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Exploring household natural gas consumption patterns and their influencing factors: An integrated clustering and econometric method

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  • Li, Lanlan
  • Ming, Huayang
  • Fu, Weizhong
  • Shi, Quan
  • Yu, Shiwei

Abstract

Understanding household natural gas consumption patterns and their influencing factors can help implement specific energy consumption policies and improve energy efficiency. Based on a dataset of natural gas consumption bills for 3995 households in Hefei city, China, this study identifies different household gas consumption patterns using intelligent cluster analysis, taking into account the increasing block tariffs (IBTs) and temperature factors. Subsequently, the survey data of 348 households are matched with the billing and weather data to explore the key drivers of natural gas consumption patterns in different households. The results show that (1) household gas consumption patterns can be divided into four types: a single-point spike, double-point flat-peak, micro-peak, and linear. Among them, single-point spike type consumers and double-point flat-peak type consumers belong to the second and third levels of consumers with high gas consumption, and consumers using wall-hung boilers account for a high proportion of households. (2) For the whole sample, both the IBT policy and the temperature have a significant negative impact on household gas consumption. In terms of different types of consumers, micro-peak and linear consumers are more sensitive to the IBT policy, whereas temperature has the greatest impact on the gas consumption of single-point peak consumers.

Suggested Citation

  • Li, Lanlan & Ming, Huayang & Fu, Weizhong & Shi, Quan & Yu, Shiwei, 2021. "Exploring household natural gas consumption patterns and their influencing factors: An integrated clustering and econometric method," Energy, Elsevier, vol. 224(C).
  • Handle: RePEc:eee:energy:v:224:y:2021:i:c:s0360544221004436
    DOI: 10.1016/j.energy.2021.120194
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    as
    1. Fu, Xin & Zeng, Xiao-Jun & Feng, Pengpeng & Cai, Xiuwen, 2018. "Clustering-based short-term load forecasting for residential electricity under the increasing-block pricing tariffs in China," Energy, Elsevier, vol. 165(PB), pages 76-89.
    2. Viegas, Joaquim L. & Vieira, Susana M. & Melício, R. & Mendes, V.M.F. & Sousa, João M.C., 2016. "Classification of new electricity customers based on surveys and smart metering data," Energy, Elsevier, vol. 107(C), pages 804-817.
    3. Hara, Keishiro & Uwasu, Michinori & Kishita, Yusuke & Takeda, Hiroyuki, 2015. "Determinant factors of residential consumption and perception of energy conservation: Time-series analysis by large-scale questionnaire in Suita, Japan," Energy Policy, Elsevier, vol. 87(C), pages 240-249.
    4. Maximilian Auffhammer & Anin Aroonruengsawat, 2011. "Simulating the impacts of climate change, prices and population on California’s residential electricity consumption," Climatic Change, Springer, vol. 109(1), pages 191-210, December.
    5. Zhang, Zibin & Cai, Wenxin & Feng, Xiangzhao, 2017. "How do urban households in China respond to increasing block pricing in electricity? Evidence from a fuzzy regression discontinuity approach," Energy Policy, Elsevier, vol. 105(C), pages 161-172.
    6. Staffell, Iain & Pfenninger, Stefan, 2018. "The increasing impact of weather on electricity supply and demand," Energy, Elsevier, vol. 145(C), pages 65-78.
    7. Baldini, Mattia & Trivella, Alessio & Wente, Jordan William, 2018. "The impact of socioeconomic and behavioural factors for purchasing energy efficient household appliances: A case study for Denmark," Energy Policy, Elsevier, vol. 120(C), pages 503-513.
    8. Gautam, Tej K. & Paudel, Krishna P., 2018. "The demand for natural gas in the Northeastern United States," Energy, Elsevier, vol. 158(C), pages 890-898.
    9. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    10. Pothitou, Mary & Hanna, Richard F. & Chalvatzis, Konstantinos J., 2016. "Environmental knowledge, pro-environmental behaviour and energy savings in households: An empirical study," Applied Energy, Elsevier, vol. 184(C), pages 1217-1229.
    11. 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.
    12. Li, Lanlan & Gong, Chengzhu & Tian, Shizhong & Jiao, Jianling, 2016. "The peak-shaving efficiency analysis of natural gas time-of-use pricing for residential consumers: Evidence from multi-agent simulation," Energy, Elsevier, vol. 96(C), pages 48-58.
    13. Gong, Chengzhu & Yu, Shiwei & Zhu, Kejun & Hailu, Atakelty, 2016. "Evaluating the influence of increasing block tariffs in residential gas sector using agent-based computational economics," Energy Policy, Elsevier, vol. 92(C), pages 334-347.
    14. Hancevic, Pedro Ignacio & Lopez-Aguilar, Javier Alejandro, 2019. "Energy efficiency programs in the context of increasing block tariffs: The case of residential electricity in Mexico," Energy Policy, Elsevier, vol. 131(C), pages 320-331.
    15. 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.
    16. Liu, Guixian & Dong, Xiucheng & Jiang, Qingzhe & Dong, Cong & Li, Jiaman, 2018. "Natural gas consumption of urban households in China and corresponding influencing factors," Energy Policy, Elsevier, vol. 122(C), pages 17-26.
    17. Lin, Boqiang & Chen, Xing, 2018. "Is the implementation of the Increasing Block Electricity Prices policy really effective?--- Evidence based on the analysis of synthetic control method," Energy, Elsevier, vol. 163(C), pages 734-750.
    18. Motlagh, Omid & Berry, Adam & O'Neil, Lachlan, 2019. "Clustering of residential electricity customers using load time series," Applied Energy, Elsevier, vol. 237(C), pages 11-24.
    19. 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.
    20. Khanna, Nina Zheng & Guo, Jin & Zheng, Xinye, 2016. "Effects of demand side management on Chinese household electricity consumption: Empirical findings from Chinese household survey," Energy Policy, Elsevier, vol. 95(C), pages 113-125.
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    Cited by:

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    5. Sima, Catalina Alexandra & Roscia, Mariacristina & Dancu, Vasile Sebastian, 2022. "Social behavior analysis for improving the positive energy transition," Renewable Energy, Elsevier, vol. 196(C), pages 1325-1344.
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