Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
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
Analytics-driven asset management
IBM Journal of Research and Development
Activity analysis based on low sample rate smart meters
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Fault detection in water supply systems using hybrid (theory and data-driven) modelling
Mathematical and Computer Modelling: An International Journal
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We present our experience of using machine learning techniques over data originating from advanced meter infrastructure (AMI) systems for water consumption in a medium-size city. We focus on two new use cases that are of special importance to city authorities. One use case is the automatic identification of malfunctioning meters, with a focus on distinguishing them from legitimate non-consumption such as during periods when the household residents are on vacation. The other use case is the identification of leaks or theft in the unmetered common areas of apartment buildings. These two use cases are highly important to city authorities both because of the lost revenue they imply and because of the hassle to the residents in cases of delayed identification. Both cases are inherently complex to analyze and require advanced data mining techniques in order to achieve high levels of correct identification. Our results provide for faster and more accurate detection of malfunctioning meters as well as leaks in the common areas. This results in significant tangible value to the authorities in terms of increase in technician efficiency and a decrease in the amount of wasted, non-revenue, water.