Ice Accretion Forecast for Power Grids Based on Pangu Model and Machine Learning Correction: A Case Study on Late December 2021 in Xinjiang, China

During late December 2021, an ice accretion disaster occurred in North Xinjiang, especially in the western part. It is found that the meteorological conditions suitable for the occurrence of ice accretion disasters are when the temperature is between −14 °C and −3 °C, the relative humidity is greater than 80%, the wind speed is between 4.5 m s−1 and 7.5 m s−1, and the pressure is between 919 hPa and 928 hPa. The ice accretion disaster is influenced by large-scale circulation, including the two-trough and one-ridge geopotential height structure in the middle troposphere and the spatially moving Ural Mountain blocking high pressure. Furthermore, using the artificial intelligence-based Pangu model and machine learning algorithms within the application of multiple linear regression and the leave-ten-out cross-validation, a skillful forecast correction model for ice accretion thickness in North Xinjiang is constructed. The prediction model has significant prediction skill for ice accretion thickness in North Xinjiang with 24 h, 48 h, and even 72 h in advance. The findings of the study can improve the timeliness of business system in the short-term and immediate forecast of ice accretion thickness, providing more reliable technical support for the ice prevention and disaster reduction of the power grids.