A comparative analysis of water meter data recording systems using time series similarity measures
DOI:
https://doi.org/10.17512/INSTAL.2026.10.03Keywords:
water consumption, water supply networks, water meters, remote reading, smart meteringAbstract
Water consumption is increasingly measured using automatic meter reading (AMR) systems. This involves the use of electronic modules for data recording and transmission, which, depending on the solution, may also enable preliminary analysis of measurements within the device. This paper presents a comparative analysis of water consumption data recording in a single-family house. Water consumption was measured using volumetric water meters with a diameter of DN 20 and a measuring range ratio of R160, and the resulting measurements served as input data for the recording and transmission systems. Recordings obtained from two different devices connected to the water meters were compared within the same water installation and over the same period. The aim of this study is to determine differences in the recording and processing of information resulting from the use of two different recording systems and to assess the usefulness and similarity of the acquired data. In addition, the significance of the observed differences was evaluated using selected time series similarity measures. The study reveals clear temporal variability in water consumption, characterized by a daily rhythm and differences between weekdays and weekends. Differences in recordings made at minute intervals are important primarily for the analysis of short-term events and flow dynamics. At the hourly aggregation level, both data recording systems show high agreement and yield comparable results under the tested conditions, especially in water balance analyses. Longer data aggregation periods reduce the impact of differences in sampling and data recording methods and improve agreement between the two recording approaches, as confirmed in this study.
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[1] Cichoń, T., & Królikowska, J. (2024). Rola i znaczenie inteligentnych wodomierzy w systemie zaopatrzenia w wodę. INSTAL, (12), 38–42. https://doi.org/10.36119/15.2024.12.7
[2] Stańczyk, J., Ramm, K., & Licznar, P. (2025). Assessing the operating condition of a domestic distribution system based on the power spectra of high--frequency water consumption signals. Water Research, 278, 123320. https://doi.org/10.1016/j.watres.2025.123320
[3] Almeida, G., Vieira, J., Marques, J., & Cardoso, A. (2011). Pattern recognition of the household water consumption through signal analysis. In L. M. Camarinha-Matos (Ed.), Technological innovation for sustainability: DoCEIS 2011 (IFIP Advances in Information and Communication Technology, Vol. 349, pp. 349–356). Springer. https://doi.org/10.1007/978-3-642-19170-1_38
[4] Cominola, A., Nguyen, K., Giuliani, M., Stewart, R. A., Maier, H. R., & Castelletti, A. (2019). Data mining to uncover heterogeneous water use behaviors from smart meter data. Water Resources Research, 55(11), 9315–9333. https://doi.org/10.1029/2019WR024897
[5] Ritu. (2024). The role of smart technologies in water distribution management. Frontiers in Health Informatics, 13(6), 1404–1412.
[6] Bełcik, M., & Nowakowska, M. (2026). Zastosowanie sztucznych sieci neuronowych do predykcji rozbioru wody na potrzeby optymalizacji eksploatacji sieci. INSTAL, (7), 27–31. https://doi.org/10.17512/INSTAL.2026.07.02
[7] Koral, W. (2019). Research in unitary water consumption in multifamily building for daily recording. INSTAL, (11), 46–49. https://doi.org/10.36119/15.2019.11.5
[8] Torres-Gutierrez, J. L., Castañeda-Miranda, C. L., Martínez-Blanco, M. D. R., Guerrero-Osuna, H. A., Jiménez-Díaz, G., Espinoza-García, G., Moreno--Lucio, M., Ibarra-Pérez, T., & Solís-Sánchez, L. O. (2026). Development and implementation of a smart water metering and monitoring system for homes with intermittent water supply. Technologies, 14(2), 135. https://doi.org/10.3390/technologies14020135
[9] Khaki, M., & Mortazavi, N. (2022). Water consumption demand pattern analysis using uncertain smart water meter data. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence (ICAART 2022) (Vol. 3, pp. 436443). SciTePress. https://doi.org/10.5220/0010834900003116
[10] Obaid, S., Hosoi, K., Ngoc, N. M., Inoue, T., & Yokota, K. (2024). Assessment of water consumption behavior in single households using smart water meters. Applied Sciences, 14(19), 8857. https://doi.org/10.3390/app14198857
[11] Dzimińska, P., Stańczyk, J., Pałczyński, K., Licznar, P., & Andrysiak, T. (2024). Potencjał algorytmów inteligencji obliczeniowej w interpretacji rozproszonych danych wodomierzowych. INSTAL, (1), 36–43. https://doi.org/10.36119/15.2024.1.5
[12] Ustawa z dnia 20 kwietnia 2021 r. o zmianie ustawy o efektywności energetycznej oraz niektórych innych ustaw, Dz.U. 2021 poz. 868; Dyrektywa Parlamentu Europejskiego i Rady (UE) 2018/2002 z dnia 11 rudnia 2018 r. zmieniająca dyrektywę 2012/27/UE w sprawie efektywności energetycznej.
