Real-Time Unsupervised Nonintrusive Load Monitoring Based on Graph Signal Processing

Nonintrusive load monitoring (NILM) is an economical technology for promoting demand-side management (DSM) by offering electricity usage details. Driven by the fact that most existing NILM works do not support either real-time response or plug-and-play, this article proposes a real-time unsupervised NILM method based on novel graph signal processing (GSP) concepts. Instead of traditional features such as power variation and steady-state sequence, more informative state transition sequences (STSs) are featured. STSs of various types of loads are adaptively extracted based on specifically defined anchor points. After each falling STS (FSTS) referring to load switching-off event is paired with the optimal rising STS (RSTS) in milliseconds, such STS pairs are labeled as identified loads based on an undirected graph with introducing a penalty term in graph total variation regularization. In addition, the scaling factor selection and negative samples are designed to enhance NILM performance. Validation on three publicly available datasets shows the proposed method generally outperforms seven state-of-the-art benchmarks in three NILM metrics and real-time performance.