## 1 Introduction  
With the depletion of global fossil energy and the increasingly prominent environmental issues, the proportion of wind power in the energy mix has increased [1]. By December 2024, China's cumulative installed power capacity was about 3.35 TW, with installed wind power capacity reaching approximately 520 GW, an 18.0% increase year-on-year [2]. However, the growth in wind power capacity significantly impacts the stable operation of the power system [3]. In recent years, extreme cold wave events have occurred intermittently against the backdrop of global climate change. Cold wave events are often characterized by a sudden drop in temperature and a sharp increase in wind speed [4]. When such weather occurs in areas with large-scale wind farms, the fluctuation and uncertainty of wind power generation increase, and in severe cases, may cause widespread issues such as wind turbine shutdowns and units going off-grid [5; 6]. The 2021 Texas power crisis was directly caused by prolonged extreme cold wave events, resulting in a lack of sustainable power supply from renewable energy sources and power outages, affecting more than 4.5 million customers [7]. Therefore, accurate forecasting of cold wave events and wind power forecasting during such conditions is crucial for the stable operation of the power system under extreme weather conditions [8]. In recent years, forecasting methods that effectively model the complex nonlinear relationships in data have been widely used in wind power forecasting [9; 10]. Methods such as Empirical Mode Decomposition (EMD) [11; 12; 13], wavelet transform [14], and principal component analysis [15] are used to capture the complex interdependencies between variables. Genetic Algorithms (GAs) [16] and particle swarm optimization (PSO) algorithms [17; 18] have been applied to optimize the parameters of forecasting models. Additionally, methods that modify Numerical Weather Prediction (NWP) by incorporating the spatiotemporal characteristics of wind power features [19; 20] can significantly improve forecasting accuracy. However, regular weather forecasting methods cannot capture power changes during extreme weather events. To enhance wind power forecasting accuracy under extreme weather conditions, reference [21] proposed a short-term forecasting methodology incorporating the recognition and verification of transitional weather processes. The approach employs an object-oriented diagnostic verification method to analyze wind speed sequences during transitional weather, extracting NWP forecast patterns for extreme meteorological events. Transitional weather is primarily characterized by abrupt fluctuations in power output, while cold-wave conditions mainly lead to wind power losses, demonstrating the distinct impacts of these two weather scenarios on wind power generation. Reference [22] developed a dual-adaptive sliding window inflection point detection method for extracting transitional power periods, effectively identifying wind power ramp events during extreme weather. Current research predominantly focuses on characterizing and detecting ramp events, yet singular ramp forecasting proves inadequate for cold wave scenarios given the diversity of extreme weather types. Existing studies have improved wind power forecasting accuracy under extreme weather conditions through scenario classification methods. However, their scenario-partitioning mechanisms still exhibit significant limitations. For instance, some studies classify NWP data into different scenarios based on wind speed and wind direction [23]. Additionally, one study categorized scenarios into low-temperature shutdown, high-wind cutoff, and icing-induced derating based on different mechanisms by which low-temperature cold waves affect wind power shutdown [24]. Scenario classification enhances the adaptability of forecasting models to different types of extreme weather.