Analysis of advanced meter infrastructure data of water consumption in apartment buildings

Abstract

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.