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Heterogeneous Study of Multiple Disturbance Factors Outside Residential Electricity Consumption: A Case Study of Beijing

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  • Yaqing Sheng

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Beijing 102206, China)

  • Jinpeng Liu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Beijing 102206, China)

  • Delin Wei

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Beijing 102206, China)

  • Xiaohua Song

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Beijing 102206, China)

Abstract

Residential electricity consumption is an important part of the electricity consumption of the whole society. The systematic analysis of the influence mechanism of the external complex factors of residential electricity consumption is significant for scientific and effective power demand side optimization management. From the socio-economic and climate perspectives, Spearman’s correlation was used to analyze external multiple disturbance indicators, and principal component analysis (PCA) was used to reduce data dimensionality. The multi-factor residential electricity measurement model (PCA-MCA) was established to explore the heterogeneity of influence mechanisms. Taking Beijing as a case study, the results show that the sensitivity of residential electricity consumption of Beijing to socio-economic indicators is greater than that of climate indicators, and the two influencing factors are obviously heterogeneous. The impact of socio-economic factors on residential electricity consumption appears to have continuous and stable characteristics, but climate factors are more volatile. This paper discusses factors and disturbance mechanisms of regional residential electricity consumption, fully considering the actual situation in Beijing. Taking the realization of regional power demand lateral optimization management as the idea, the paper proposes some optimization strategies to achieve regional power availability. This provides an analysis basis and practical reference for sustainable development of regional power.

Suggested Citation

  • Yaqing Sheng & Jinpeng Liu & Delin Wei & Xiaohua Song, 2021. "Heterogeneous Study of Multiple Disturbance Factors Outside Residential Electricity Consumption: A Case Study of Beijing," Sustainability, MDPI, vol. 13(6), pages 1-22, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:6:p:3335-:d:519311
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    as
    1. Ha-Hyun Jo & Minwoo Jang & Jaehyeok Kim, 2020. "How Population Age Distribution Affects Future Electricity Demand in Korea: Applying Population Polynomial Function," Energies, MDPI, vol. 13(20), pages 1-17, October.
    2. Guefano, Serge & Tamba, Jean Gaston & Azong, Tchitile Emmanuel Wilfried & Monkam, Louis, 2021. "Forecast of electricity consumption in the Cameroonian residential sector by Grey and vector autoregressive models," Energy, Elsevier, vol. 214(C).
    3. 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).
    4. Blázquez, Leticia & Boogen, Nina & Filippini, Massimo, 2013. "Residential electricity demand in Spain: New empirical evidence using aggregate data," Energy Economics, Elsevier, vol. 36(C), pages 648-657.
    5. Amusa, Hammed & Amusa, Kafayat & Mabugu, Ramos, 2009. "Aggregate demand for electricity in South Africa: An analysis using the bounds testing approach to cointegration," Energy Policy, Elsevier, vol. 37(10), pages 4167-4175, October.
    6. Weibin Lin & Bin Chen & Shichao Luo & Li Liang, 2014. "Factor Analysis of Residential Energy Consumption at the Provincial Level in China," Sustainability, MDPI, vol. 6(11), pages 1-15, November.
    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. Zhou, Shaojie & Teng, Fei, 2013. "Estimation of urban residential electricity demand in China using household survey data," Energy Policy, Elsevier, vol. 61(C), pages 394-402.
    9. Mukhopadhyay, Sayanti & Nateghi, Roshanak, 2017. "Climate sensitivity of end-use electricity consumption in the built environment: An application to the state of Florida, United States," Energy, Elsevier, vol. 128(C), pages 688-700.
    10. 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.
    11. Melissa R. Allen & Steven J. Fernandez & Joshua S. Fu & Mohammed M. Olama, 2016. "Impacts of climate change on sub-regional electricity demand and distribution in the southern United States," Nature Energy, Nature, vol. 1(8), pages 1-9, August.
    12. Aydin, Erdal & Brounen, Dirk, 2019. "The impact of policy on residential energy consumption," Energy, Elsevier, vol. 169(C), pages 115-129.
    13. Marwen Elkamel & Lily Schleider & Eduardo L. Pasiliao & Ali Diabat & Qipeng P. Zheng, 2020. "Long-Term Electricity Demand Prediction via Socioeconomic Factors—A Machine Learning Approach with Florida as a Case Study," Energies, MDPI, vol. 13(15), pages 1-21, August.
    14. Nawaz, Saima & Iqbal, Nasir & Anwar, Saba, 2014. "Modelling electricity demand using the STAR (Smooth Transition Auto-Regressive) model in Pakistan," Energy, Elsevier, vol. 78(C), pages 535-542.
    15. Holtedahl, Pernille & Joutz, Frederick L., 2004. "Residential electricity demand in Taiwan," Energy Economics, Elsevier, vol. 26(2), pages 201-224, March.
    16. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    17. Liang, Long & Wu, Wenliang & Lal, Rattan & Guo, Yanbin, 2013. "Structural change and carbon emission of rural household energy consumption in Huantai, northern China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 767-776.
    18. Debora Maia-Silva & Rohini Kumar & Roshanak Nateghi, 2020. "The critical role of humidity in modeling summer electricity demand across the United States," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    19. Kyohei Shibano & Gento Mogi, 2020. "Electricity Consumption Forecast Model Using Household Income: Case Study in Tanzania," Energies, MDPI, vol. 13(10), pages 1-14, May.
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    1. Zhenhua Sun & Lingjun Du & Houyin Long, 2023. "Regional Heterogeneity Analysis of Residential Electricity Consumption in Chinese Cities: Based on Spatial Measurement Models," Energies, MDPI, vol. 16(23), pages 1-22, November.

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