Influence of Data Granularity on Smart Meter Privacy

Abstract

Through smart metering in the smart grid end-user domain, load profiles are measured per household. Personal data can be inferred from these load profiles by using nonintrusive appliance load monitoring methods, which has led to privacy concerns. Privacy is expected to increase with longer intervals between measurements of load curves. This paper studies the impact of data granularity on edge detection methods, which are the common first step in nonintrusive load monitoring algorithms. It is shown that when the time interval exceeds half the on-time of an appliance, the appliance use detection rate declines. Through a one-versus-rest classification modeling, the ability to detect an appliance's use is evaluated through F-scores. Representing these F-scores visually through a heatmap yields an easily understandable way of presenting potential privacy implications in smart metering to the end-user or other decision makers.