Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
EPRI test circuits. http://svn.code.sf.net/p/electricdss/code/trunk/Distrib/EPRITestCircuits/Readme.pdf
Akyurek, A.S., Aksanli, B., Rosing, T., S2Sim: smart grid swarm simulator. In: International Green and Sustainable Computing Conference (IGSC). IEEE (2015)
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)
Basu, K., Hawarah, L., Arghira, N., Joumaa, H., Ploix, S.: A prediction system for home appliance usage. Energy Build. 67, 668–679 (2013)
Chen, D., Barker, S., Subbaswamy, A., Irwin, D., Shenoy, P.: Non-intrusive occupancy monitoring using smart meters. In: ACM Buildsys (2013)
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)
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)
U.S. E.I.A. Residential energy consumption survey (2009)
General electric. http://visualization.geblogs.com/visualization/appliances/
Center for climate and energy solutions. Energy and technology (2011). http://www.c2es.org/category/topic/energy-technology
Pecan street Inc. Dataport (2015)
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)
Kolter, Z., Johnson, M.J.: REDD: a public data set for energy disaggregation research. In: Workshop on Data Mining Applications in Sustainability (2011)
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)
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)
Neill, D.O., Levorato, M., Goldsmith, A., Mitra, U.: Residential demand response using reinforcement learning. In: IEEE SmartGridComm (2010)
Bureau of Labor Statistics. American time use survey (2014)
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)
Acknowledgment
This work was supported in part by TerraSwarm, one of six centers of STARnet, a Semiconductor Research Corporation program sponsored by MARCO and DARPA.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Aksanli, B., Akyurek, A.S., Rosing, T.S. (2016). User Behavior Modeling for Estimating Residential Energy Consumption. In: Leon-Garcia, A., et al. Smart City 360°. SmartCity 360 SmartCity 360 2016 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 166. Springer, Cham. https://doi.org/10.1007/978-3-319-33681-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-33681-7_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-33680-0
Online ISBN: 978-3-319-33681-7
eBook Packages: Computer ScienceComputer Science (R0)