Journal of Korean Society of Water and Wastewater (상하수도학회지)

The Korean Society of Water and Wastewater (대한상하수도학회)
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Water consumption forecasting and pattern classification according to demographic factors and automated meter reading

인구통계학적 요인 및 원격검침 자료를 활용한 가정용 물 사용패턴 분류 및 물 사용량 예측 연구

  • Kim, Kibum (Division of Construction Engineering and Management, Purdue University) ;
  • Park, Haekeum (Department of Environmental Engineering, University of Seoul) ;
  • Kim, Taehyeon (Department of Environmental Engineering, University of Seoul) ;
  • Hyung, Jinseok (Department of Environmental Engineering, University of Seoul) ;
  • Koo, Jayong (Department of Environmental Engineering, University of Seoul)
  • Received : 2022.04.20
  • Accepted : 2022.05.02
  • Published : 2022.06.15
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Abstract

The water consumption data of individual consumers must be analyzed and forecast to establish an effective water demand management plan. A k-mean cluster model that can monitor water use characteristics based on hourly water consumption data measured using automated meter reading devices and demographic factors is developed in this study. In addition, the quantification model that can estimate the daily water consumption is developed. K-mean cluster analysis based on the four clusters shows that the average silhouette coefficient is 0.63, also the silhouette coefficients of each cluster exceed 0.60, thereby verifying the high reliability of the cluster analysis. Furthermore, the clusters are clearly classified based on water usage and water usage patterns. The correlation coefficients of four quantification models for estimating water consumption exceed 0.74, confirming that the models can accurately simulate the investigated demographic data. The statistical significance of the models is considered reasonable, hence, they are applicable to the actual field. Because the use of automated smart water meters has become increasingly popular in recent year, water consumption has been metered remotely in many areas. The proposed methodology and the results obtained in this study are expected to facilitate improvements in the usability of smart water meters in the future.

Keywords

Acknowledgement

본 연구논문은 서울시립대학교 교내학술연구비(201904301063)에 의하여 지원되었습니다.

References

  1. Blokker, E., Vreeburg, J. and Van Dijk, J. (2010). Simulating residential water demand with a stochastic end-use model, J. Water Res. Plan. Man., 136(1), 19-26. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000002
  2. Cardell-Oliver, R., Wang, J. and Gigney, H. (2016). Smart meter analytics to pinpoint opportunities for reducing household water use, J. Water Res. Plan. Man., 142(6), 04016007.
  3. Cominola, A., Giuliani, M. Castelletti, A., Resenberg, D. and Abdallah, A. (2018). Implications of data sampling resolution on water use simulation, end-use disaggregation, and demand management, Environ. Modell. Softw., 102, 199-212. https://doi.org/10.1016/j.envsoft.2017.11.022
  4. Cominola, A., Giuliani, M., Piga, D., Castelletti, A. and Rizzoli, A. (2015). Benefits and challenges of using smart meters for advancing residential water demand modeling and management: A review, Environ. Modell. Softw., 72, 198-214. https://doi.org/10.1016/j.envsoft.2015.07.012
  5. Creaco, E., Blokker, M. and Buchberger, S. (2017). Models for generating household water demand pulse: Literature review and comparison, J. Water Res. Plan. Man., 143(6), 04017013.
  6. Di Nardo, A., Di Natale, M., Gargano, R., Giudicianni, C., Greco, R. and Santonastaso, G. (2018). Performance of partitioned water distribution networks under sparial-temporal variability of water demand, Environ. Modell. Softw., 101, 128-136. https://doi.org/10.1016/j.envsoft.2017.12.020
  7. Gargano, R., Di Palma, F., De Marinis, G., Granata, F. and Greco, R. (2016). A stochastic approach for the water demand of residential end users, Urban Water J., 13(6), 569-582. https://doi.org/10.1080/1573062X.2015.1011666
  8. Gulisano, V., Almgren, M. and Papatriantafilou, M. (2014). "Online and scalable data validation in advanced metering infrastructure", In IEEE PES Innovative Smart Grid Technologies, Europe, IEEE, 1-6.
  9. Gurung, T., Stewart, R., Beal, C. and Sharma, A. (2016). Smart meter enabled informatics for economically efficient diversified water supply infrastructure planning, J. Clean Prod., 135, 1023-1033. https://doi.org/10.1016/j.jclepro.2016.07.017
  10. Kofinas, D., Mellios, N., Papageorgiou, E. and Laspidou, C. (2014). Urban water demand forecasting for the island of skiathos, Proced. Eng., 89, 1023-1030. https://doi.org/10.1016/j.proeng.2014.11.220
  11. Koo, K., Han, K., Jun, K., Lee, G., Kim, J. and Yum, K. (2021). Performance assessment for short-term water demand forecasting models on distinctive water uses in Korea, Sustainability, 13(11), 6056.
  12. Luciani, C., Casellato, F., Alvisi, S. and Franchini, M. (2019). Green smart technology for water (GST4Water): water loss identification at user level by using smart metering systgems, Water, 11(3), 405.
  13. Mayer, P. and DeOreo, W. (1999). Residential end uses of water, American Water Works Association Research Foundation(AWWARF), Denver, CO, United States.
  14. Osman, M., Abu-Mahfouz, A. and Page, P. (2018). A survey on data imputation techniques: Water distribution system as a use case, IEEE Access, 6, 63279-63291. https://doi.org/10.1109/ACCESS.2018.2877269
  15. Pesantez, J., Berglund, E. and Kaza, N. (2020). Smart meters data for modeling and forecasting water demand at the user-level, Environ. Modell. Softw., 125, 104633.
  16. Shang, F., Uber, J., Waanders, B. and Boccelli, D. (2006). "Real time water demand estimation in water distribution system", In Water Distribution Systems Analysis (WDSA) 2006, Cincinnati, OH, United States.
  17. Sonderlund, A., Smith, J., Hutton, C. and Kapelan, Z. (2014). Using smart meters for household water consumption feedback, Proced. Eng., 89, 990-997. https://doi.org/10.1016/j.proeng.2014.11.216
  18. Yoo, S. and Chae, K. (2001). Measuring the economic benefits of the ozone pollution control policy in Seoul: Results of a contingent valuation survey, Urban Stud., 38(1), 49-60. https://doi.org/10.1080/00420980020014802