Generating power footprints without appliance interaction : an enabler for privacy intrusion

Abstract

Appliance load monitoring (ALM) systems are systems capable of monitoring appliances’ operation within a building using a single metering point. As such, they uncover information on occupants’ activities of daily living and subsequently an exploitable privacy leak. Related work has shown monitoring accuracies higher than 90% ̇ achieved by ALM systems, yet requiring interaction with appliances for system calibration. In the context of external privacy intrusion, ALM systems have the following obstacles for system calibration: (1) type and model of appliances inside the monitored building are entirely unknown; (2) appliances cannot be operated to record power footprints; and (3) ground truth data is not available to fine- tune algorithms. Within this work, we focus on monitoring those appliances from which we can infer occupants’ activities. Without appliance interaction, appliances’ profiling is realised via automated capture and analysis of shapes, steady-state durations, and occurrence patterns of power loads. Such automated process produces unique power footprints, and naming is realised using heuristics and known characteristics of typical home equipment. Data recorded within a kitchen area and one home illustrates the various processing steps, from data acquisition to power footprint naming.