Accurate prediction of wind speed in extreme weather can provide important guidance for disaster prevention and resistance of the distribution network. This paper proposes a method based on temporal convolutional network (TCN) and Bi-directional Long Short Term Memory（BiLSTM）and error correction model for wind speed prediction in extreme weather. Firstly, the weather data is preprocessed. The time series characteristics of multi-feature data are extracted by TCN, and the extracted information is input into BiLSTM for wind speed prediction. In order to further improve the prediction accuracy, variational mode decomposition (VMD) is introduced to decompose the error sequence, and constructing BiLSTM models for the decomposed error subsequences respectively to perform error prediction. Then the error prediction value is used to correct the wind speed prediction value. Combined with the measured weather data in a certain place in Henan Province, the simulation results verified that the proposed method can effectively predict wind speed and has a high prediction accuracy when extreme weather occurs.