Nonintrusive Load Disaggregation for Residential Users Based on Alternating Optimization and Downsampling
Renhai Feng, Wanqi Yuan, Leijiao Ge, Siyu Ji
Abstract
With the continuous development of smart grid, details of residential users electricity consumption have received lots of attention. In recent years, a new concept of non-intrusive load monitoring (NILM) based on graph signal processing (NILM-GSP) has been proposed. NILM allows researchers to access electricity consumption profile of every household appliance (AP) online, which greatly reduced cost of grid operation. This paper proposes a new NILM based on alternating optimization (NILM-AO) to solve the load disaggregation problem. Since 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 optimal sampling rate. Simulation results show that NILM-AO is more accurate than NILM-GSP, the sampling rate of NILM-AODM is also smaller than that of NILM-AO which leads to less communication load as well as time delay.