Abstract: Aiming at  the problems of difficult hyperparameter selection and high volatility of prediction results in the temporal convolutional network, we proposed a temporal convolutional network model based on an improved northern goshawk optimization algorithm. Firstly, we proposed an improved northern goshawk optimization algorithm based on a hybrid strategy, which enhanced global exploration and local exploitation capabilities of the algorithm by integrating Sine chaotic mapping for population initialization, 
introducing a nonlinear inertia weight adjustment strategy, and combining with  the Lévy flight mechanism. Secondly, we took  the prediction error of the temporal convolutional network as the optimization objective, and used the improved northern goshawk optimization algorithm to automatically search for its optimal hyperparameter combination for constructing a time series 
prediction model. Experimental results on the power load forecasting task show that the proposed prediction model has significant advantages in prediction accuracy and result stability compared to other improved temporal convolutional network models. It provides an efficient and robust automated optimization method for solving the hyperparameter selection problem of temporal convolutional networks, improves the accuracy and reliability of temporal convolutional network models in complex time series prediction tasks, and has practical application value.

Key words: northern goshawk optimization algorithm, temporal convolutional network, power load forecasting, hyperparameter selection