Anomaly Detection in Water Consumption Patterns Using Prediction and Clustering Approaches

Abnormal consumption patterns or potential leakages in the water distribution system can be identified at an early stage with the help of anomaly detection. This can prevent water wastage, encourages responsible usage and minimize environmental impact. Labeled data indicating the presence of an anomaly has a considerable impact in the accuracy of the anomaly detection model, but also for its validation. In this direction we propose two methods for anomaly detection in daily water consumption. The first approach revolves around predicting future water usage and comparing it with real-time values, enabling the identification of deviations from expected consumption. The other one is focused on classifying consumption behavior: With the help of a pre-trained classifier, the new measurements are labeled as anomalies or regular patterns.