest prediction accuracy, with MAE and RMSE values of 4.38 MW and 7.37 MW, respectively, and an R\({}^{2}\) value of 98.73%. This confirms that the proposed model is more advantageous compared to other wind power forecasting models. 4. The proposed model uses EWMA for noise reduction, which has the advantage of denoising without altering the data trend by adjusting the decay factor. However, finding the optimal decay factor requires ongoing experimentation to enhance the model's prediction accuracy. In future research, employing a rational optimization method to determine the most suitable decay factor could potentially further improve the model's prediction accuracy. Conceptualization, F.X. and C.Q. ; methodology, F.X. and C.Q. ; software, Y.G. ; validation, Y.G. ; formal analysis, Y.G. and L.K. ; investigation, F.X. ; resources, L.K. and M.Z. ; data curation, Y.G. ; writing--original draft preparation, Y.G. ; writing--review and editing, F.X. and C.Q; visualization, L.K. ; supervision, M.Z. ; project administration, C.Q. ; funding acquisition, F.X. and M.Z. All authors have read and agreed to the published version of the manuscript. This research was in part supported by the 2024 Fundamental Research Project (No. LJ212410154011) of the Educational Department of Liaoning Province, in part by National Natural Science Foundation of China (No.