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Computer Science - Research and Development

, Volume 33, Issue 1–2, pp 127–133 | Cite as

NIWM: non-intrusive water monitoring to uncover heat energy use in households

  • Samuel Schöb
  • Sebastian A. Günther
  • Karl Regensburger
  • Thorsten Staake
Special Issue Paper
  • 247 Downloads

Abstract

In Europe and the US, hot water use accounts for 13–18% of the average home’s energy consumption, compared to just 4 and 6% for lighting and cooking, respectively. As water heating mostly relies on oil, gas, and electricity, hot water use has been identified as an important target of many carbon reduction programs. We propose and describe a system that—comparable to non-intrusive load monitoring for electricity—disaggregates water extractions from a central metering device. The system can be used to provide consumption feedback, feed information into energy management systems, and can help to identify excessive water and energy use. The system relies on event-detection techniques and adapted Random Forest classifiers. We have tested and validated the system in two households over four months. The system was able to detect 85% of the extraction events which we then classify (“Dishwasher”, “Shower”, “Tap”, “Toilet”, and “Washing machine”). Random Forest achieves an F-measure between 71 and 91%. The area under the curve is above 0.9 for each appliance. We conclude that appliances are predicted reliably.

Keywords

Non-intrusive Water disaggregation Residential Machine learning 

References

  1. 1.
    Allcott H (2011) Social norms and energy conservation. J Public Econ 95(9–10):1082–1095CrossRefGoogle Scholar
  2. 2.
    Bertoldi P, Hirl B, Labanca N (2012) Energy Efficiency Status Report 2012. Tech. repGoogle Scholar
  3. 3.
    Bishop CM (2006) Pattern recognition and machine learning. Information science and statistics. Springer, New YorkzbMATHGoogle Scholar
  4. 4.
    Buchanan K, Russo R, Anderson B (2015) The question of energy reduction: the problem(s) with feedback. Energy Policy 77:89–96CrossRefGoogle Scholar
  5. 5.
    Carboni D, Gluhak A, McCann J, Beach T (2016) Contextualising water use in residential settings: a survey of non-intrusive techniques and approaches. Sensors 16(5):738CrossRefGoogle Scholar
  6. 6.
    Chen F, Dai J, Wang B, Sahu S, Naphade M, Lu C (2011) Activity analysis based on low sample rate smart meters. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, pp 240–248Google Scholar
  7. 7.
    Cole AI, Albicki A (1998) Algorithm for nonintrusive identification of residential appliances. In: Proceedings of the 1998 IEEE international symposium on circuits and systems, vol 3. Monterey, pp 338–341Google Scholar
  8. 8.
    DeOreo WB, Heaney JP, Mayer PW (1996) Flow trace analysis to assess water use. Am Water Works Assoc 88(1):79–90Google Scholar
  9. 9.
    Dong H, Wang B, Lu C (2013) Deep sparse coding based recursive disaggregation model for water conservation. In: Proceedings of the 23rd international joint conference on artificial intelligence. Beijing, pp 2804–2810Google Scholar
  10. 10.
    Ellert B, Makonin S, Popowich F (2015) Appliance water disaggregation via non-intrusive load monitoring (NILM). In: Proceedings of the EAI international conference on big data and analytics for smart cities, vol 166. Springer International Publishing, pp 455–467Google Scholar
  11. 11.
    Faruqui A, Sergici S, Sharif A (2010) The impact of informational feedback on energy consumption-a survey of the experimental evidence. Energy 35:1598–1608CrossRefGoogle Scholar
  12. 12.
    Fontdecaba S, Sánchez-Espigares JA, Marco-Almagro L, Tort-Martorell X, Cabrespina F, Zubelzu J (2013) An approach to disaggregating total household water consumption into major end-uses. Water Resour Manag 27:2155–2177CrossRefGoogle Scholar
  13. 13.
    Froehlich J, Larson E, Campbell T, Haggerty C, Fogarty J, Patel SN (2009) HydroSense: infrastructure-mediated single-point sensing of whole-home water activity. In: Proceedings of the 11th international conference on ubiquitous computing. pp 235–244Google Scholar
  14. 14.
    Hart GW (1992) Nonintrusive appliance load monitoring. Proc IEEE 80(12):1870–1891CrossRefGoogle Scholar
  15. 15.
    Huang J, Ling CX (2005) Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng 17:299–310CrossRefGoogle Scholar
  16. 16.
    Ibarz A, Bauer G, Casas R, Marco A, Lukowicz P (2008) Design and evaluation of a sound based water flow measurement system. In: Proceedings of the 3rd European conference on smart sensing and context. Springer, Zurich, pp 41–54Google Scholar
  17. 17.
    James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning. Springer texts in statistics. Springer, New YorkCrossRefzbMATHGoogle Scholar
  18. 18.
    Kim Y, Schmid T, Charbiwala ZM, Friedman J, Srivastava MB (2008) NAWMS: Nonintrusive autonomous water monitoring system. In: Proceedings of the 6th ACM conference on embedded network sensor systems, Raleigh, pp 309–321Google Scholar
  19. 19.
    Kotsiantis SB (2007) Supervised machine learning: a review of classification techniques. Informatica 31(3):249–268MathSciNetzbMATHGoogle Scholar
  20. 20.
    Kozlovskiy I, Schöb S, Sodenkamp M (2016) Non-intrusive disaggregation of water consumption data in a residential household. In: Lecture notes in informatics informatik. pp 1381–1387Google Scholar
  21. 21.
    Schantz C, Sennett B, Donnal J, Gillman M, Leeb S (2014) Non-intrusive load monitoring for water (WaterNILM). In: Mambretti S, Brebbia C (eds) Urban water II. Ashurst, United Kingdom, pp 103–114CrossRefGoogle Scholar
  22. 22.
    Srinivasan V, Stankovic J, Whitehouse K (2011) WaterSense: water flow disaggregation using motion sensors. In: Proceedings of the 3rd ACM workshop on embedded sensing systems for energy-efficiency in buildings. Seattle, pp 19–25Google Scholar
  23. 23.
    Tiefenbeck V, Götte L, Degen K, Tasic V, Fleisch E, Lalive R, Staake T (2016) Overcoming salience bias: how real-time feedback fosters resource conservation. Manag Sci. doi: 10.1287/mnsc.2016.2646
  24. 24.
    US Energy Information Administration (2013) Heating and cooling no longer majority of U.S. home energy use. https://www.eia.gov/todayinenergy/detail.php?id=10271

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Samuel Schöb
    • 1
  • Sebastian A. Günther
    • 1
  • Karl Regensburger
    • 2
  • Thorsten Staake
    • 1
  1. 1.University of BambergBambergGermany
  2. 2.LAGRARPfundsAustria

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