Very low-resolution residential load disaggregation with unsupervised graph signal processing

Abstract

An unsupervised graph signal processing-based non-intrusive load monitoring algorithm is used to disaggregate very low-resolution 15-min real power signals from advanced metering infrastructure smart meters. This study presents the application of load disaggregation to new datasets that are more representative of single-family houses within the U.S. than those previously presented in the graph signal processing-based load disaggregation literature. Filtering is performed on signals within the signal database used for labeling reconstructed signals resulting from disaggregation. Results demonstrate improved accuracy due to filtering target device signals. Level 2 electric vehicle chargers are demonstrated to be the most promising candidate for very low-resolution load disaggregation using graph signal processing, with most houses demonstrating FM > 0.8 and absolute percent error in energy estimation less than 25%. The implications of these results on demand response assessment are presented, with an estimated 1350 MWh of energy available during peak demand periods from EV chargers for demand response applications in regions with acceptable disaggregation accuracy.