For lower complexity of the load sequence, the empirical mode decomposition (EMD) method is used to obtain different components. For shorter training time and a smaller cumulative error caused by component forecasting one by one, the components are reconstructed into high-frequency and low-frequency ones according to the zero-crossing rate of the components. The high-frequency components of the load are forecasted by the temporal convolutional network (TCN) model, whereas the low-frequency ones are forecasted by the extreme learning machine (ELM). The proposed EMD-TCN-ELM model is compared with three individual models TCN, ELM, and long short-term memory (LSTM) and three mixed models EMD-TCN, EMD-ELM, and EMD-LSTM through experiments, and its mean absolute percentage error (MAPE) is reduced by 0.538%, 1.866%, 1.191%, 0.026%, 1.559%, and 0.323%, respectively. The forecasting accuracy of the proposed model is also the highest. Additionally, the proposed model has the shortest training time among the top three models in forecasting accuracy. The above results verify the superiority of the proposed model in load forecasting accuracy and training time.