Accurate prediction of wind speed in extreme weather can provide important guidance for distribution network to enhance disaster prevention and resilience.This paper proposes a method based on Temporal Convolutional Network (TCN), Bi-directional Long Short-Term Memory(BiLSTM) and error correction for wind speed prediction in extreme weather.First, the time series characteristics of multi-feature weather data are extracted by TCN, and then input into BiLSTM for wind speed prediction.To further improve the prediction accuracy, Variational Mode Decomposition (VMD) is introduced to decompose the error sequence, and BiLSTM models are constructed to perform error prediction for the decomposed error subsequences respectively.Then the error prediction value is used to correct the wind speed prediction value.Finally, simulations are carried out for a place of Henan province, and the results show that compared with measured weather data, the proposed method can effectively predict wind speed with high accuracy when extreme weather occurs.