[13] Cichoń, T., & Królikowska, J. (2021). Remote reading of water meters as an element of a smart city concept. Rocznik Ochrona Środowiska, 23, 883–890. https://doi.org/10.54740/ros.2021.060
[14] Doğan, N. Ö. (2018). Bland-Altman analysis: A paradigm to understand correlation and agreement. Turkish Journal of Emergency Medicine, 18(4), 139–141. https://doi.org/10.1016/j.tjem.2018.09.001
[15] Fadlalla, D., Holagh, S. G., Ahmed, W. H., Weales, D., & Moussa, M. (2024). Using machine vision algorithms for characterizing gas-liquid slug flows in vertical pipes. Flow Measurement and Instrumentation, 99, 102671. https://doi.org/10.1016/j.flowmeasinst.2024.102671
[16] Bland, J. M., & Altman, D. G. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. The Lancet, 327(8476), 307–310. https://doi.org/10.1016/S0140-6736(86)90837-8
[17] Mazzoni, F., Marsili, V., & Alvisi, S. (2025). Characterizing hot-water consumption at household and end-use levels based on smart-meter data. Water, 17(13), 1906. https://doi.org/10.3390/w17131906
[18] Wang, R., Zhao, X., Qiu, H., Cheng, X., & Liu, X. (2024). Uncovering urban water consumption patterns through time series clustering and entropy analysis. Water Research, 262, 122085. https://doi.org/10.1016/j.watres.2024.122085
[19] Naseri, M. Y., Bernosky, G., Mayer, P. W., & Marston, L. T. (2025). Patterns and predictors of residential indoor water use across major US cities [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-5098971/v2
[20] Borchling, A., Kroner, C., & Tränckner, J. (2022). Effect of the sampling interval on the measurement accuracy of electronic water meters under real consumption conditions. Water Supply, 22(12), 8405–8417. https://doi.org/10.2166/ws.2022.404
[21] Pavlou, P. V., Filippou, S., Solonos, S., Vrachimis, S. G., Malialis, K., Eliades, D. G., Theocarides, T., & Polycarpou, M. M. (2024). Monitoring domestic water consumption: A comparative study of model-based and data-driven end-use disaggregation methods. Journal of Hydroinformatics, 26(4), 709–726. https://doi.org/10.2166/hydro.2024.120
[22] Knutsson, J. (2026). Household water consumption patterns revealed through high-resolution fixture-level monitoring in a Swedish living lab [Preprint]. SSRN. https://doi.org/10.2139/ssrn.6657318
[23] Naseri, Y., Bernosky, G., Mayer, P., & Marston, L. (2026). Analysis of fixture efficiency and behavioral factors of indoor residential water use of single--family households. Water Resources Research, 62, e2025WR040452. https://doi.org/10.1029/2025WR040452
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Copyright (c) 2026 Tomasz Cichoń, Justyna Stańczyk, Ewa Burszta-Adamiak (Autor)
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