Waterloss Detection in Water Distribution Networks using Wavelet Change-Point Detection
| Autor: | Christodoulou, Symeon E., Kourti, Elena, Agathokleous, Agathoklis |
|---|---|
| Přispěvatelé: | Christodoulou, Symeon E. [0000-0002-9859-0381] |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Distribution networks
Computer science 0208 environmental biotechnology 02 engineering and technology Wavelet analysis computer.software_genre Wavelet Water consumption Water distribution Water Utilities (CE) [41] Continuous wavelet transform Water Science and Technology Civil and Structural Engineering Water engineering Normal conditions Hydrogeology business.industry Pattern recognition Anomalies 020801 environmental engineering Meters Anomaly detection Data mining Artificial intelligence Networks CSA / ASCE Civil Engineering Abstracts (CE) business computer Automatic meter reading Change detection |
| Zdroj: | Water Resources Management |
| Popis: | The work presented herein addresses the automatic detection of water losses in water distribution networks (WDN), through the dynamic analysis of the time series related to water consumption within the network and the use of a wavelet change-point detection classifier for identifying anomalies in the consumption patterns. The wavelet change-point method utilizes the continuous wavelet transform (CWT) of time-series (signals) to analyze how the frequency content of a signal changes over time. In the case of water distribution networks the time-series relates to streaming water consumption data from automatic meter reading (AMR) devices, at either the individual consumers' level or at an aggregated district meter area (DMA) level. The wavelet change-point detection method analyzes the provided time-series to acquire inherent knowledge on water consumption under normal conditions at household or area-wide levels, to then make inferences about water consumption under abnormal conditions. The method is demonstrated on several abnormal WDN operating conditions and anomaly detection cases. 31 3 979 994 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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