IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v288y2024ics0360544223031365.html
   My bibliography  Save this article

The inequality in household electricity consumption due to temperature change: Data driven analysis with a function-on-function linear model

Author

Listed:
  • Chen, Haitao
  • Zhang, Bin
  • Liu, Hua
  • Cao, Jiguo

Abstract

This paper constructs a function-on-function linear model that identifies the unknown comprehensive response of household electricity consumption toward temperature changes from a data-driven perspective. We also analyze the contribution of dynamic temperature changes to electricity consumption inequality based on large-scale smart meter data. Specifically, we use the Gini index, which characterizes electricity consumption inequality, to explore the heterogeneity of household behaviors. The results show that extreme temperatures will significantly affect household electricity consumption, and the response inertia is approximately 48 days. The response inertia is mainly affected by the household electricity consumption scale. The inertia of large electricity users is four times that of small users. This response inertia difference leads to the occurrence of household electricity consumption inequity inequality in a relatively narrow time window of approximately 18 days. The results also reveal that extreme temperature fluctuations play a key role in enlarging this inequality.

Suggested Citation

  • Chen, Haitao & Zhang, Bin & Liu, Hua & Cao, Jiguo, 2024. "The inequality in household electricity consumption due to temperature change: Data driven analysis with a function-on-function linear model," Energy, Elsevier, vol. 288(C).
  • Handle: RePEc:eee:energy:v:288:y:2024:i:c:s0360544223031365
    DOI: 10.1016/j.energy.2023.129742
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223031365
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2023.129742?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kang, Jieyi & Reiner, David M., 2022. "What is the effect of weather on household electricity consumption? Empirical evidence from Ireland," Energy Economics, Elsevier, vol. 111(C).
    2. Lee, Chien-Chiang & Chiu, Yi-Bin, 2011. "Electricity demand elasticities and temperature: Evidence from panel smooth transition regression with instrumental variable approach," Energy Economics, Elsevier, vol. 33(5), pages 896-902, September.
    3. Harezlak, Jaroslaw & Coull, Brent A. & Laird, Nan M. & Magari, Shannon R. & Christiani, David C., 2007. "Penalized solutions to functional regression problems," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4911-4925, June.
    4. Grottera, Carolina & Barbier, Carine & Sanches-Pereira, Alessandro & Abreu, Mariana Weiss de & Uchôa, Christiane & Tudeschini, Luís Gustavo & Cayla, Jean-Michel & Nadaud, Franck & Pereira Jr, Amaro Ol, 2018. "Linking electricity consumption of home appliances and standard of living: A comparison between Brazilian and French households," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 877-888.
    5. Bessec, Marie & Fouquau, Julien, 2008. "The non-linear link between electricity consumption and temperature in Europe: A threshold panel approach," Energy Economics, Elsevier, vol. 30(5), pages 2705-2721, September.
    6. Marilyn A. Brown & Matt Cox & Ben Staver & Paul Baer, 2016. "Modeling climate-driven changes in U.S. buildings energy demand," Climatic Change, Springer, vol. 134(1), pages 29-44, January.
    7. Zhang, Guoxing & Shen, Lin & Su, Bin, 2023. "Temperature change and daily urban-rural residential electricity consumption in northwestern China: Responsiveness and inequality," Energy Economics, Elsevier, vol. 126(C).
    8. Zheng, Shuguang & Huang, Guohe & Zhou, Xiong & Zhu, Xiaohang, 2020. "Climate-change impacts on electricity demands at a metropolitan scale: A case study of Guangzhou, China," Applied Energy, Elsevier, vol. 261(C).
    9. Arora, Vipin & Lieskovsky, Jozef, 2016. "Electricity Use as an Indicator of U.S. Economic Activity," EconStor Research Reports 126147, ZBW - Leibniz Information Centre for Economics.
    10. Shimei Wu & Xinye Zheng & Chu Wei, 2017. "Measurement of inequality using household energy consumption data in rural China," Nature Energy, Nature, vol. 2(10), pages 795-803, October.
    11. Marilyn Brown & Matt Cox & Ben Staver & Paul Baer, 2016. "Modeling climate-driven changes in U.S. buildings energy demand," Climatic Change, Springer, vol. 134(1), pages 29-44, January.
    12. Jian Huang & Shuange Ma & Huiliang Xie & Cun-Hui Zhang, 2009. "A group bridge approach for variable selection," Biometrika, Biometrika Trust, vol. 96(2), pages 339-355.
    13. repec:dau:papers:123456789/8180 is not listed on IDEAS
    14. Zhang, Bin & Zhang, Yingnan & Li, Jia & Song, Yanwu & Wang, Zhaohua, 2023. "Does the energy efficiency of buildings bring price premiums? Evidence from urban micro-level energy data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 181(C).
    15. Chen, Haitao & Zhang, Bin & Wang, Zhaohua, 2022. "Hidden inequality in household electricity consumption: Measurement and determinants based on large-scale smart meter data," China Economic Review, Elsevier, vol. 71(C).
