To fully develop the potential characteristics of electric load historical data and improve the prediction accuracy of the short-term load forecasting model

a residual AM-Bi-LSTM prediction model combining improved residual network (ResNetPlus)

attention mechanism (AM) and bi-directional long short-term memory network(Bi-LSTM) is proposed in this paper. This model takes historical load

temperature and the features of the predicted date as input. First

based on the Bi-LSTM prediction model

multi-layer improved residual networks are introduced to extract the hidden features of input data

which solves the problem of network degradation caused by the deepening of hidden layers of neural networks

and greatly improves the back propagation ability of the model. Second

the attention mechanism is used to analyze the correlation between input information and current load in the network and highlight the impact of important information

thus improving the speed and accuracy of the model. Third

the Snapshot strategy is used to integrate multiple models that converge on different local minima

in order to improve the accuracy and robustness of the model. Finally

the US ISO-NE Dataset is used to verify the performance of the model. The experimental results show that the proposed model has achieved an average prediction accuracy of 98.27%. The average prediction accuracy over 12 consecutive months with the proposed model has improved by 2.87% compared to the traditional LSTM model. In addition

the average prediction accuracy under different seasons based on the proposed model has improved by 1.03% and 1.16% compared to the AM-Bi-LSTM and ResNetPlus models

respectively. This indicates that compared to the contrast model

the residual AM-Bi-LSTM model has higher accuracy

robustness and generalization ability.