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Due to the uncertainty and randomness of wind power, wind power prediction is very important for the stable operation of power system. To improve the prediction accuracy of wind power model; After studying the mathematical model of wind turbine, combining physical modeling and data-driven modeling, a time convolutional neural network model based on physical information was proposed for the power prediction of wind turbine. The rotor motion equation of the wind turbine is embedded into the loss function of the temporal convolutional neural network, so as to improve the prediction ability, generalization and physical interpretability of the model. The physical model of wind turbine is built in Simulink simulation software to obtain experimental data samples. The same working condition experiment and extrapolation experiment show that compared with the original time convolutional neural network model, the root mean square error of the time convolutional neural network model based on physical information is reduced by 50.8%, and the root mean square error of the extrapolation experiment is reduced by 55.2%. The accuracy of wind power prediction is significantly improved.