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Predicting household energy consumption in an aging society

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  • 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
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    References listed on IDEAS

    as
    1. Shi, Xinjie, 2019. "Inequality of opportunity in energy consumption in China," Energy Policy, Elsevier, vol. 124(C), pages 371-382.
    2. 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.
    3. Ryu, Jun-Yeol & Kim, Dae-Wook & Kim, Man-Keun, 2021. "Household differentiation and residential electricity demand in Korea," Energy Economics, Elsevier, vol. 95(C).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Su, Yu-Wen, 2019. "Residential electricity demand in Taiwan: Consumption behavior and rebound effect," Energy Policy, Elsevier, vol. 124(C), pages 36-45.
    10. 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.
    11. 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.
    12. Damari, Yuval & Kissinger, Meidad, 2018. "An integrated analysis of households' electricity consumption in Israel," Energy Policy, Elsevier, vol. 119(C), pages 51-58.
    13. 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.
    14. 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).
    15. 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.
    16. 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).
    17. Trotta, Gianluca, 2020. "An empirical analysis of domestic electricity load profiles: Who consumes how much and when?," Applied Energy, Elsevier, vol. 275(C).
    18. 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).
    19. 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).
    20. 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.
    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).
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