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A High‐Resolution Statistical Model of Residential Energy End Use Characteristics for the United States

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  • Jihoon Min
  • Zeke Hausfather
  • Qi Feng Lin

Abstract

The absence of detailed information on residential energy end use characteristics for the United States has in the past presented an impediment to the effective development and targeting of residential energy efficiency programs. This article presents a framework for modeling space heating, cooling, water heating, and appliance energy end uses, fuels used, and carbon emissions at a zip code–level resolution for the entire United States. It combines a regression‐based statistical model derived from Residential Energy Consumption Survey data with U.S. census 2000 five‐digit zip code level information, climate division–level temperature data, and other sources. The results show large variations in energy use characteristics both between and within different regions of the country, with particularly notable differences in the magnitude of and distribution by fuel of residential energy use in urban and rural areas. The results are validated against residential energy sales data and have useful implications for both residential energy efficiency planning and further study of variations in use patterns.

Suggested Citation

  • Jihoon Min & Zeke Hausfather & Qi Feng Lin, 2010. "A High‐Resolution Statistical Model of Residential Energy End Use Characteristics for the United States," Journal of Industrial Ecology, Yale University, vol. 14(5), pages 791-807, October.
    • Handle: RePEc:bla:inecol:v:14:y:2010:i:5:p:791-807
    DOI: 10.1111/j.1530-9290.2010.00279.x
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    Cited by:

    1. Belaïd, Fateh, 2016. "Understanding the spectrum of domestic energy consumption: Empirical evidence from France," Energy Policy, Elsevier, vol. 92(C), pages 220-233.
    2. Estiri, Hossein, 2014. "Energy Planning in the Big Data Era: A Theme Study of the Residential Sector," EconStor Conference Papers 106936, ZBW - Leibniz Information Centre for Economics.
    3. Kelly, J. Andrew & Clinch, J. Peter & Kelleher, L. & Shahab, S., 2020. "Enabling a just transition: A composite indicator for assessing home-heating energy-poverty risk and the impact of environmental policy measures," Energy Policy, Elsevier, vol. 146(C).
    4. Soo-Jin Lee & You-Jeong Kim & Hye-Sun Jin & Sung-Im Kim & Soo-Yeon Ha & Seung-Yeong Song, 2019. "Residential End-Use Energy Estimation Models in Korean Apartment Units through Multiple Regression Analysis," Energies, MDPI, vol. 12(12), pages 1-18, June.
    5. Seyed Azad Nabavi & Alireza Aslani & Martha A. Zaidan & Majid Zandi & Sahar Mohammadi & Naser Hossein Motlagh, 2020. "Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors," Energies, MDPI, vol. 13(19), pages 1-22, October.
    6. Baik, Sosung & Hines, Jeffrey F. & Sim, Jaeung, 2023. "Racial disparities in the energy burden beyond socio-economic inequality," Energy Economics, Elsevier, vol. 127(PA).
    7. Kialashaki, Arash & Reisel, John R., 2013. "Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks," Applied Energy, Elsevier, vol. 108(C), pages 271-280.
    8. Quan, Steven Jige & Li, Chaosu, 2021. "Urban form and building energy use: A systematic review of measures, mechanisms, and methodologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    9. Wang, Siyan & Sun, Xun & Lall, Upmanu, 2017. "A hierarchical Bayesian regression model for predicting summer residential electricity demand across the U.S.A," Energy, Elsevier, vol. 140(P1), pages 601-611.
    10. Yen-Jong Chen & Rodney H Matsuoka & Tzu-Min Liang, 2018. "Urban form, building characteristics, and residential electricity consumption: A case study in Tainan City," Environment and Planning B, , vol. 45(5), pages 933-952, September.
    11. Streltsov, Artem & Malof, Jordan M. & Huang, Bohao & Bradbury, Kyle, 2020. "Estimating residential building energy consumption using overhead imagery," Applied Energy, Elsevier, vol. 280(C).
    12. Wang, Jianming & Li, Yongqiang & He, Zhengxia & Gao, Jian & Wang, Jianguo, 2022. "Scale framing, benefit framing and their interaction effects on energy-saving behaviors: Evidence from urban residents of China," Energy Policy, Elsevier, vol. 166(C).
    13. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    14. Buylova, Alexandra, 2020. "Spotlight on energy efficiency in Oregon: Investigating dynamics between energy use and socio-demographic characteristics in spatial modeling of residential energy consumption," Energy Policy, Elsevier, vol. 140(C).
    15. Selima Sultana & Nastaran Pourebrahim & Hyojin Kim, 2018. "Household Energy Expenditures in North Carolina: A Geographically Weighted Regression Approach," Sustainability, MDPI, vol. 10(5), pages 1-22, May.
    16. Raissi, Shiva & Reames, Tony G., 2020. "“If we had a little more flexibility.” perceptions of programmatic challenges and opportunities implementing government-funded low-income energy efficiency programs," Energy Policy, Elsevier, vol. 147(C).
    17. Anderson, John E. & Wulfhorst, Gebhard & Lang, Werner, 2015. "Energy analysis of the built environment—A review and outlook," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 149-158.
    18. Raihanian Mashhadi, Ardeshir & Behdad, Sara, 2018. "Discriminant effects of consumer electronics use-phase attributes on household energy prediction," Energy Policy, Elsevier, vol. 118(C), pages 346-355.
    19. Lu Jiang & Bowenpeng Ding & Xiaonan Shi & Chunhua Li & Yamei Chen, 2022. "Household Energy Consumption Patterns and Carbon Emissions for the Megacities—Evidence from Guangzhou, China," Energies, MDPI, vol. 15(8), pages 1-21, April.
    20. Reames, Tony Gerard, 2016. "Targeting energy justice: Exploring spatial, racial/ethnic and socioeconomic disparities in urban residential heating energy efficiency," Energy Policy, Elsevier, vol. 97(C), pages 549-558.

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