A Meteorological Risk Identification and Early Warning Model for Power System Based on Multi-Source Data Fusion

This study utilizes multi-source data fusion technology and advanced Corrformer models to improve the prediction accuracy and response efficiency of the power system to extreme meteorological conditions. At the beginning of the study, the key to meteorological forecasting was discussed in detail, especially in the context of global climate change, the impact and challenges of frequent extreme weather on power infrastructure. In the experimental phase, we recorded the performance indicators of the Corrformer model under both conventional and extreme meteorological conditions, and found that its average response time under standard meteorological conditions was only 200 milliseconds, slightly longer under extreme conditions, at 250 milliseconds. The overall prediction accuracy remained at 85%. These data validate the significant advantages of the Corrformer model in improving prediction speed and accuracy compared to traditional prediction tools, greatly enhancing the stability and safety of the power system in responding to extreme weather events.