This study focuses on the electric load forecasting of the core link in the power grid. On the basis of summarizing and analyzing the research results of previous researchers, a method based on the combination of pre-training GRU and LightGBM is proposed. This method first uses electrical load data to train a feature extraction network GRU, then uses the network to extract timing features, and uses LightGBM to predict the electrical load of the extracted timing features and non-sequential features. The innovation of this method is to propose a pre-training network to expand the features and fully integrate the timing features and non-timing features. And taking into account the regional differences of the power grid, the GRU network parameters were adaptively fine-tuned during the overall training process. Ensure that the extracted time series features are consistent with the current regional characteristics. Finally, it is found through simulation experiments that this method has achieved a 2% improvement in various indicators.