Abstract: The current user mode extraction technology is mainly based on the time-domain characteristics of load data. It cannot accurately distinguish load data with close Euclidean distance in time domain and different fluctuation characteristics in frequency domain, and the classification accuracy of imbalanced data is low. This paper proposes a user mode extraction model to solve the above problems. The model firstly processes the data set with class imbalance by SVM-SMOTE over-sampling method, secondly obtains the scale and wavelet coefficients which is used to form the characteristic matrix in frequency domain through overlapping discrete wavelet transform, finally inputs the characteristic matrix into the deep LSTM network for classification and obtains the typical user mode by calculating the centroid of each category. The experimental results show that the method can effectively process imbalanced data and increase classification accuracy.

Key words: deep learning, class imbalance, maximum overlapping discrete wavelet transform（MODWT）, load classification, long-term and short-term memory network （LSTM）