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Building Simulation

, Volume 10, Issue 6, pp 949–962 | Cite as

A preliminary investigation of water usage behavior in single-family homes

  • Peng XueEmail author
  • Tianzhen Hong
  • Bing Dong
  • Cheukming Mak
Research Article

Abstract

As regional drought conditions continue deteriorating around the world, residential water use has been brought into the built environment spotlight. Nevertheless, the understanding of water use behavior in residential buildings is still limited. This paper presents data analytics and results from monitoring data of daily water use (DWU) in 50 single-family homes in Texas, USA. The results show the typical frequency distribution curve of the DWU per household and indicate personal income, education level and energy use of appliances all have statistically significant effects on the DWU per capita. Analysis of the water-intensive use demonstrates the residents tend to use more water in post-vacation days. These results help generate awareness of water use behavior in homes. Ultimately, this research could support policy makers to establish a water use baseline and inform water conservation programs.

Keywords

water usage behavior daily water use data analytics occupant behavior residential water consumption 

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Notes

Acknowledgements

This work is supported by the Assistant Secretary for Energy Efficiency and Renewable Energy of the U.S. Department of Energy under contract number DE-AC02-05CH11231. It is also part of the research activities of International Energy Agency Energy in Buildings and Communities Program Annex 66, definition and simulation of occupant behavior in buildings. The source data were provided by Pecan Street, Inc. (http://www.pecanstreet.org/), headquartered in Austin, TX. The authors thank this nonprofit research institute for allowing us access to their subscriber water usage database.

