A Fundamental Bound on Performance of Non-Intrusive Load Monitoring Algorithms with Application to Smart-Meter Privacy

Abstract

We consider non-intrusive load monitoring by a sophisticated adversary that knows the load profiles of the appliances and wants to determine their start-finish times based on smart-meter readings. We prove that the expected estimation error of non-intrusive load monitoring algorithms is lower bounded by the trace of the inverse of the cross-correlation matrix between the derivatives of the load profiles of the appliances. This is an interesting observation illustrating that the derivatives of the load profiles are more important than the profiles themselves for non-intrusive load monitoring (i.e., small rapidly-changing loads are easier to identify than large, yet slowly-varying ones). This fundamental bound on the performance of non-intrusive load monitoring adversaries is used to develop privacy-preserving policies. Particularly, we devise a load-scheduling policy by maximizing the lower bound on the expected estimation error of non-intrusive load monitoring algorithms.