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

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