Research on Negative Charge Prediction Method Based on Deep Learning

Power load forecasting is very important in balancing energy distribution and ensuring the safe operation of power system, and accurate load forecasting can effectively reduce the occurrence of electricity accidents and improve electricity efficiency. Using the advantages of the neural network combination model, the features of the time series are fully extracted and input into the Gate Recurrent Unit (GRU) network for prediction, so that the prediction accuracy of the model is higher and the generalization ability is stronger. In order to verify the proposed technology, 100 data nodes were selected in this experiment, and ResNet+GRU was proved to be superior to all methods by experiments, The values of MAE, MSE, RMSE and MAPE were obtained using ResNet+GRU.