SmartCity 360 2016, SmartCity 360 2015: Smart City 360° pp 348-361 | Cite as

User Behavior Modeling for Estimating Residential Energy Consumption

  • Baris Aksanli
  • Alper Sinan Akyurek
  • Tajana Simunic Rosing
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 166)


Residential energy constitutes a significant portion of the total US energy consumption. Several researchers proposed energy-aware solutions for houses, promising significant energy and cost savings. However, it is important to evaluate the outcomes of these methods on larger scale, with hundreds of houses. This paper presents a human-activity based residential energy modeling framework, that can create power demand profiles considering the characteristics of household members. It constructs a mathematical model to show the detailed relationships between human activities and house power consumption. It can be used to create various house profiles with different energy demand characteristics in a reproducible manner. Comparison with real data shows that our model captures the power demand differences between different family types and accurately follows the trends seen in real data. We also show a case study that evaluates voltage deviation in a neighborhood, which requires accurate estimation of the trends in power consumption.


Residential energy Modeling Appliance User activity 



This work was supported in part by TerraSwarm, one of six centers of STARnet, a Semiconductor Research Corporation program sponsored by MARCO and DARPA.


  1. 1.
  2. 2.
    Akyurek, A.S., Aksanli, B., Rosing, T., S2Sim: smart grid swarm simulator. In: International Green and Sustainable Computing Conference (IGSC). IEEE (2015)Google Scholar
  3. 3.
    Barker, S., Mishra, A., Irwin, D., Cecchet, E., Shenoy, P., Albrecht, J.: Smart*: An open data set and tools for enabling research in sustainable homes. In: SustKDD 2012 (2012)Google Scholar
  4. 4.
    Basu, K., Hawarah, L., Arghira, N., Joumaa, H., Ploix, S.: A prediction system for home appliance usage. Energy Build. 67, 668–679 (2013)CrossRefGoogle Scholar
  5. 5.
    Chen, D., Barker, S., Subbaswamy, A., Irwin, D., Shenoy, P.: Non-intrusive occupancy monitoring using smart meters. In: ACM Buildsys (2013)Google Scholar
  6. 6.
    Chiou, Y.: Deriving us household energy consumption profiles from american time use survey data a bootstrap approach. In: 11th International Building Performance Simulation Association Conference and Exhibition (2009)Google Scholar
  7. 7.
    Collin, A.J., Tsagarakis, G., Kiprakis, A.E., McLaughlin, S.: Multi-scale electrical load modelling for demand-side management. In: IEEE PES ISGT Europe (2012)Google Scholar
  8. 8.
    U.S. E.I.A. Residential energy consumption survey (2009)Google Scholar
  9. 9.
  10. 10.
    Center for climate and energy solutions. Energy and technology (2011).
  11. 11.
    Pecan street Inc. Dataport (2015)Google Scholar
  12. 12.
    Johnson, B.J., Starke, M.R., Abdelaziz, O., Jackson, R.K., Tolbert, L.M.: A method for modeling household occupant behavior to simulate residential energy consumption. In: Innovative Smart Grid Technologies Conference, IEEE PES (2014)Google Scholar
  13. 13.
    Kolter, Z., Johnson, M.J.: REDD: a public data set for energy disaggregation research. In: Workshop on Data Mining Applications in Sustainability (2011)Google Scholar
  14. 14.
    López-Rodríguez, M.A., Santiago, I., Trillo-Montero, D., Torriti, J., Moreno-Munoz, A.: Analysis, modeling of active occupancy of the residential sector in spain: an indicator of residential electricity consumption. Energy Policy 62, 742–751 (2013)CrossRefGoogle Scholar
  15. 15.
    Muratori, M., Roberts, M., Sioshansi, R., Marano, V., Rizzoni, G.: A highly resolved modeling technique to simulate residential power demand. Appl. Energy 107, 465–473 (2013)CrossRefGoogle Scholar
  16. 16.
    Neill, D.O., Levorato, M., Goldsmith, A., Mitra, U.: Residential demand response using reinforcement learning. In: IEEE SmartGridComm (2010)Google Scholar
  17. 17.
    Bureau of Labor Statistics. American time use survey (2014)Google Scholar
  18. 18.
    Venkatesh, J., Aksanli, B., Junqua, J., Morin, P., Rosing, T.: Homesim: comprehensive, smart, residential electrical energy simulation and scheduling. In: International Green Computing Conference (IGCC). IEEE (2013)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  • Baris Aksanli
    • 1
  • Alper Sinan Akyurek
    • 1
  • Tajana Simunic Rosing
    • 1
  1. 1.University of California San DiegoLa JollaUSA

Personalised recommendations