Research Papers: Energy Systems Analysis

Demographical Energy Usage Analysis of Residential Buildings

[+] Author and Article Information
Alice Sokolova

Electrical and Computer Engineering Department,
San Diego State University,
San Diego, CA 92182
e-mail: asokolova@sdsu.edu

Baris Aksanli

Electrical and Computer Engineering Department,
San Diego State University,
San Diego, CA 92182
e-mail: baksanli@sdsu.edu

Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received August 27, 2018; final manuscript received December 28, 2018; published online January 18, 2019. Assoc. Editor: Reza Baghaei Lakeh.

J. Energy Resour. Technol 141(6), 062003 (Jan 18, 2019) (6 pages) Paper No: JERT-18-1665; doi: 10.1115/1.4042451 History: Received August 27, 2018; Revised December 28, 2018

Residential energy consumption constitutes a significant portion of the overall energy consumption. There are significant amount of studies that target to reduce this consumption, and these studies mainly create mathematical models to represent and regenerate the energy consumption of individual houses. Most of these models assume that the residential energy consumption can be classified and then predicted based on the household size. As a result, most of the previous studies suggest that the household size can be treated as an independent variable which can be used to predict energy consumption. In this work, we test this hypothesis on a large residential energy consumption dataset that also includes demographic information. Our results show that other variables such as income, geographic location, house type, and personal preferences strongly impact energy consumption and decrease the importance of the household size because the household size can explain only 26.55% of the electricity consumption variation across the houses.

Copyright © 2019 by ASME
Your Session has timed out. Please sign back in to continue.


