Water consumption forecasting and pattern classification according to demographic factors and automated meter reading |
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) |
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. DOI |
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. DOI |
4 | 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. DOI |
5 | 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. DOI |
6 | 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. |
7 | 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. |
8 | 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. DOI |
9 | 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. DOI |
10 | 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. |
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 | Mayer, P. and DeOreo, W. (1999). Residential end uses of water, American Water Works Association Research Foundation(AWWARF), Denver, CO, United States. |
13 | 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. DOI |
14 | Sonderlund, A., Smith, J., Hutton, C. and Kapelan, Z. (2014). Using smart meters for household water consumption feedback, Proced. Eng., 89, 990-997. DOI |
15 | 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. |
16 | 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. DOI |
17 | Kofinas, D., Mellios, N., Papageorgiou, E. and Laspidou, C. (2014). Urban water demand forecasting for the island of skiathos, Proced. Eng., 89, 1023-1030. DOI |
18 | 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. |