## 1 Introduction  
In recent years, wind power generation has rapidly developed, leading to an increase in the capacity and scale of wind farms. As a result, the proportion of wind power generation in the electricity market is expanding, which has made improving the utilization efficiency of wind energy resources a crucial topic in clean energy research. Renewable energy represented by wind energy is a type of clean energy with abundant reserves and huge development potential. However, the stability of wind power generation is significantly affected by the randomness and volatility of wind speeds [1]. This uncertainty not only reduces the efficiency and stability of wind power generation but also increases the complexity of grid scheduling, which affects the stable operation and economic benefits of power systems. Therefore, in order to overcome these challenges and ensure the reliability and efficiency of wind power generation, the development of accurate wind speed prediction models is urgently needed. To tackle the wind power generation challenges caused by wind speed volatility and unpredictability, accurate prediction models for wind speed variations are crucial. The accurate prediction of wind speed is essential for wind power generation, as it improves the power generation efficiency on wind farms and ensures the stable operation of power systems [2]. Specifically, the needs of wind speed prediction vary depending on the application scenarios. The wind speed has a direct impact on the power output of wind turbines in power prediction. Accurate short-term wind speed prediction can assist wind -farm operators with optimizing their power generation plans, reducing their energy waste, lowering their production costs, and improving their wind resource utilization efficiency [3]. When evaluating the fatigue load, the high-frequency change information of the wind speed within a short period can be used to predict the transient loads and local structural stresses of the wind turbine. Through reducing the fatigue loads borne by the wind turbine tower, this prediction can effectively improve the wind robustness of the turbine and its durability [4]. In terms of power quality management, fluctuations in wind energy can lead to grid instability, affecting the frequency and voltage. Predicting and dealing with fluctuations caused by wind speed variations can improve the power quality and overall system stability [5]. Therefore, depending on the application scenarios, the requirements for wind speed prediction models are different. The aim of this study was to optimize the short-term wind speed prediction model for improved power prediction, providing more reliable decision support for wind farm operators, as well as ensuring stable power grid operation and efficient wind energy utilization. Wind speed prediction can be divided into short-term and long-term prediction according to different time scales. The short-term prediction duration is within the next few hours, which helps wind farm operators to adjust the power scheduling in real time and optimize the power generation efficiency, while the long-term prediction duration is from a few months to a year, which provides key information for the siting of wind farms and the long-term planning of the power system [6]. In the existing studies, the common model-driven approaches for short-term wind speed prediction include probabilistic statistical models, machine learning models, deep learning models, and combined prediction [7]. Probabilistic statistical models usually rely on a statistical analysis of historical wind speed data, and the common methods include autoregressive models, autoregressive moving average models, autoregressive integral moving average models, and so on [8; 9; 10]. [PERSON] et al. [11] decomposed a wind speed series and established a differential autoregressive moving average model for the obtained low-frequency subsequence. [PERSON] et al.