Detecting urban water consumption patterns: a time-series clustering approach

被引:6
|
作者
Leitao, Joaquim [1 ]
Simoes, Nuno [2 ]
Marques, Jose Alfeu Sa [2 ]
Gil, Paulo [1 ,3 ]
Ribeiro, Bernardete [1 ]
Cardoso, Alberto [1 ]
机构
[1] Univ Coimbra, CISUC, Dept Informat Engn, Coimbra, Portugal
[2] Univ Coimbra, Dept Civil Engn, Inst Syst Engn & Comp Coimbra INESC Coimbra, Coimbra, Portugal
[3] Univ Nova Lisboa, Dept Elect Engn, Ctr Technol & Syst UNINOVA CTS, Monte De Caparica, Portugal
关键词
pattern recognition; time-series clustering; water consumption patterns; water resources management;
D O I
10.2166/ws.2019.113
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The need for efficient management of water distribution systems is a growing concern for economic, environmental and social reasons. Water supply systems are commonly designed to ensure adequate behaviour under the worst conditions, such as maximum consumption, which leads to overestimation in supply tanks and energy waste. While overestimation should be considered, to account for unpredictable demands and emergency scenarios, we advocate that a detailed understanding of consumption patterns enables an improvement in water management and is additionally beneficial to correlated resources such as electricity. A novel framework to detect water consumption patterns is developed and applied to an urban scenario. Observed discrepancies among computed patterns enable readjustments of supplied water flowrate, thus promoting effective water allocation and pumping costs, while mitigating water contamination risks.
引用
收藏
页码:2323 / 2329
页数:7
相关论文
共 50 条
  • [1] Uncovering urban water consumption patterns through time series clustering and entropy analysis
    Wang, Renfang
    Zhao, Xinyu
    Qiu, Hong
    Cheng, Xu
    Liu, Xiufeng
    [J]. WATER RESEARCH, 2024, 262
  • [2] Investigating Water Consumption Patterns Through Time Series Clustering
    Abu Waraga, Omnia
    Abdeljaber, Abdulrahman
    Abu Talib, Manar
    Abdallah, Mohamed
    [J]. 2021 14TH INTERNATIONAL CONFERENCE ON DEVELOPMENTS IN ESYSTEMS ENGINEERING (DESE), 2021, : 44 - 49
  • [3] AMERICAN CONSUMPTION PATTERNS AND THE PRICE OF TIME - A TIME-SERIES ANALYSIS
    BRYANT, WK
    WANG, Y
    [J]. JOURNAL OF CONSUMER AFFAIRS, 1990, 24 (02) : 280 - 306
  • [4] UNSUPERVISED TIME-SERIES CLUSTERING OF DISTORTED AND ASYNCHRONOUS TEMPORAL PATTERNS
    Mure, Simon
    Grenier, Thomas
    Guttmann, Charles R. G.
    Benoit-Cattin, Hugues
    [J]. 2016 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING PROCEEDINGS, 2016, : 1263 - 1267
  • [5] DETECTING CHAOS IN TIME-SERIES
    SERIO, C
    [J]. FRACTALS IN THE NATURAL AND APPLIED SCIENCES, 1994, 41 : 371 - 383
  • [6] DETECTING CHANGE IN A TIME-SERIES
    SEGEN, J
    SANDERSON, AC
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 1980, 26 (02) : 249 - 255
  • [7] DETECTING NONLINEARITY IN TIME-SERIES
    DAVIES, N
    PETRUCCELLI, JD
    [J]. STATISTICIAN, 1986, 35 (02): : 271 - 280
  • [8] A time-series clustering methodology for knowledge extraction in energy consumption data
    Ruiz, L. G. B.
    Pegalajar, M. C.
    Arcucci, R.
    Molina-Solana, M.
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2020, 160
  • [9] A time-series clustering methodology for knowledge extraction in energy consumption data
    Ruiz, L.G.B.
    Pegalajar, M.C.
    Arcucci, R.
    Molina-Solana, M.
    [J]. Expert Systems with Applications, 2020, 160
  • [10] A bitmap approach to trend clustering for prediction in time-series databases
    Yoon, JP
    Luo, YX
    Nam, JY
    [J]. DATA MINING AND KNOWLEDGE DISCOVERY: THEORY, TOOLS AND TECHNOLOGY III, 2001, 4384 : 302 - 312