Detection of non-standard situation in smart water metering

In this paper the algorithm for detection of non-standard situation in smart water metering based on machine learning is designed. The main categories for non-standard situation or anomaly detection and two common methods for anomaly detection are analyzed. Proposed solution needs to fit the requirements for correct, efficient and real-time detection of non-standard situation in actual water consumption with minimal required consumer intervention to its operation. The final implemented and tested solution evaluates anomalies in water consumption for a given time in specific day and month using machine learning with semi-supervised approach.