References

  1. Abdallah AM, Rosenberg DE (2012). Heterogeneous residential water and energy linkages and implications for conservation and management. Journal of Water Resources Planning and Management, 140: 288–297.CrossRefGoogle Scholar
  2. Aquacraft (2015). Application of end use study data for development of residential demand models. Available at http://www.aquacraft.com/wp-content/uploads/2015/09/Residential-Models.pdf.Google Scholar
  3. Balling RC, Gober P, Jones N (2008). Sensitivity of residential water consumption to variations in climate: An intraurban analysis of Phoenix, Arizona. Water Resources Research, 44: W10401.CrossRefGoogle Scholar
  4. Breyer B, Chang H, Parandvash GH (2012). Land-use, temperature, and single-family residential water use patterns in Portland, Oregon and Phoenix, Arizona. Applied Geography, 35: 142–151.CrossRefGoogle Scholar
  5. Bullock DC, Peebles RW, Smith HH (1980). Water usage patterns in the U.S. Virgin Islands. Water Resources Research Center, Caribbean Research Institute, College of the Virgin Islands.Google Scholar
  6. Campbell HE, Johnson RM, Larson EH (2004). Prices, devices, people, or rules: The relative effectiveness of policy instruments in water conservation. Review of Policy Research, 21: 637–662.CrossRefGoogle Scholar
  7. Cardell-Oliver R, Wang J, Gigney H (2016). Smart meter analytics to pinpoint opportunities for reducing household water use. Journal of Water Resources Planning and Management, 142(6): 04016007.CrossRefGoogle Scholar
  8. Chang H, Parandvash GH, Shandas V (2010). Spatial variations of single-family residential water consumption in Portland, Oregon. Urban geography, 31: 953–972.CrossRefGoogle Scholar
  9. Chu J, Wang C, Chen J, Wang H (2009). Agent-based residential water use behavior simulation and policy implications: A case-study in Beijing City. Water Resources Management, 23: 3267–3295.CrossRefGoogle Scholar
  10. Cominola A, Giuliani M, Piga D, Castelletti A, Rizzoli AE (2015). Benefits and challenges of using smart meters for advancing residential water demand modeling and management: A review. Environmental Modelling & Software, 72: 198–214.CrossRefGoogle Scholar
  11. Corral-Verdugo V, Bechtel RB, Fraijo-Sing B (2003). Environmental beliefs and water conservation: An empirical study. Journal of Environmental Psychology, 23: 247–257.CrossRefGoogle Scholar
  12. Cosgrove WJ, Rijsberman FR (2000). World Water Vision: Making Water Everybody’s Business. London: Earthscan Publications.Google Scholar
  13. DeOreo WB, Heaney JP, Mayer PW (1996). Flow trace analysis to assess water use. Journal—American Water Works Association, 88(1): 79–90.Google Scholar
  14. DeOreo WB, Mayer PW (1994). Project report: A process approach for measuring residential water use and assessing conservation effectiveness. City of Boulder Office of Water Conservation, Boulder, CO, USA.Google Scholar
  15. DeOreo WB, Mayer PW, Martien L, et al. (2011). California singlefamily water use efficiency study. Aquacraft Water Engineering and Management, Boulder, CO, USA. Available at http://water.cityofdavis.org/Media/PublicWorks/Documents/PDF/PW/Water/Documents/California-Single-Family-Home-Water-Use-Efficiency-Study-20110420.pdfGoogle Scholar
  16. Dong B, Li Z, Mcfadden G (2015). An investigation on energy-related occupancy behavior for low-income residential buildings. Science and Technology for the Built Environment, 21: 892–901.CrossRefGoogle Scholar
  17. Fox C, McIntosh BS, Jeffrey P (2009). Classifying households for water demand forecasting using physical property characteristics. Land Use Policy, 26: 558–568.CrossRefGoogle Scholar
  18. Grafton RQ, Ward MB, To H, Kompas T (2011). Determinants of residential water consumption: Evidence and analysis from a 10- country household survey. Water Resources Research, 47: W08537.Google Scholar
  19. Harlan SL, Yabiku ST, Larsen L, Brazel AJ (2009). Household water consumption in an arid city: Affluence, affordance, and attitudes. Society and Natural Resources, 22: 691–709.CrossRefGoogle Scholar
  20. Heberger M, Cooley H, Gleick P (2014). Urban water conservation and efficiency potential in California. Available at http://www.nrdc.org/water/files/ca-water-supply-solutions-urban-IB.pdfGoogle Scholar
  21. Hong T, Taylor-Lange SC, D’Oca S, Yan D, Corgnati SP (2016). Advances in research and applications of energy-related occupant behavior in buildings. Energy and Buildings, 116: 694–702.CrossRefGoogle Scholar
  22. House-Peters LA, Chang H (2011). Urban water demand modeling: Review of concepts, methods, and organizing principles. Water Resources Research, 47: W05401.CrossRefGoogle Scholar
  23. Inman D, Jeffrey P (2006). A review of residential water conservation tool performance and influences on implementation effectiveness. Urban Water Journal, 3: 127–143.CrossRefGoogle Scholar
  24. Kenney DS, Goemans C, Klein R, Lowrey J, Reidy K (2008). Residential water demand management: Lessons from Aurora, Colorado. Journal of the American Water Resources Association, 44: 192–207.CrossRefGoogle Scholar
  25. Kontokosta CE, Jain RK (2015). Modeling the determinants of largescale building water use: Implications for data-driven urban sustainability policy. Sustainable Cities and Society, 18: 44–55.CrossRefGoogle Scholar
  26. Lutz J (2012). Hot water draw patterns in single-family houses: findings from field studies. Lawrence Berkeley National Laboratory, LBNL Paper LBNL-4830E. Available at http://escholarship.org/uc/item/2k24v1kj.Google Scholar
