# KAN-Transformer Model for UltraShort-Term Wind Power Prediction Based on EWMA Data Processing
## 1 Introduction
As research in this area has progressed, the emergence of Kolmogorov-Arnold networks (KANs) in 2024 [26] successfully addressed this limitation, improving model prediction accuracy. KAN is an extension of deep learning based on the mathematical principles of the Kolmogorov-Arnold representation theorem (KART). This theorem proves that a multivariate continuous function can be approximated by the summation of a finite number of nonlinear elementary functions. The KAN network initializes several B-spline functions and learns their parameters. By summing these B-spline functions to form nonlinear elementary functions, and then aggregating all the nonlinear elementary functions to map them to the prediction dimension, the model's capacity to capture nonlinear relationships is enhanced. Since KAN networks are adept at capturing nonlinear relationships, they have subsequently been integrated with time-series models. For instance, literature [27] demonstrates that KAN networks, due to their highly accurate modeling of complex nonlinear systems, have proven to be effective in energy management systems, showcasing their practical utility and effectiveness. Another study [28] incorporated KAN into time-series forecasting to improve interpretability and nonlinear relationship modeling, verifying the effectiveness of KAN in the field of time-series prediction. When forecasting future wind power, the prediction is not only related to past wind power values but also closely linked to other variables such as historical wind speed, wind direction, temperature, and more. Therefore, multiple variable features are often introduced alongside wind power features. However, during this process, the presence of noise and outliers in the multivariable data can reduce the accuracy of the prediction model. To address this, the exponential weighted moving average (EWMA) method is introduced. By controlling the decay factor, EWMA smooths the data while retaining the original trends, thereby reducing the impact of noise on the prediction accuracy. For example, in [29], EWMA analysis is used in the monitoring stage to detect abnormal changes in features. In [30], EWMA assigns greater weight to more recent data, smoothing time-series data and improving performance through this optimized data-mining approach. In conclusion, when using the Transformer model with point-wise attention mechanisms for wind power prediction, the model's accuracy is reduced due to noise in wind power data and the inability of the simple linear output layer to capture nonlinear relationships. To address this, this paper proposes a KAN-Transformer model incorporating EWMA data processing. The contributions of this paper are as follows: First, EWMA is employed to denoise the introduced multivariable features, improving the predictability of wind power data. Second, the KAN layer is used to replace the Transformer model's final linear layer, enhancing the model's ability to capture nonlinear relationships and thus improving prediction accuracy. The chapter arrangement is as follows: In Section 2, EWMA is used to reduce the impact of noise on the model's prediction accuracy. KAN is combined with the Transformer model, where features are extracted through the Encoder-Decoder structure and then passed into the KAN layer to obtain prediction results, enhancing the model's ability to capture nonlinear relationships. In Section 3, simulation experiments and analysis are conducted. In Section 4, conclusions are summarized.