IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v352y2023ics0306261923012631.html
   My bibliography  Save this article

Predicting household energy consumption in an aging society

Author

Listed:
  • Shi, Zhengyu
  • Wu, Libo
  • Zhou, Yang

Abstract

Social aging will significantly affect energy consumption in the residential sector, with a critical influence on the demand side of the energy sector, especially for megacities. Despite previous studies on the total change in energy demand due to aging, limited research has focused on high-frequency effects. Using a machine learning model, this study examines how social aging will affect household hourly consumption patterns, covering both the change in total consumption and the hourly distribution. We first unsupervised cluster household high-frequency consumption patterns in Shanghai from 2016 to 2018 into 12 groups, and then trained a finite mixture model to analyze the correlation between clusters and household features. We further used the well-tuned model based on the out-of-sample result to simulate the consumption patterns under scenarios of social aging and income growth. The simulation results demonstrate that in addition to increasing overall energy consumption, an aging society will also change the hourly consumption pattern, leading to a larger gap between peak and non-peak periods.

Suggested Citation

  • Shi, Zhengyu & Wu, Libo & Zhou, Yang, 2023. "Predicting household energy consumption in an aging society," Applied Energy, Elsevier, vol. 352(C).
    • Handle: RePEc:eee:appene:v:352:y:2023:i:c:s0306261923012631
    DOI: 10.1016/j.apenergy.2023.121899
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923012631
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121899?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. Ota, Toru & Kakinaka, Makoto & Kotani, Koji, 2018. "Demographic effects on residential electricity and city gas consumption in the aging society of Japan," Energy Policy, Elsevier, vol. 115(C), pages 503-513.
    2. Damari, Yuval & Kissinger, Meidad, 2018. "An integrated analysis of households' electricity consumption in Israel," Energy Policy, Elsevier, vol. 119(C), pages 51-58.
    3. Yilmaz, S. & Chambers, J. & Patel, M.K., 2019. "Comparison of clustering approaches for domestic electricity load profile characterisation - Implications for demand side management," Energy, Elsevier, vol. 180(C), pages 665-677.
    4. Shi, Xinjie, 2019. "Inequality of opportunity in energy consumption in China," Energy Policy, Elsevier, vol. 124(C), pages 371-382.
    5. Yang Zhou & Lirong Xue & Zhengyu Shi & Libo Wu & Jianqing Fan, 2022. "Measuring Housing Vitality from Multi-Source Big Data and Machine Learning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(539), pages 1045-1059, September.
    6. Ryu, Jun-Yeol & Kim, Dae-Wook & Kim, Man-Keun, 2021. "Household differentiation and residential electricity demand in Korea," Energy Economics, Elsevier, vol. 95(C).
    7. Wu, Wenchao & Kanamori, Yuko & Zhang, Runsen & Zhou, Qian & Takahashi, Kiyoshi & Masui, Toshihiko, 2021. "Implications of declining household economies of scale on electricity consumption and sustainability in China," Ecological Economics, Elsevier, vol. 184(C).
    8. Zhou, Yang & Shi, Zhixiong & Shi, Zhengyu & Gao, Qing & Wu, Libo, 2019. "Disaggregating power consumption of commercial buildings based on the finite mixture model," Applied Energy, Elsevier, vol. 243(C), pages 35-46.
    9. Satre-Meloy, Aven & Diakonova, Marina & Grünewald, Philipp, 2020. "Cluster analysis and prediction of residential peak demand profiles using occupant activity data," Applied Energy, Elsevier, vol. 260(C).
    10. Guo, Zhifeng & Zhou, Kaile & Zhang, Chi & Lu, Xinhui & Chen, Wen & Yang, Shanlin, 2018. "Residential electricity consumption behavior: Influencing factors, related theories and intervention strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 399-412.
    11. Trotta, Gianluca, 2020. "An empirical analysis of domestic electricity load profiles: Who consumes how much and when?," Applied Energy, Elsevier, vol. 275(C).
    12. Satre-Meloy, Aven, 2019. "Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models," Energy, Elsevier, vol. 174(C), pages 148-168.
    13. Park, Jongmun & Yun, Sun-Jin, 2022. "Social determinants of residential electricity consumption in Korea: Findings from a spatial panel model," Energy, Elsevier, vol. 239(PE).
    14. Srivastava, Aman & Van Passel, Steven & Kessels, Roselinde & Valkering, Pieter & Laes, Erik, 2020. "Reducing winter peaks in electricity consumption: A choice experiment to structure demand response programs," Energy Policy, Elsevier, vol. 137(C).
    15. Jaehyeok Kim & Minwoo Jang & Donghyun Shin, 2019. "Examining the Role of Population Age Structure upon Residential Electricity Demand: A Case from Korea," Sustainability, MDPI, vol. 11(14), pages 1-19, July.
    16. 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.
    17. Valeriya Azarova & Dominik Engel & Cornelia Ferner & Andrea Kollmann & Johannes Reichl, 2018. "Exploring the impact of network tariffs on household electricity expenditures using load profiles and socio-economic characteristics," Nature Energy, Nature, vol. 3(4), pages 317-325, April.
    18. Wang, Shanyong & Lin, Shoufu & Li, Jun, 2018. "Exploring the effects of non-cognitive and emotional factors on household electricity saving behavior," Energy Policy, Elsevier, vol. 115(C), pages 171-180.
    19. Su, Yu-Wen, 2019. "Residential electricity demand in Taiwan: Consumption behavior and rebound effect," Energy Policy, Elsevier, vol. 124(C), pages 36-45.
    20. Biying Yu & Yi-Ming Wei & Kei Gomi & Yuzuru Matsuoka, 2018. "Future scenarios for energy consumption and carbon emissions due to demographic transitions in Chinese households," Nature Energy, Nature, vol. 3(2), pages 109-118, February.
    21. 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).
    22. Miller, Wendy & Vine, Desley & Amin, Zakaria, 2017. "Energy efficiency of housing for older citizens: Does it matter?," Energy Policy, Elsevier, vol. 101(C), pages 216-224.
    23. Balezentis, Tomas, 2020. "Shrinking ageing population and other drivers of energy consumption and CO2 emission in the residential sector: A case from Eastern Europe," Energy Policy, Elsevier, vol. 140(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Giacomo Falchetta & Enrica Cian & Ian Sue Wing & Deborah Carr, 2024. "Global projections of heat exposure of older adults," Nature Communications, Nature, vol. 15(1), pages 1-13, December.