  27. Malinowski PA, Stillwell AS, Wu JS, Schwarz PM (2015). Energy-water nexus: Potential energy savings and implications for sustainable integrated water management in urban areas from rainwater harvesting and gray-water reuse. Journal of Water Resources Planning and Management, 141(12): A4015003.CrossRefGoogle Scholar
  28. Maupin MA, Kenny JF, Hutson SS, Lovelace JK, Barber NL, Linsey KS (2014). Estimated use of water in the United States in 2010. Reston, VA, USA: U.S. Geological Survey.CrossRefGoogle Scholar
  29. Mayer PW (2009). Water efficiency benchmarks for new single-family homes. Aquacraft Water Engineering and Management, Boulder, CO, USA. Available at http://www.watersmartinnovations.com/documents/pdf/2009/sessions/T-1009.pdfGoogle Scholar
  30. Mayer PW, DeOreo WB, Opitz EM, et al. (1999). Residential end uses of water. AWWA Research Foundation and American Water Works Association, Denver, CO, USA.Google Scholar
  31. Mazzanti M, Montini A (2006). The determinants of residential water demand: Empirical evidence for a panel of Italian municipalities. Applied Economics Letters, 13: 107–111.CrossRefGoogle Scholar
  32. Ouyang Y, Wentz EA, Ruddell BL, Harlan SL (2013). A multi-scale analysis of single-family residential water use in the Phoenix metropolitan area. Journal of the American Water Resources Association, 50: 448–467.CrossRefGoogle Scholar
  33. Papakostas KT, Papageorgiou NE, Sotiropoulos BA (1995). Residential hot water use patterns in Greece. Solar Energy, 54: 369–374.CrossRefGoogle Scholar
  34. Polebitski AS, Palmer RN, Waddell P (2011). Evaluating water demands under climate change and transitions in the urban environment. Journal of Water Resources Planning and Management, 137: 249–257.CrossRefGoogle Scholar
  35. Praskievicz S, Chang H (2009). Identifying the relationships between urban water consumption and weather variables in Seoul, Korea. Physical Geography, 30: 324–337.CrossRefGoogle Scholar
  36. Richter CP (2010). Automatic dishwashers: Efficient machines or less efficient consumer habits? International Journal of Consumer Studies, 34: 228–234.CrossRefGoogle Scholar
  37. Rosenberg DE, Madani K (2014). Water resources systems analysis: A bright past and a challenging but promising future. Journal of Water Resources Planning and Management, 140: 407–409.CrossRefGoogle Scholar
  38. Sanders R (1987). The Pareto principle: Its use and abuse. Journal of Services Marketing, 1(2): 37–40.CrossRefGoogle Scholar
  39. Schleich J, Hillenbrand T (2009). Determinants of residential water demand in Germany. Ecological Economics, 68: 1756–1769.CrossRefGoogle Scholar
  40. Suero FJ, Mayer PW, Rosenberg DE (2012). Estimating and verifying United States households’ potential to conserve water. Journal of Water Resources Planning and Management, 138: 299–306.CrossRefGoogle Scholar
  41. Tinker A, Bame S, Burt R, Speed M (2005). Impact of “non-behavioral fixed effects” on water use: Weather and economic construction differences on residential water use in Austin, Texas. Electronic Green Journal, 1(22). Available at http://escholarship.org/uc/item/7rh33286.Google Scholar
  42. USGS (2015). State drought information. U.S. Geological Survey, U.S. Department of the Interior. Available at http://waterwatch.usgs.gov/index.php?r=us&m=dryw.Google Scholar
  43. Wentz EA, Wills AJ, Kim WK, Myint SW, Gober P, Balling Jr RC (2014). Factors influencing water consumption in multifamily housing in Tempe, Arizona. The Professional Geographer, 66: 501–510.CrossRefGoogle Scholar
  44. WHO, UNICEF (2015). Progress on sanitation and drinking water: 2015 update and MDG assessment. World Health Organization. Available at http://apps.who.int/iris/bitstream/10665/177752/1/9789241509145_eng.pdf?ua=1.Google Scholar
  45. Willis RM, Stewart RA, Panuwatwanich K, Jones S, Kyriakides A (2010). Alarming visual display monitors affecting shower end use water and energy conservation in Australian residential households. Resources, Conservation and Recycling, 54: 1117–1127.CrossRefGoogle Scholar
  46. Yan D, O’Brien W, Hong T, Feng X, Gunay BH, Tahmasebi F, Mahdavi A (2015). Occupant behavior modeling for building performance simulation: Current state and future challenges. Energy and Buildings, 107: 264–278.CrossRefGoogle Scholar
  47. Zar JH (1972). Significance testing of the Spearman rank correlation coefficient. Journal of the American Statistical Association, 67: 578–580.CrossRefzbMATHGoogle Scholar
  48. Zhang HH, Brown DF (2005). Understanding urban residential water use in Beijing and Tianjin, China. Habitat International, 29: 469–491.CrossRefGoogle Scholar

Copyright information

© Tsinghua University Press and Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Peng Xue
    • 1
    • 2
    Email author
  • Tianzhen Hong
    • 2
  • Bing Dong
    • 3
  • Cheukming Mak
    • 4
  1. 1.Beijing Key Laboratory of Green Built Environment and Energy Efficient TechnologyBeijing University of TechnologyBeijingChina
  2. 2.Building Technology and Urban Systems DivisionLawrence Berkeley National LaboratoryBerkeleyUSA
  3. 3.Department of Mechanical EngineeringUniversity of Texas at San AntonioSan AntonioUSA
  4. 4.Department of Building Services EngineeringThe Hong Kong Polytechnic UniversityHong KongChina

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