In order to solve the problem of insufficient forecasting accuracy caused by many factors such as nonlinear and time series of load data, a short-term load forecasting model based on CEEMDAN and HBA-BiGRU-SelfAttention was proposed. Firstly, the random forest algorithm is used to extract the features of meteorological factors, which ensures the characteristics of data and reduces the complexity of data. Secondly, the adaptive noise complete set empirical mode decomposition algorithm is used to decompose the original load data, and some relatively stable modal components are obtained. Then, meteorological factors and modal components extracted by feature are taken as input data, and BiGRU-SelfAttention model is used for prediction. For the problem that it is difficult to select the optimal solution for hyperparameters of BiGRU-SelfAttention model, The Honey Badger algorithm is introduced to optimize the hyperparameters of BiGRU-SelfAttention model. Finally, the subsequence prediction results are superimposed to obtain the final prediction results. The comparison test with the actual load data of a certain place proves that the model has high prediction accuracy and can provide reliable basis for the stable operation of power system.