Non-intrusive load disaggregation using graph signal processing

He, Kanghang and Stankovic, Lina and Liao, Jing and Stankovic, Vladimir (2018) Non-intrusive load disaggregation using graph signal processing. IEEE Transactions on Smart Grid, 9 (3). pp. 1739-1747. ISSN 1949-3053

Text (He-etal-IEEETSG2016-non-intrusive-load-disaggregation)
Accepted Author Manuscript

Download (760kB)| Preview


    With the large-scale roll-out of smart metering worldwide, there is a growing need to account for the individual contribution of appliances to the load demand. In this paper, we design a Graph signal processing (GSP)-based approach for non-intrusive appliance load monitoring (NILM), i.e., disaggregation of total energy consumption down to individual appliances used. Leveraging piecewise smoothness of the power load signal, two GSP-based NILM approaches are proposed. The first approach, based on total graph variation minimization, searches for a smooth graph signal under known label constraints. The second approach uses the total graph variation minimizer as a starting point for further refinement via simulated annealing. The proposed GSP-based NILM approach aims to address the large training overhead and associated complexity of conventional graph-based methods through a novel event-based graph approach. Simulation results using two datasets of real house measurements demonstrate the competitive performance of the GSP-based approaches with respect to traditionally used Hidden Markov Model-based and Decision Tree-based approaches.