Activity analysis based on low sample rate smart meters

Activity analysis disaggregates utility consumption from smart meters into specific usage that associates with human activities. It can not only help residents better manage their consumption for sustainable lifestyle, but also allow utility managers to devise conservation programs. Existing research efforts on disaggregating consumption focus on analyzing consumption features with high sample rates (mainly between 1 Hz ~ 1MHz). However, many smart meter deployments support sample rates at most 1/900 Hz, which challenges activity analysis with occurrences of parallel activities, difficulty of aligning events, and lack of consumption features. We propose a novel statistical framework for disaggregation on coarse granular smart meter readings by modeling fixture characteristics, household behavior, and activity correlations. This framework has been implemented into two approaches for different application scenarios, and has been deployed to serve over 300 pilot households in Dubuque, IA. Interesting activity-level consumption patterns have been identified, and the evaluation on both real and synthetic datasets has shown high accuracy on discovering washer and shower.