Water Consumption Pattern Analysis Using Biclustering: When, Why and How

Sensors deployed within water distribution systems collect consumption data that enable the application of data analysis techniques to extract essential information. Time series clustering has been traditionally applied for modeling end-user water consumption profiles to aid water management. Howeve...
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Gespeichert in:

Autor*in:	Miguel G. Silva [verfasserIn] Sara C. Madeira [verfasserIn] Rui Henriques [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	water consumption analysis biclustering time series pattern discovery clustering subspace clustering

Übergeordnetes Werk:	In: Water - MDPI AG, 2010, 14(2022), 12, p 1954
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:12, p 1954

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/w14121954

Katalog-ID:	DOAJ021093830

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520			\|a Sensors deployed within water distribution systems collect consumption data that enable the application of data analysis techniques to extract essential information. Time series clustering has been traditionally applied for modeling end-user water consumption profiles to aid water management. However, its effectiveness is limited by the diversity and local nature of consumption patterns. In addition, existing techniques cannot adequately handle changes in household composition, disruptive events (e.g., vacations), and consumption dynamics at different time scales. In this context, biclustering approaches provide a natural alternative to detect groups of end-users with coherent consumption profiles during local time periods while addressing the aforementioned limitations. This work discusses when, why and how to apply biclustering techniques for water consumption data analysis, and further proposes a methodology to this end. To the best of our knowledge, this is the first work introducing biclustering to water consumption data analysis. Results on data from a real-world water distribution system—Quinta do Lago, Portugal—confirm the potentialities of the proposed approach for pattern discovery with guarantees of statistical significance and robustness that entities can rely on for strategic planning.
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allfieldsSound	10.3390/w14121954 doi (DE-627)DOAJ021093830 (DE-599)DOAJ85936a939db04aeb9c56e1debb511c68 DE-627 ger DE-627 rakwb eng TC1-978 TD201-500 Miguel G. Silva verfasserin aut Water Consumption Pattern Analysis Using Biclustering: When, Why and How 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Sensors deployed within water distribution systems collect consumption data that enable the application of data analysis techniques to extract essential information. Time series clustering has been traditionally applied for modeling end-user water consumption profiles to aid water management. However, its effectiveness is limited by the diversity and local nature of consumption patterns. In addition, existing techniques cannot adequately handle changes in household composition, disruptive events (e.g., vacations), and consumption dynamics at different time scales. In this context, biclustering approaches provide a natural alternative to detect groups of end-users with coherent consumption profiles during local time periods while addressing the aforementioned limitations. This work discusses when, why and how to apply biclustering techniques for water consumption data analysis, and further proposes a methodology to this end. To the best of our knowledge, this is the first work introducing biclustering to water consumption data analysis. Results on data from a real-world water distribution system—Quinta do Lago, Portugal—confirm the potentialities of the proposed approach for pattern discovery with guarantees of statistical significance and robustness that entities can rely on for strategic planning. water consumption analysis biclustering time series pattern discovery clustering subspace clustering Hydraulic engineering Water supply for domestic and industrial purposes Sara C. Madeira verfasserin aut Rui Henriques verfasserin aut In Water MDPI AG, 2010 14(2022), 12, p 1954 (DE-627)611729008 (DE-600)2521238-2 20734441 nnns volume:14 year:2022 number:12, p 1954 https://doi.org/10.3390/w14121954 kostenfrei https://doaj.org/article/85936a939db04aeb9c56e1debb511c68 kostenfrei https://www.mdpi.com/2073-4441/14/12/1954 kostenfrei https://doaj.org/toc/2073-4441 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 12, p 1954
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source	In Water 14(2022), 12, p 1954 volume:14 year:2022 number:12, p 1954
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callnumber-first	T - Technology
author	Miguel G. Silva
spellingShingle	Miguel G. Silva misc TC1-978 misc TD201-500 misc water consumption analysis misc biclustering misc time series misc pattern discovery misc clustering misc subspace clustering misc Hydraulic engineering misc Water supply for domestic and industrial purposes Water Consumption Pattern Analysis Using Biclustering: When, Why and How
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topic	misc TC1-978 misc TD201-500 misc water consumption analysis misc biclustering misc time series misc pattern discovery misc clustering misc subspace clustering misc Hydraulic engineering misc Water supply for domestic and industrial purposes
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title	Water Consumption Pattern Analysis Using Biclustering: When, Why and How
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abstract	Sensors deployed within water distribution systems collect consumption data that enable the application of data analysis techniques to extract essential information. Time series clustering has been traditionally applied for modeling end-user water consumption profiles to aid water management. However, its effectiveness is limited by the diversity and local nature of consumption patterns. In addition, existing techniques cannot adequately handle changes in household composition, disruptive events (e.g., vacations), and consumption dynamics at different time scales. In this context, biclustering approaches provide a natural alternative to detect groups of end-users with coherent consumption profiles during local time periods while addressing the aforementioned limitations. This work discusses when, why and how to apply biclustering techniques for water consumption data analysis, and further proposes a methodology to this end. To the best of our knowledge, this is the first work introducing biclustering to water consumption data analysis. Results on data from a real-world water distribution system—Quinta do Lago, Portugal—confirm the potentialities of the proposed approach for pattern discovery with guarantees of statistical significance and robustness that entities can rely on for strategic planning.
abstractGer	Sensors deployed within water distribution systems collect consumption data that enable the application of data analysis techniques to extract essential information. Time series clustering has been traditionally applied for modeling end-user water consumption profiles to aid water management. However, its effectiveness is limited by the diversity and local nature of consumption patterns. In addition, existing techniques cannot adequately handle changes in household composition, disruptive events (e.g., vacations), and consumption dynamics at different time scales. In this context, biclustering approaches provide a natural alternative to detect groups of end-users with coherent consumption profiles during local time periods while addressing the aforementioned limitations. This work discusses when, why and how to apply biclustering techniques for water consumption data analysis, and further proposes a methodology to this end. To the best of our knowledge, this is the first work introducing biclustering to water consumption data analysis. Results on data from a real-world water distribution system—Quinta do Lago, Portugal—confirm the potentialities of the proposed approach for pattern discovery with guarantees of statistical significance and robustness that entities can rely on for strategic planning.
abstract_unstemmed	Sensors deployed within water distribution systems collect consumption data that enable the application of data analysis techniques to extract essential information. Time series clustering has been traditionally applied for modeling end-user water consumption profiles to aid water management. However, its effectiveness is limited by the diversity and local nature of consumption patterns. In addition, existing techniques cannot adequately handle changes in household composition, disruptive events (e.g., vacations), and consumption dynamics at different time scales. In this context, biclustering approaches provide a natural alternative to detect groups of end-users with coherent consumption profiles during local time periods while addressing the aforementioned limitations. This work discusses when, why and how to apply biclustering techniques for water consumption data analysis, and further proposes a methodology to this end. To the best of our knowledge, this is the first work introducing biclustering to water consumption data analysis. Results on data from a real-world water distribution system—Quinta do Lago, Portugal—confirm the potentialities of the proposed approach for pattern discovery with guarantees of statistical significance and robustness that entities can rely on for strategic planning.
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