Nonintrusive Load Disaggregation for Residential Users Based on Alternating Optimization and Downsampling

Abstract

With the continuous development of the smart grid, details of residential users’ electricity consumption have received lots of attention. In recent years, a new concept of nonintrusive load monitoring (NILM) based on graph signal processing (NILM-GSP) has been proposed. NILM allows researchers to access the electricity consumption profile of every household appliance (AP) online, which greatly reduced the cost of grid operation. This article proposes a new NILM based on alternating optimization (NILM-AO) to solve the load disaggregation problem. Since the power consumption of residential users is basically stable in its operation cycle, a power consumption constraint is constructed to make NILM realistic. In order to improve real-time processing capability, we propose a statistical downsampling method (NILM-AODM) to find the optimal sampling rate. Simulation results show that NILM-AO is more accurate than NILM-GSP, and the sampling rate of NILM-AODM is also smaller than that of NILM-AO, which leads to less communication load and time delay.