Analysing the influence of different temporal resolutions of water consumption data for leakage detection and localisation

Digital water meters are increasingly installed in water distribution networks providing detailed information about the water consumption in households at a high temporal resolution (e.g., ranging from seconds to daily readings). While the benefit on household scale is well described in literature (e.g., scarcity billing, awareness raising, leakage detection in domestic installations), recent research is also investigating the potential of digital water meters for an accurate fault management on network scale. In this context, water losses represent a major challenges for the operation of water distribution networks (WDNs), and a timely detection and localisation of water leakages is of greatest interest to reduce these losses. Especially model-based techniques require accurate nodal demands for the numerical simulation of the hydraulic states, which can be obtained for example by using high resolution consumption data.

Therefore, the aim of this work is to first investigate the influence of different temporal resolution of household water consumption data and to define an optimal temporal resolution for the detection and localisation of water leakages. However, power supply (e.g., transmission interval), communication technology (e.g., packet losses), and urban population (e.g., consumer agreement to digital water meters) influence the temporal and spatial quality of data received in a real-word implementation and may differ from the optimal performance. Therefore, different methods are tested to overcome the data gaps caused by data transmission and availability uncertainties. As case study, a real WDN from a pilot project in the city of Klagenfurt is used which is extended by artificial water demand series (temporal resolution varies between 1 min and 24 h) and water leakages. Following, performance of leakage detection (data-based approach) and localisation (model-based approach) in combination with machine learning techniques is evaluated by using detection time and distance between leakage and identified location as selected indicators.

The first results showed that the temporal resolution of consumption data influences the applicable methods for an efficient leakage detection and localisation. For example, water consumption data with a temporal resolution of 15 min allow an accurate mapping of consumption fluctuations, therefore the difference between inflow and measured values is very well suited to identify leakages. In contrast, using the same technique for 24 h consumption data (e.g., difference from inflow and daily mean value), the morning and evening peak would also be indicated as a possible leakage and thus requiring different approaches.