A new type of water meter makes it possible to monitor water temperature. It provides a minimum temperature value once day, on account of the battery capacity. In the first instance this capacity might seem limited, but a large-scale application makes it possible to draw up a daily ‘heat’ map of a particular area. By combining this map with other measurements taken in the same area, one can monitor the temperature of the pipe water and identify the ‘hot’ and ‘cool’ spots. This allows for the more accurate determination of (possible) risks, and the formulation of more targeted control measures. The measurements also serve to test a possible connection between reports of discoloured water and the temperature in the mains network.
Up until now, the monitoring of water temperature in mains networks has only been done on an ad hoc basis, which has made the accurate identification of ‘hot’ and ‘cool’ spots difficult. The idea of the project is to make use of data-driven techniques to distil information and to use it for a dynamic ‘hot/cool-spots’ map.
Relatively limited water temperature data are measured and collected in the mains network. The water temperature at a connection is only measured once a day, on account of the battery capacity of the water meter. And while the region contains thousands of connections, only a few dozen smart water meters are available for this pilot. Moreover, not all the factors that contribute to the local heat transfer – for example, an aquifer thermal storage system – are known and defined. Uncertainties also remain about the flow paths taken by the water between the production company and the client, because there is no reliable way of determining for all the (water) valves whether they are open or shut.
This project applies big data techniques to provide the missing information required to gain a clear picture of water temperature in the mains network. To this end, we make use of information from smart water meters as well as data on the network itself and on its surroundings. An example of a first set of data collected is shown in Figure 1. We employ machine-learning algorithms to extract the most relevant information from the data, and to find the closest possible connection between these data and the temperatures measured by the smart water meters. Our data sources include pipe material, ambient temperature, water-flow volume at the client’s, ground type, number of sunshine hours during a day, and building type. The connections are tested and used in the reconstruction of a temperature map covering the entire mains network.