Prediction of transformer district operation status based on Attention-CNN-GRU

In order to better meet the electricity demand of residents and reasonably arrange dispatching management, this paper proposes a hybrid neural network model based on the mechanism of CNN-GRU for low voltage and overload trans-former area prediction. Firstly, the original data is preprocessed, including missing value completion and data normalization, the model is established and learning parameters are set; Then, the pro-posed method was verified by a single transformer district in a certain area, and compared with a single neural network, the robustness greatly improved; Finally, the data of all transformer districts in three cities in North China, Central China and South China in 2022 were used for comparison, with an average accuracy of 84.4%, providing help for the safe and stable operation of power systems and references for transformer district prediction research.