    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. Yabin Da & Bin Zeng & Jing-Li Fan & Jiawei Hu & Lanlan Li, 2023. "Heterogeneous responses to climate: evidence from residential electricity consumption," Climatic Change, Springer, vol. 176(8), pages 1-19, August.
    2. Wen, Hanguan & Liu, Xiufeng & Yang, Ming & Lei, Bo & Xu, Cheng & Chen, Zhe, 2024. "A novel approach for identifying customer groups for personalized demand-side management services using household socio-demographic data," Energy, Elsevier, vol. 286(C).
    3. Lina Liu & Jiansheng Qu & Tek Narayan Maraseni & Yibo Niu & Jingjing Zeng & Lihua Zhang & Li Xu, 2020. "Household CO 2 Emissions: Current Status and Future Perspectives," IJERPH, MDPI, vol. 17(19), pages 1-19, September.
    4. Zhang, Yimeng & Wang, Feng & Zhang, Bing, 2023. "The impacts of household structure transitions on household carbon emissions in China," Ecological Economics, Elsevier, vol. 206(C).
    5. Yarbaşı, İkram Yusuf & Çelik, Ali Kemal, 2023. "The determinants of household electricity demand in Turkey: An implementation of the Heckman Sample Selection model," Energy, Elsevier, vol. 283(C).
    6. Jieyi Kang & David Reiner, 2021. "Identifying residential consumption patterns using data-mining techniques: A large-scale study of smart meter data in Chengdu, China," Working Papers EPRG2114, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    7. Ki, Jaehong & Yoon, D.K., 2024. "The impact of urban form on residential electricity consumption: Panel data analyses of South Korean urban municipalities," Energy Policy, Elsevier, vol. 186(C).
    8. Ryu, Jun-Yeol & Kim, Dae-Wook & Kim, Man-Keun, 2021. "Household differentiation and residential electricity demand in Korea," Energy Economics, Elsevier, vol. 95(C).
    9. Li, Yi & Liu, Tianya & Xu, Jinpeng, 2023. "Analyzing the economic, social, and technological determinants of renewable and nonrenewable electricity production in China: Findings from time series models," Energy, Elsevier, vol. 282(C).
    10. 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).
    11. Gianluca Trotta & Kirsten Gram-Hanssen & Pernille Lykke Jørgensen, 2020. "Heterogeneity of Electricity Consumption Patterns in Vulnerable Households," Energies, MDPI, vol. 13(18), pages 1-17, September.
    12. Yamaguchi, Yohei & Shoda, Yuto & Yoshizawa, Shinya & Imai, Tatsuya & Perwez, Usama & Shimoda, Yoshiyuki & Hayashi, Yasuhiro, 2023. "Feasibility assessment of net zero-energy transformation of building stock using integrated synthetic population, building stock, and power distribution network framework," Applied Energy, Elsevier, vol. 333(C).
    13. Pullinger, Martin & Zapata-Webborn, Ellen & Kilgour, Jonathan & Elam, Simon & Few, Jessica & Goddard, Nigel & Hanmer, Clare & McKenna, Eoghan & Oreszczyn, Tadj & Webb, Lynda, 2024. "Capturing variation in daily energy demand profiles over time with cluster analysis in British homes (September 2019 – August 2022)," Applied Energy, Elsevier, vol. 360(C).
    14. Li, Xue & Smyth, Russell & Xin, Guangyi & Yao, Yao, 2023. "Warmer temperatures and energy poverty: Evidence from Chinese households," Energy Economics, Elsevier, vol. 120(C).
    15. Jiang, Lei & Yang, Yue & Wu, Qingyang & Yang, Linshuang & Yang, Zaoli, 2024. "Hotter days, dirtier air: The impact of extreme heat on energy and pollution intensity in China," Energy Economics, Elsevier, vol. 130(C).
    16. 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.
    17. Lesley Thomson & David Jenkins, 2023. "The Use of Real Energy Consumption Data in Characterising Residential Energy Demand with an Inventory of UK Datasets," Energies, MDPI, vol. 16(16), pages 1-29, August.
    18. Li, Zhen & Niu, Shuwen & Halleck Vega, Sol Maria & Wang, Jinnian & Wang, Dakang & Yang, Xiankun, 2024. "Electrification and residential well-being in China," Energy, Elsevier, vol. 294(C).
    19. Hoai Son Nguyen & Minh Ha-Duong & Xuân Tháng Nguyen, 2023. "Economy of Scale in Electricity Consumption of Households in Vietnam [Tính kinh tế theo quy mô trong tiêu dùng điện của các hộ gia đình tại Việt Nam]," Post-Print hal-04332519, HAL.
    20. 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).

    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:appene:v:352:y:2023:i:c:s0306261923012631. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.