供水管网计量区水量数据的特征分析
摘要：基于供水管网计量区监测数据快速分析与挖掘,进行近实时异常工况检测,从时间序列角度解析了供水管网计量区水量的数据特征。结果表明,水量数据的波动变化可归结为规律性波动变化、不确定性波动变化以及错误数据三类,为数据驱动的异常工况检测方法开发提供技术支撑。
关键词：供水管网时间序列统计分析异常检测
尊敬的用户，本篇文章需要20元，点击支付交费后阅读
限时优惠福利：领取VIP会员
全年期刊、VIP视频免费!
全年期刊、VIP视频免费!
参考文献[1] MOUNCE S R,BOXALL J B,MACHELL J.Development and verification of an online artificial intelligence system for detection of bursts and other abnormal flows[J].Journal of Water Resources Planning and Management,2010,136(3):309-318．
[2] ROMANO M,KAPELAN Z,SAVIC D A.Automated detection of pipe bursts and other events in water distribution systems[J].Journal of Water Resources Planning and Management,2014,140(4):457-467．
[3] YE G L,FENNER R A.Weighted least squares with expectation-maximization algorithm for burst detection in U.K.water distribution systems[J].Journal of Water Resources Planning and Management,2014,140(4):417-424．
[4] WU Y,LIU S,WU X,et al.Burst detection in district metering areas using a data driven clustering algorithm[J].Water Research,2016,100:28-37．
[5] 郭东进．给水管网爆管识别定位方法研究[D]．杭州：浙江大学,2015．
[6] TAN P N,STEINBACH M,KUMAR V.Introduction to Data Mining[M].New Jersey:Addison-Wesley,2005．
[7] 陈海燕,刘晨晖,孙博．时间序列数据挖掘的相似性度量综述[J]．控制与决策,2017,32(1):1-11．
[8] HAN J,KAMBER M,PEI J.Data Mining:Concepts and Techniques.3rd[M].USA:Morgan Kaufmann,2011．
[9] 周俊．流量仪表及其选型[J]．冶金动力,2008(2):82-84.
[2] ROMANO M,KAPELAN Z,SAVIC D A.Automated detection of pipe bursts and other events in water distribution systems[J].Journal of Water Resources Planning and Management,2014,140(4):457-467．
[3] YE G L,FENNER R A.Weighted least squares with expectation-maximization algorithm for burst detection in U.K.water distribution systems[J].Journal of Water Resources Planning and Management,2014,140(4):417-424．
[4] WU Y,LIU S,WU X,et al.Burst detection in district metering areas using a data driven clustering algorithm[J].Water Research,2016,100:28-37．
[5] 郭东进．给水管网爆管识别定位方法研究[D]．杭州：浙江大学,2015．
[6] TAN P N,STEINBACH M,KUMAR V.Introduction to Data Mining[M].New Jersey:Addison-Wesley,2005．
[7] 陈海燕,刘晨晖,孙博．时间序列数据挖掘的相似性度量综述[J]．控制与决策,2017,32(1):1-11．
[8] HAN J,KAMBER M,PEI J.Data Mining:Concepts and Techniques.3rd[M].USA:Morgan Kaufmann,2011．
[9] 周俊．流量仪表及其选型[J]．冶金动力,2008(2):82-84.
Characteristic analysis of water consumption data in metering zones of water distribution systems
Abstract: Monitoring data reflect the operation state of water distribution systems.With the development of information and communication technology,monitoring data can be transmitted in near real time.Under this condition,it becomes possible to detect abnormal conditions in near real time through the rapid analysis and mining of the monitoring data in metering zones.This paper uses statistical analysis method to analyze the data characteristics of water consumption data in metering zones from the perspective of time series.The results show that the fluctuation of water consumption data can be divided into three types:regular fluctuation,uncertainty fluctuation and error data.This lays a foundation for developing data-driven anomaly detection methods.
Keywords: Water distribution systems; Time series; Statistical analysis; Abnomal detection;
854
0
0