In order to popularize the load identification technology of smart meters and solve the problem of low identification accuracy of traditional non-intrusive load identification algorithm on unbalanced sampled data

a non-intrusive load identification method based on adaptive synthetic（ADASYN）and image analysis is proposed. 1D power data is converted into 2D MTF feature images by markov transition field（MTF）coding

which is used as the input of image recognition network. Based on the deep information mining capability of dense connectivity network（DenseNet）

2D images are input into DenseNet121 network to extract feature information and realize the identification of load types. Based on ADASYN algorithm

the unbalanced data set is oversampled to eliminate the model learning bias caused by the unbalanced data distribution. The results show that ADASYN algorithm can solve the non-intrusive load monitoring data imbalance problem well

and its identification accuracy and F1 score are increased by 0.247 and 0.267

respectively. At the same time

MTF images have clear and easily distinguishable feature information. Combined with the powerful deep feature capture capability of DenseNet121 network

the identification accuracy and F1 score can both reach 0.952

which effectively improves the identification accuracy of non-intrusive load types on unbalanced sampled data.