A Short-Term Data Based Water Consumption Prediction Approach

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.

Keywords

• water
• forecasting
• pattern-based
• machine-learning