An Approach for Unsupervised Non-Intrusive Load Monitoring of Residential Appliances

Non-Intrusive Load Monitoring (NILM) refers to the analysis of the aggregate power consumption of electric loads in order to recognize the existence and the consumption profile of each individual appliance. In this paper, we briefly describe our ongoing research on an unsupervised NILM system suitable for applications in the residential sector. The proposed system consists of the typical stages of an event-based NILM system with the difference that only unsupervised algorithms are utilized in each stage eliminating the need for a pre-training process and providing wider applicability. In the event detector, a grid-based clustering algorithm is utilized in order to segment the power signals into transient and steady-state sections. Macroscopic features are extracted from the detected events and used in a mean-shift clustering algorithm. The system is tested on the publicly available BLUED dataset and shows event detection and clustering accuracy more than 98%. The system also shows possible disaggregation up to 92% of the energy of phase A of the BLUED dataset. Moreover, the system has been utilized in an energy-disaggregation competition held by Belkin and achieved a score within the top ten results with disaggregation of more than 93% of the total time.