The potential of computational intelligence algorithms in the interpretation of distributed water meter data

Abstract

The development of awareness and legislative aspects related to the energy efficiency of water distribution systems, combined with the ageing of water supply infrastructure and water stress, led to the search for solutions to support more effective control and management of technical infrastructure. Increasing the standard of smart or intelligent water supply systems at all levels of key areas is still a problem under domestic and foreign conditions. This also applies to the microscale of water supply networks, namely water consumers of water and the use of smart water meters with integrated machine learning algorithms. This article presents the results of research on the implementation of a short- -term water consumption prediction model with anomaly detection for  multifamily residential buildings. The prediction of water consumption, based on high-frequency measurements and deep neural networks, achieved a prediction error of less than 3.0%. Anomaly detection, based on the underlying prediction model, had up to 97.3% accuracy.