U.S. EIA, 2017, “ How Much Energy is Consumed in US Residential and Commercial Buildings?” United States Energy Information Administration, Washington, DC, accessed Dec. 11, 2018, https://www.eia.gov/tools/faqs/faq.php?id=86&t=1
Bureau of Labor Statistics, 2014, “ American Time Use Survey,” Bureau of Labor Statistics, Washington, DC, accessed Dec. 11, 2018, https://www.bls.gov/tus/
Basu, K. , Hawarah, L. , Arghira, N. , Joumaa, H. , and Ploix, S. , 2013, “ A Prediction System for Home Appliance Usage,” Energy Build., 67, pp. 668–679. [CrossRef]
Collin, A. , Tsagarakis, G. , Kiprakis, A. , and McLaughlin, S. , 2012, “ Multi-Scale Electrical Load Modelling for Demand-Side Management,” IEEE PES Innovative Smart Grid Technologies Europe, Berlin, Germany, Oct. 4–17, pp. 1–8.
López-Rodríguez, M. , Santiago, I. , Trillo-Montero, D. , Torriti, J. , and Moreno-Munoz, A. , 2013, “ Analysis and Modeling of Active Occupancy of the Residential Sector in Spain: An Indicator of Residential Electricity Consumption,” Energy Policy, 62, pp. 742–751. [CrossRef]
U.S. EIA, 2015, “Residential Energy Consumption Survey,” United States Energy Information Administration, Washington, DC, accessed Dec. 11, 2018, https://www.eia.gov/consumption/residential
Kolter, J. Z. , and Johnson, M. J. , 2011, “ Redd: A Public Data Set for Energy Disaggregation Research,” Workshop on Data Mining Applications in Sustainability (SIGKDD), San Diego, CA, pp. 59–62.
Barker, S. , Mishra, A. , Irwin, D. , Cecchet, E. , Shenoy, P. , and Albrecht, J. , 2012, “ Smart*: An Open Data Set and Tools for Enabling Research in Sustainable Homes,” SustKDD, 111(112), p. 108. http://wan.poly.edu/KDD2012/forms/workshop/SustKDD12/doc/SustKDD12_3.pdf
Wei, N. , Li, C. , Li, C. , Xie, H. , Du, Z. , Zhang, Q. , and Zeng, F. , 2019, “ Short-Term Forecasting of Natural Gas Consumption Using Factor Selection Algorithm and Optimized Support Vector Regression,” ASME J. Energy Resour. Technol., 141(3), p. 032701. [CrossRef]
Gunes, M. B. , and Ellis, M. W. , 2003, “ Evaluation of Energy, Environmental, and Economic Characteristics of Fuel Cell Combined Heat and Power Systems for Residential Applications,” ASME J. Energy Resour. Technol., 125(3), pp. 208–220. [CrossRef]
Facci, A. L. , Andreassi, L. , Martini, F. , and Ubertini, S. , 2014, “ Comparing Energy and Cost Optimization in Distributed Energy Systems Management,” ASME J. Energy Resour. Technol., 136(3), p. 032001. [CrossRef]
Elnakat, A. , Gomez, J. D. , and Booth, N. , 2016, “ A Zip Code Study of Socioeconomic, Demographic, and Household Gendered Influence on the Residential Energy Sector,” Energy Rep., 2, pp. 21–27. [CrossRef]
Hache, E. , Leboullenger, D. , and Mignon, V. , 2017, “ Beyond Average Energy Consumption in the French Residential Housing Market: A Household Classification Approach,” Energy Policy, 107, pp. 82–95. [CrossRef]
Zhang, M. , Song, Y. , Li, P. , and Li, H. , 2016, “ Study on Affecting Factors of Residential Energy Consumption in Urban and Rural Jiangsu,” Renewable Sustainable Energy Rev., 53, pp. 330–337. [CrossRef]
Lévy, J.-P. , and Belaïd, F. , 2017, “ The Determinants of Domestic Energy Consumption in France: Energy Modes, Habitat, Households and Life Cycles,” Renewable Sustainable Energy Rev., 81, pp. 2104–2114. [CrossRef]
Huebner, G. , Shipworth, D. , Hamilton, I. , Chalabi, Z. , and Oreszczyn, T. , 2016, “ Understanding Electricity Consumption: A Comparative Contribution of Building Factors, Socio-Demographics, Appliances, Behaviours And Attitudes,” Appl. Energy, 177, pp. 692–702. [CrossRef]
Marcus, W. B. , and Ruszovan, G. , 2007, “ Know Your Customers: A Review of Load Research Data and Economic, Demographic, and Appliance Saturation Characteristics of California Utility Residential Customers,” JBS Energy Inc., Broderick, CA, prepared on behalf of Utility Reform Network California Public Utilities Commission App, No. 06-03-005.
Deka, A. , Hamta, N. , Esmaeilian, B. , and Behdad, S. , 2016, “ Predictive Modeling Techniques to Forecast Energy Demand in the United States: A Focus on Economic and Demographic Factors,” ASME J. Energy Resour. Technol., 138(2), p. 022001. [CrossRef]
Pecan Street Incorporation, 2015, “ Dataport,” Pecan Street Inc., Austin, TX, accessed Dec. 11, 2018, http://www.pecanstreet.org/category/dataport/
OVO Energy, 2014, “ What's the Average Gas Bill and Average Electricity Bill in the UK?,” CleanTechnica.com, accessed Dec. 11, 2018, https://cleantechnica.com/2013/03/08/us-electricity-consumption-much-more-evenly-distributed-than-income-wealth/
Hartigan, J. A. , and Wong, M. A. , 1979, “ Algorithm as 136: A k-Means Clustering Algorithm,” J. R. Stat. Soc. Ser. C, 28(1), pp. 100–108.
Bartiaux, F. , and Gram-Hanssen, K. , 2005, “ Socio-Political Factors Influencing Household Electricity Consumption: A Comparison Between Denmark and Belgium,” ECEEE Summer Study Proceedings, Vol. 3, Mandelieu la Napoule, France, pp. 1313–1325.
Nielsen, L. , 1993, “ How to Get the Birds in the Bush Into Your Hand: Results From a Danish Research Project on Electricity Savings,” Energy Policy, 21(11), pp. 1133–1144. [CrossRef]
Genjo, K. , Tanabe, S.-I. , Matsumoto, S.-I. , Hasegawa, K.-I. , and Yoshino, H. , 2005, “ Relationship Between Possession of Electric Appliances and Electricity for Lighting and Others in Japanese Households,” Energy Build., 37(3), pp. 259–272. [CrossRef]
U.S. EIA, 2018, “ Annual Energy Outlook 2018–Table 4: Residential Sector Key Indicators and Consumption,” United States Energy Information Administration, Washington, DC, accessed Dec. 11, 2018, https://www.eia.gov/tools/faqs/faq.php?id=96&t=3
U.S. EIA, 2012, “ Annual Energy Review—Household End Uses: Fuel Types, Appliances, and Electronics, Selected Years, 1978–2009,” United States Energy Information Administration, Washington, DC.


Grahic Jump Location
Fig. 1

Southern California Edison average monthly summer month use by income and household size. Images are created using numbers from Ref. [17]: (a) hot climate, (b) mild climate, (c) cool climate, and (d) all.

Grahic Jump Location
Fig. 2

Variation of electricity usage across homes of the same size. Image recreated from Ref. [20].

Grahic Jump Location
Fig. 3

Average house size versus income level. The figure is created using numbers from Residential Energy Consumption Survey [6].

Grahic Jump Location
Fig. 4

Clustering results based on only mean and standard deviation of monthly energy consumption

Grahic Jump Location
Fig. 5

Clustering results based on only household size, and energy consumption is monthly

Grahic Jump Location
Fig. 6

Clustering results based on daily average energy consumption for different family sizes

Grahic Jump Location
Fig. 7

Clustering results with 20-dimensional data, and energy consumption is monthly

Grahic Jump Location
Fig. 8

Clustering results with 40-dimensional data, and energy consumption is hourly

Grahic Jump Location
Fig. 9

The U.S. residential sector electricity consumption by major end uses in 2017. Image created using the table in Ref.[25].



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In