Non-intrusive load monitoring (NILM) of residential houses is an important research content of the user demand side of smart grids

and the energy consumption analysis and power consumption management of residential loads are key steps in achieving energy conservation

emission reduction

and sustainable development. Aiming at the problems of poor recognition performance of traditional algorithms and difficulty in adapting to the current complex electricity environment

a NILM load recognition method integrating convolutional neural network (CNN)-self-attention mechanism is proposed from the optimization idea of enhancing the feature extraction performance of classification algorithms. Firstly

the power data of eight different household appliances are collected to establish a U-I trajectory curve database. Secondly

the feature aggregation ability of CNN is improved by using squeeze-and-excitation network (SENet) attention mechanism to complete the feature extraction and load identification of U-I trajectory curves of different electrical appliances. Finally

the private dataset and PLAID dataset are tested

and the example results show that the proposed method has high recognition accuracy and good generalization performance in different operational scenarios.