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.
If the inline PDF is not rendering correctly, you can download the PDF file here.
 Penttilä J., A method for anomaly detection in hyperspectral images, using deep convolutional autoencoders, 2017.
 Dorj E. and Altangerel E., Anomaly detection approach using hiddenmarkov model, in Ifost, 2013, 2, 141–144.
 Carvalho L., Teixeira C., Dias E. C., Meira W., and Carvalho O., Asimple and effective method for anomaly detection in health-care, Proceedings of the SIAM International Conference on Data Mining Workshop, 2015, 16–24.
 Hodge V. and Austin J., A survey of outlier detection methodologies, Artificial intelligence review, 2004, 22(2), 85–126.
 Geijer C. and Andreasson J., Log-based anomaly detection for system surveillance, Ph.D. dissertation, Masters thesis, Chalmers University of Technology, Gothenburg, Sweden, 2015.
 Chandola V., Banerjee A., and Kumar V., Anomaly detection: A survey, ACM computing surveys (CSUR), 2009, 41(3), 15.
 Friedman J., Hastie T., and Tibshirani R., The elements of statistical learning. Springer series in statistics New York, 2001, 1(10).
 Amer M. and Goldstein M., Nearest-neighbor and clustering based anomaly detection algorithms for rapidminer, Proceedings of the 3rd Rapid-Miner Community Meeting and Conference, 2012, 1–12.
 Petija R., Kainz O., Dujava M., Alexandrova G., Fecilak P. and Moravcik M., Measurement of Water Consumption based on Image Processing, in International Conference on Emerging eLearning Technologies and Applications, 2019.