2020
DOI: 10.1515/comp-2020-0190
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Non-standard situation detection in smart water metering

Abstract: 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… Show more

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Cited by 2 publications
(2 citation statements)
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“…Hence provides accurate results to the user who uses this system in the stores. Kainzet al [14] used the K-means algorithm for anomaly detection in water consumption. The data processing was made on water consumption prediction and non-standard situations.…”
Section: Anomaly Detectionmentioning
confidence: 99%
“…Hence provides accurate results to the user who uses this system in the stores. Kainzet al [14] used the K-means algorithm for anomaly detection in water consumption. The data processing was made on water consumption prediction and non-standard situations.…”
Section: Anomaly Detectionmentioning
confidence: 99%
“…When extracting the measured point data, the average calculation is performed by traversing the data points in the 3×3 window [15].…”
Section: Remote Sensing Image Preprocessingmentioning
confidence: 99%