TY - JOUR
T1 - Is smart water meter temporal resolution a limiting factor to residential water end-use classification? A quantitative experimental analysis
AU - Heydari, Zahra
AU - Cominola, Andrea
AU - Stillwell, Ashlynn S.
N1 - Thanks to the study home occupants for their participation in recording the water diary. The custom ally® water meter used in this analysis was provided by Sensus. Thanks to Marie-Philine Gross (Technische Universität Berlin) and Riccardo Taormina (TU Delft) for sharing the results reproduced in figures 1(b), S1(b) and S2(b). This work was supported by the National Science Foundation, Grant CBET-1847404; the opinions, findings, and conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of the National Science Foundation.
Thanks to the study home occupants for their participation in recording the water diary. The custom ally water meter used in this analysis was provided by Sensus. Thanks to Marie-Philine Gross (Technische Universität Berlin) and Riccardo Taormina (TU Delft) for sharing the results reproduced in figures (b), S1(b) and S2(b). This work was supported by the National Science Foundation, Grant CBET-1847404; the opinions, findings, and conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of the National Science Foundation. ®
PY - 2022/12/1
Y1 - 2022/12/1
N2 - Water monitoring in households provides occupants and utilities with key information to support water conservation and efficiency in the residential sector. High costs, intrusiveness, and practical complexity limit appliance-level monitoring via sub-meters on every water-consuming end use in households. Non-intrusive machine learning methods have emerged as promising techniques to analyze observed data collected by a single meter at the inlet of the house and estimate the disaggregated contribution of each water end use. While fine temporal resolution data allow for more accurate end-use disaggregation, there is an inevitable increase in the amount of data that needs to be stored and analyzed. To explore this tradeoff and advance previous studies based on synthetic data, we first collected 1 s resolution indoor water use data from a residential single-point smart water metering system installed at a four-person household, as well as ground-truth end-use labels based on a water diary recorded over a 4-week study period. Second, we trained a supervised machine learning model (random forest classifier) to classify six water end-use categories across different temporal resolutions and two different model calibration scenarios. Finally, we evaluated the results based on three different performance metrics (micro, weighted, and macro F1 scores). Our findings show that data collected at 1- to 5-s intervals allow for better end-use classification (weighted F-score higher than 0.85), particularly for toilet events; however, certain water end uses (e.g., shower and washing machine events) can still be predicted with acceptable accuracy even at coarser resolutions, up to 1 min, provided that these end-use categories are well represented in the training dataset. Overall, our study provides insights for further water sustainability research and widespread deployment of smart water meters.
AB - Water monitoring in households provides occupants and utilities with key information to support water conservation and efficiency in the residential sector. High costs, intrusiveness, and practical complexity limit appliance-level monitoring via sub-meters on every water-consuming end use in households. Non-intrusive machine learning methods have emerged as promising techniques to analyze observed data collected by a single meter at the inlet of the house and estimate the disaggregated contribution of each water end use. While fine temporal resolution data allow for more accurate end-use disaggregation, there is an inevitable increase in the amount of data that needs to be stored and analyzed. To explore this tradeoff and advance previous studies based on synthetic data, we first collected 1 s resolution indoor water use data from a residential single-point smart water metering system installed at a four-person household, as well as ground-truth end-use labels based on a water diary recorded over a 4-week study period. Second, we trained a supervised machine learning model (random forest classifier) to classify six water end-use categories across different temporal resolutions and two different model calibration scenarios. Finally, we evaluated the results based on three different performance metrics (micro, weighted, and macro F1 scores). Our findings show that data collected at 1- to 5-s intervals allow for better end-use classification (weighted F-score higher than 0.85), particularly for toilet events; however, certain water end uses (e.g., shower and washing machine events) can still be predicted with acceptable accuracy even at coarser resolutions, up to 1 min, provided that these end-use categories are well represented in the training dataset. Overall, our study provides insights for further water sustainability research and widespread deployment of smart water meters.
KW - residential water use
KW - smart water meter
KW - supervised machine learning
KW - temporal resolution
KW - water sustainability
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U2 - 10.1088/2634-4505/ac8a6b
DO - 10.1088/2634-4505/ac8a6b
M3 - Article
AN - SCOPUS:85144218062
SN - 2634-4505
VL - 2
JO - Environmental Research: Infrastructure and Sustainability
JF - Environmental Research: Infrastructure and Sustainability
IS - 4
M1 - 045004
ER -