A Non-invasive Load Recognition Approach Incorporating SENet Attention Mechanism and GA-CNN

With the popularization of smart meters and the gradual improvement of grid informatization and digitization, non-intrusive load monitoring (NILM) on the demand side of residential customers' energy usage is becoming one of the key technologies for power supply companies to boost energy efficiency. Regarding the problems of the current non-intrusive load recognition algorithms, such as feature redundancy, high computational overhead, and low recognition performance, the paper proposes a non-intrusive load recognition method integrating SENet attention mechanism and GA-CNN. Firstly, the SENet attention mechanism is embedded in a convolutional neural network (CNN) to improve the characterizaion of key features and reduce feature redundancy. Secondly, the U-I trajectory map of the residential load is extracted and weighted pixelated to obtain the WVI (Weighted pixelated VI) feature matrix through computation, which is applied as the feature coefficient to train the SENet-CNN model. Finally, by virtue of the genetic algorithm, the SENet-CNN model is trained and the hyperparameters of the CNN-SENet model are optimized to improve the model load recognition performance and computational efficiency. The experimental results show that the proposed method can reduce the computational overhead of non-intrusive load identification, accurately identify the residential load categories, and significantly improve the efficiency of non-intrusive load identification.