IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v38y2024i13d10.1007_s11269-024-03898-6.html
   My bibliography  Save this article

From Pressure to Water Consumption: Exploiting High-Resolution Pressure Data to Investigate the End Uses of Water

Author

Listed:
  • Valentina Marsili

    (University of Ferrara)

  • Filippo Mazzoni

    (University of Ferrara)

  • Stefano Alvisi

    (University of Ferrara)

  • Marco Franchini

    (University of Ferrara)

Abstract

Highlights A method to investigate water consumption based on pressure data is developed. The method exploits the headloss-flowrate equation to obtain water-consumption data. The method is validated on a real case study, resulting in an average error of 2.3%. Limitations affecting the installation of domestic flow meters are overcome. Insights into the features of individual water-consumption events are provided.

Suggested Citation

  • Valentina Marsili & Filippo Mazzoni & Stefano Alvisi & Marco Franchini, 2024. "From Pressure to Water Consumption: Exploiting High-Resolution Pressure Data to Investigate the End Uses of Water," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 4969-4985, October.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:13:d:10.1007_s11269-024-03898-6
    DOI: 10.1007/s11269-024-03898-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-024-03898-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-024-03898-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sara Fontdecaba & José Sánchez-Espigares & Lluís Marco-Almagro & Xavier Tort-Martorell & Francesc Cabrespina & Jordi Zubelzu, 2013. "An Approach to Disaggregating Total Household Water Consumption into Major End-Uses," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2155-2177, May.
    2. Cominola, A. & Giuliani, M. & Piga, D. & Castelletti, A. & Rizzoli, A.E., 2017. "A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring," Applied Energy, Elsevier, vol. 185(P1), pages 331-344.
    3. Irene Marzola & Stefano Alvisi & Marco Franchini, 2022. "A Comparison of Model-Based Methods for Leakage Localization in Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5711-5727, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shi, Xin & Ming, Hao & Shakkottai, Srinivas & Xie, Le & Yao, Jianguo, 2019. "Nonintrusive load monitoring in residential households with low-resolution data," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    2. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Effective non-intrusive load monitoring of buildings based on a novel multi-descriptor fusion with dimensionality reduction," Applied Energy, Elsevier, vol. 279(C).
    3. Moreno Jaramillo, Andres F. & Laverty, David M. & Morrow, D. John & Martinez del Rincon, Jesús & Foley, Aoife M., 2021. "Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks," Renewable Energy, Elsevier, vol. 179(C), pages 445-466.
    4. Ce Peng & Guoying Lin & Shaopeng Zhai & Yi Ding & Guangyu He, 2020. "Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model," Energies, MDPI, vol. 13(21), pages 1-19, October.
    5. Qian Wu & Fei Wang, 2019. "Concatenate Convolutional Neural Networks for Non-Intrusive Load Monitoring across Complex Background," Energies, MDPI, vol. 12(8), pages 1-17, April.
    6. Song, Chunhe & Jing, Wei & Zeng, Peng & Yu, Haibin & Rosenberg, Catherine, 2018. "Energy consumption analysis of residential swimming pools for peak load shaving," Applied Energy, Elsevier, vol. 220(C), pages 176-191.
    7. Yini Ni & Yanghong Xia & Zichen Li & Qifan Feng, 2023. "A Non-Intrusive Identification Approach for Residential Photovoltaic Systems Using Transient Features and TCN with Attention Mechanisms," Sustainability, MDPI, vol. 15(20), pages 1-22, October.
    8. Jiang, Zhanhong & Liu, Chao & Akintayo, Adedotun & Henze, Gregor P. & Sarkar, Soumik, 2017. "Energy prediction using spatiotemporal pattern networks," Applied Energy, Elsevier, vol. 206(C), pages 1022-1039.
    9. Anthony Faustine & Lucas Pereira, 2020. "Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks," Energies, MDPI, vol. 13(13), pages 1-15, July.
    10. Dai, Shuang & Meng, Fanlin & Wang, Qian & Chen, Xizhong, 2024. "DP2-NILM: A distributed and privacy-preserving framework for non-intrusive load monitoring," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    11. Yan, Lei & Tian, Wei & Han, Jiayu & Li, Zuy, 2022. "Event-driven two-stage solution to non-intrusive load monitoring," Applied Energy, Elsevier, vol. 311(C).
    12. Ahir, Rajesh K. & Chakraborty, Basab, 2021. "A meta-analytic approach for determining the success factors for energy conservation," Energy, Elsevier, vol. 230(C).
    13. Dinesh, Chinthaka & Welikala, Shirantha & Liyanage, Yasitha & Ekanayake, Mervyn Parakrama B. & Godaliyadda, Roshan Indika & Ekanayake, Janaka, 2017. "Non-intrusive load monitoring under residential solar power influx," Applied Energy, Elsevier, vol. 205(C), pages 1068-1080.
    14. Hwan Kim & Sungsu Lim, 2021. "Temporal Patternization of Power Signatures for Appliance Classification in NILM," Energies, MDPI, vol. 14(10), pages 1-17, May.
    15. Welikala, Shirantha & Thelasingha, Neelanga & Akram, Muhammed & Ekanayake, Parakrama B. & Godaliyadda, Roshan I. & Ekanayake, Janaka B., 2019. "Implementation of a robust real-time non-intrusive load monitoring solution," Applied Energy, Elsevier, vol. 238(C), pages 1519-1529.
    16. Sara Tavakoli & Kaveh Khalilpour, 2021. "A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability," Energies, MDPI, vol. 14(16), pages 1-27, August.
    17. Bisaga, Iwona & Puźniak-Holford, Nathan & Grealish, Ashley & Baker-Brian, Christopher & Parikh, Priti, 2017. "Scalable off-grid energy services enabled by IoT: A case study of BBOXX SMART Solar," Energy Policy, Elsevier, vol. 109(C), pages 199-207.
    18. Benjamin Völker & Andreas Reinhardt & Anthony Faustine & Lucas Pereira, 2021. "Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective," Energies, MDPI, vol. 14(3), pages 1-21, January.
    19. Bonfigli, Roberto & Principi, Emanuele & Fagiani, Marco & Severini, Marco & Squartini, Stefano & Piazza, Francesco, 2017. "Non-intrusive load monitoring by using active and reactive power in additive Factorial Hidden Markov Models," Applied Energy, Elsevier, vol. 208(C), pages 1590-1607.
    20. María Isabel Berenguer & Manuel Ruiz Galán, 2022. "An Iterative Algorithm for Approximating the Fixed Point of a Contractive Affine Operator," Mathematics, MDPI, vol. 10(7), pages 1-10, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:38:y:2024:i:13:d:10.1007_s11269-024-03898-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.