Non-intrusive load monitoring(NILM)not only makes the flow of electric energy transparent but also sim-plifies the installation process of smart meters,effectively reducing the cost of load monitoring.To enhance the accu-racy of load recognition in NILM,a method for load recognition based on data augmentation and threshold-free re-currence plot(RP)is proposed.a denoising diffusion probability model(DDPM)is utilized to augment the load data of small samples to enhance the robustness of the load recognition method.Furthermore,a threshold-free RP,achieved by removing the Heaviside function of the recurrence graph,efficiently represents load characteristics.This is combined with a Transformer deep learning network to construct a load recognition framework.The proposed method is applied to three real-world datasets,and experimental results demonstrate its effectiveness in improving load recognition accuracy and enhancing classification performance.
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