Cloud based machine learning approaches for leakage assessment and management in smart water networks

被引：25

作者：

Mounce, S. R. ^{[1
]}

Pedraza, C. ^{[2
]}

Jackson, T. ^{[3
]}

Linford, P. ^{[4
,5
]}

Boxall, J. B. ^{[1
]}

机构：

[1] Univ Sheffield, Dept Civil & Struct Engn, Sheffield S1 3JD, S Yorkshire, England

[2] Power House Reading STW, Reading RG2 0RP, Berks, England

[3] CYBULA, York YO10 5GH, N Yorkshire, England

[4] Syrinix Ltd, Unit 19, Hethel Engn Ctr, Norwich NR14 8FB, Norfolk, England

[5] Syrinix Ltd, Unit 20, Hethel Engn Ctr, Norwich NR14 8FB, Norfolk, England

来源：

COMPUTING AND CONTROL FOR THE WATER INDUSTRY (CCWI2015): SHARING THE BEST PRACTICE IN WATER MANAGEMENT | 2015年 / 119卷

基金：

英国工程与自然科学研究理事会;

关键词：

Leakage; Smart Networks; AMR; Neural Networks; Cloud computing;

D O I：

10.1016/j.proeng.2015.08.851

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

One-third of utilities around the globe report a loss of more than 40 percent of clean water due to leaks. By reducing the amount of water leaked, smart water networks can help reduce the money wasted on producing or purchasing water, and the related energy required to pump water and treat water for distribution. A UK demo site is presented focusing on leak management, integrating fixed flow and pressure instrumentation, advanced (smart) metering infrastructure and novel instruments (capable of high resolution monitoring). Example data analysis results for this site using the AURA-Alert anomaly detection system for Condition Monitoring are presented. (C) 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the Scientific Committee of CCWI 2015

引用

页码：43 / 52

页数：10

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