    16. Wang, Yaoping & Bielicki, Jeffrey M., 2018. "Acclimation and the response of hourly electricity loads to meteorological variables," Energy, Elsevier, vol. 142(C), pages 473-485.
    17. Yating Li & William A. Pizer & Libo Wu, 2019. "Climate change and residential electricity consumption in the Yangtze River Delta, China," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(2), pages 472-477, January.
    18. Yu, Xiumei & Lei, Xiaoyan & Wang, Min, 2019. "Temperature effects on mortality and household adaptation: Evidence from China," Journal of Environmental Economics and Management, Elsevier, vol. 96(C), pages 195-212.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Haitao & Zhang, Bin & Wang, Zhaohua, 2022. "Hidden inequality in household electricity consumption: Measurement and determinants based on large-scale smart meter data," China Economic Review, Elsevier, vol. 71(C).
    2. Jones, Andrew & Nock, Destenie & Samaras, Constantine & Qiu, Yueming (Lucy) & Xing, Bo, 2023. "Climate change impacts on future residential electricity consumption and energy burden: A case study in Phoenix, Arizona," Energy Policy, Elsevier, vol. 183(C).
    3. Lanlan Li & Xinpei Song & Jingjing Li & Ke Li & Jianling Jiao, 2023. "The impacts of temperature on residential electricity consumption in Anhui, China: does the electricity price matter?," Climatic Change, Springer, vol. 176(3), pages 1-26, March.
    4. Palacios-Garcia, E.J. & Moreno-Munoz, A. & Santiago, I. & Flores-Arias, J.M. & Bellido-Outeirino, F.J. & Moreno-Garcia, I.M., 2018. "A stochastic modelling and simulation approach to heating and cooling electricity consumption in the residential sector," Energy, Elsevier, vol. 144(C), pages 1080-1091.
    5. Meixuan Teng & Hua Liao & Paul J. Burke & Tianqi Chen & Chen Zhang, 2022. "Adaptive responses: the effects of temperature levels on residential electricity use in China," Climatic Change, Springer, vol. 172(3), pages 1-20, June.
    6. Wang, Yaoping & Bielicki, Jeffrey M., 2018. "Acclimation and the response of hourly electricity loads to meteorological variables," Energy, Elsevier, vol. 142(C), pages 473-485.
    7. Zhang, Shaohui & Guo, Qinxin & Smyth, Russell & Yao, Yao, 2022. "Extreme temperatures and residential electricity consumption: Evidence from Chinese households," Energy Economics, Elsevier, vol. 107(C).
    8. Li, Xue & Smyth, Russell & Xin, Guangyi & Yao, Yao, 2023. "Warmer temperatures and energy poverty: Evidence from Chinese households," Energy Economics, Elsevier, vol. 120(C).
    9. Yongxia Ding & Wei Qu & Shuwen Niu & Man Liang & Wenli Qiang & Zhenguo Hong, 2016. "Factors Influencing the Spatial Difference in Household Energy Consumption in China," Sustainability, MDPI, vol. 8(12), pages 1-20, December.
    10. Imani, Maryam, 2021. "Electrical load-temperature CNN for residential load forecasting," Energy, Elsevier, vol. 227(C).
    11. Cuihui Xia & Tandong Yao & Weicai Wang & Wentao Hu, 2022. "Effect of Climate on Residential Electricity Consumption: A Data-Driven Approach," Energies, MDPI, vol. 15(9), pages 1-20, May.
    12. David Anthoff & Richard Tol, 2013. "The uncertainty about the social cost of carbon: A decomposition analysis using fund," Climatic Change, Springer, vol. 117(3), pages 515-530, April.
    13. Deng, Nana & Wang, Bo & Wang, Zhaohua, 2023. "Does targeted poverty alleviation improve households’ adaptation to hot weathers: Evidence from electricity consumption of poor households," Energy Policy, Elsevier, vol. 183(C).
    14. Emodi, Nnaemeka Vincent & Chaiechi, Taha & Alam Beg, A.B.M. Rabiul, 2019. "A techno-economic and environmental assessment of long-term energy policies and climate variability impact on the energy system," Energy Policy, Elsevier, vol. 128(C), pages 329-346.
    15. Tamara Sofía Propato & Diego Abelleyra & María Semmartin & Santiago R. Verón, 2021. "Differential sensitivities of electricity consumption to global warming across regions of Argentina," Climatic Change, Springer, vol. 166(1), pages 1-18, May.
    16. Bessec, Marie & Fouquau, Julien, 2018. "Short-run electricity load forecasting with combinations of stationary wavelet transforms," European Journal of Operational Research, Elsevier, vol. 264(1), pages 149-164.
    17. Chao Bi & Minna Jia & Jingjing Zeng, 2019. "Nonlinear Effect of Public Infrastructure on Energy Intensity in China: A Panel Smooth Transition Regression Approach," Sustainability, MDPI, vol. 11(3), pages 1-21, January.
    18. Matthew Ranson & Lauren Morris & Alex Kats-Rubin, 2014. "Climate Change and Space Heating Energy Demand: A Review of the Literature," NCEE Working Paper Series 201407, National Center for Environmental Economics, U.S. Environmental Protection Agency, revised Dec 2014.
    19. Tettey, Uniben Yao Ayikoe & Dodoo, Ambrose & Gustavsson, Leif, 2017. "Energy use implications of different design strategies for multi-storey residential buildings under future climates," Energy, Elsevier, vol. 138(C), pages 846-860.
    20. Zhang, Yue-Jun & Peng, Hua-Rong, 2017. "Exploring the direct rebound effect of residential electricity consumption: An empirical study in China," Applied Energy, Elsevier, vol. 196(C), pages 132-141.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:288:y:2024:i:c:s0360544223031365. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.