A non-intrusive load identification method based on data augmentation and threshold-free recurrence plot

Abstract

Non-intrusive load monitoring （NILM） not only makes the flow of electric energy transparent but also simplifies the installation process of smart meters， effectively reducing the cost of load monitoring. To enhance the accuracy of load recognition in NILM， a method for load recognition based on data augmentation and threshold-free recurrence 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.

Keywords

• nilm
• data augmentation
• load identification
• deep learning
• rp