Cold Wave Recognition and Wind Power Forecasting Technology Considering Sample Scarcity and Meteorological Periodicity Characteristics

Accurate recognition of both cold wave occurrence and wind power during cold wave events is critically significant to ensure the stable operation of the power system under extreme meteorological conditions. In view of the above problems, this paper proposes a cold wave event recognition method and a day-ahead wind farm cluster power forecasting model. In order to effectively recognize cold wave events, this paper proposes a cold wave discrimination criterion based on wind turbine operation characteristics and NWP data. The proposed recognition criterion employs seasonal meteorological features processed through a GAN-enhanced multi-modal U-Net architecture, effectively mitigating sample scarcity issues. To improve the forecasting accuracy of wind farm cluster power during cold wave events, a combined forecasting model based on the Ns-Transformer model is constructed by combining NWP data with cold wave recognition results. A wind farm cluster is taken as an example to verify the effectiveness of the proposed method. Compared to that of LSTM, Random Forest, BP, and Transformer models, the RMSE of the proposed method is reduced by 5.65%, 5.58%, 4.81%, and 0.44%, respectively, during cold wave occurrence seasons. The results show that the proposed method is superior to the conventional method in terms of cold wave recognition ability and wind power forecasting accuracy.