Water Consumption Analysis for Real-Time Leakage Detection in the Context of a Smart Tertiary Building

Water leaks are considered as abnormal consumptions of water. It is a challenge to detect them instantaneously but recent wireless smart water meters offer new opportunities for remote monitoring and controlling of consumptions and leaks. This paper presents a complete solution that consists in collecting and analyzing water consumption data. This non-intrusive solution relies on information extracted from measurements taken on a single and centralized part of a distribution network. We propose a generic approach which is based on a formal representation of water consumption data. It is made of several models and features that are representative of extremely large datasets. We use daily water load curves and define a Minimum Night Flow (MNF) and a Period Without Null Consumption (PWNC). It is therefore easy for a procedure, i.e., an algorithm running in a loop, to deal with this representation in real time for the detection of abnormal consumptions and leaks. Our approach to detect water leaks has been implemented and validated with real consumption data from a smart meter installed in a tertiary building. The maximum daily consumption curve is used as a typical load curve and allows to detect big leaks. Extracting the MNF ad the PWNC from the instantaneous flow rate allows to detect smaller leaks. The discrimination between large and small leaks is based on whether or not the typical water load curve is exceeded.