Abstract
A smart water network consists of a large number of devices that measure a wide range of parameters present in distribution networks in an automatic and continuous way. Among these data, you can find the flow, pressure, or totalizer measurements that, when processed with appropriate algorithms, allow for leakage detection at an early stage. These algorithms are mainly based on water demand forecasting. Different approaches for the prediction of water demand are available in the literature. Although they present successful results at different levels, they have two main drawbacks: the inclusion of several seasonalities is quite cumbersome, and the fitting horizons are not very large. With the aim of solving these problems, we present the application of pattern similarity-based techniques to the water demand forecasting problem. The use of these techniques removes the need to determine the annual seasonality and, at the same time, extends the horizon of prediction to 24 h. The algorithm has been tested in the context of a real project for the detection and location of leaks at an early stage by means of demand forecasting, and good results were obtained, which are also presented in this paper.
Highlights
The management of water distribution networks is not an easy task
A wide variety of technologies has been deployed that have the potential to change the paradigm of the management of water distribution networks, turning them into smart water networks (SWN)
With a one-minute frequency, we found that neural network approaches were unfeasible, and even more classic methods such as ARIMA and dynamic harmonic regression were too expensive
Summary
The management of water distribution networks is not an easy task. In Europe, there are more than 3.5 million kilometers of pipes [1]. An SWN consists of a large number of devices that measure a wide range of parameters present in distribution networks in an automatic and continuous way. Among these data, you can find flow, pressure, or totalizer measurements that, when processed with the appropriate algorithms, allow for the detection of leakages at an early stage. You can find flow, pressure, or totalizer measurements that, when processed with the appropriate algorithms, allow for the detection of leakages at an early stage These algorithms are mainly based on water demand forecasting
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