A novel model based on adaptive recurrent neural network（RNN）is proposed to address the issues of low efficiency and poor performance in identifying abnormal electricity consumption behavior among residents. Design a SMOTE-ENN resampling method to increase the classification performance of imbalanced datasets. We have established an adaptive RNN detection model

using batch normalized RNN as the basic learner

and combining hyperparameter optimization and buffer to dynamically adjust the BNRNN model. In the experimental stage

after improved SMOTE-ENN resampling

the classification performance of the model was significantly improved. At the same time

experiments have verified that the proposed adaptive RNN model with buffering and hyperparameter optimization has the lowest MAE error

indicating that the proposed model has excellent generalization ability. The experimental results validate the practicality and excellent performance of the proposed model

which can provide some reference for the development of abnormal electricity consumption behavior detection.