## 1 Introduction
However, most scenario classifications rely on the statistical characteristics of wind power operation states or the clustering of shallow meteorological parameters, failing to fully integrate the intrinsic physical correlations among multidimensional NWP variables. Moreover, the refinement of scenario identification remains insufficient, with inadequate criteria for scenario assessment, leading to blurred scenario boundaries and reduced generalization when predicting future scenarios. Cold wave events are a small-sample scenario in wind power forecasting, where data scarcity impedes effective model learning of their characteristic patterns. To address sample sparsity challenges, researchers have implemented small-sample augmentation [25; 26; 27] and transfer learning approaches [28; 29]. Reference [25] establishes meteorological criteria for cold wave recognition and the analysis of wind power generation characteristics. Specifically targeting data scarcity in cold wave modeling, the Time-GAN algorithm has been employed to synthesize enhanced meteorological and power operational datasets. Recent advancements integrate wind turbine physics with data augmentation techniques to enhance forecasting robustness [30; 31; 32]. The authors of [31] developed a physics-informed reinforcement learning framework for probabilistic forecasting, establishing analytical representations of wind power dynamics during extreme events. This methodology formalizes ramp feature error metrics and incorporates transient characteristics as neural network auxiliaries. While demonstrating superior performance to conventional models in cold-wave conditions, current approaches exhibit two critical limitations: inadequate integration of meteorological recognition processes, and insufficient temporal resolution for precise cold wave onset forecasting. Numerous studies have employed the Weather Research and Forecasting (WRF) mesoscale model coupled with the Global Forecast System (GFS) to advance the high-resolution forecasting of extreme meteorological events, particularly cold waves, across short-to-medium time horizons [33; 34; 35]. While conventional meteorological approaches can delineate regional cold wave trends through elemental variation analysis, effective wind power-specific forecasting requires the multidimensional integration of cold wave impact mechanisms on turbine operations. Conventional meteorological approaches to regional cold wave forecasting primarily delineate trends in meteorological parameter evolution. However, wind power-specific cold wave forecasting necessitates the holistic integration of multi-physics impact mechanisms on turbine operational dynamics. Through coupled analysis of meteorological element and turbine output interactions, reference [36] establishes extreme weather discrimination criteria and prognosticates forthcoming event typologies. While this framework enhances recognition accuracy for specific scenarios, the forecasting model exhibits constrained generalizability during high-variability wind power regimes. Table 1 shows the limitations of current research on wind power forecasting in cold wave weather. At present, the research on wind power forecasting under extreme weather conditions mainly focuses on wind power climbing events. There is relatively little in-depth research on wind power forecasting under cold-wave conditions, and even less research on cold wave event forecasting in the context of large regional wind power cluster operations. In summary, to enhance the recognition of future cold-wave conditions and improve the accuracy of wind power forecasting during such events, this paper proposes a recognition method for cold wave scenarios and a day-ahead wind farm cluster power forecasting model. By analyzing the impact of cold-wave conditions on wind power, this paper establishes a cold wave recognition criterion and extracts cold wave features based on the wind power segments affected by cold waves.