Sensing from the basement: a feasibility study of unobtrusive and low-cost home activity recognition
UIST '06 Proceedings of the 19th annual ACM symposium on User interface software and technology
NAWMS: nonintrusive autonomous water monitoring system
Proceedings of the 6th ACM conference on Embedded network sensor systems
HydroSense: infrastructure-mediated single-point sensing of whole-home water activity
Proceedings of the 11th international conference on Ubiquitous computing
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Smarter water management: a challenge for spatio-temporal network databases
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The design and evaluation of prototype eco-feedback displays for fixture-level water usage data
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A longitudinal study of vibration-based water flow sensing
ACM Transactions on Sensor Networks (TOSN)
Analysis of advanced meter infrastructure data of water consumption in apartment buildings
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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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.