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Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China

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  • Zhou, Kaile
  • Yang, Changhui
  • Shen, Jianxin

Abstract

With the increasing penetration of information and communication technologies (ICTs) in energy systems, traditional energy systems are being digitized. Advanced analysis of the energy production and consumption data and data-driven decision support can be combined to promote the formation and development of smart energy systems. Smart grids are a specific application of smart energy systems. Different electricity consumption patterns of residential users can be discovered and extracted by clustering analysis of the electricity consumption data collected by smart meters and other data acquisition terminals in a smart grid. This research explores daily electricity consumption patterns of low-voltage residential users in China. The service architecture of smart power use and the structure of electric energy data acquisition system of the State Grid Corporation of China (SGCC) are introduced and a process model for mining daily electricity consumption data is presented. The analysis is based on the fuzzy c-means (FCM) clustering method and a fuzzy cluster validity index (PBMF). A case study of Kunshan City, Jiangsu Province, China is presented, using the daily electricity consumption data of 1312 low-voltage users within a month.

Suggested Citation

  • Zhou, Kaile & Yang, Changhui & Shen, Jianxin, 2017. "Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China," Utilities Policy, Elsevier, vol. 44(C), pages 73-84.
    • Handle: RePEc:eee:juipol:v:44:y:2017:i:c:p:73-84
    DOI: 10.1016/j.jup.2017.01.004
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    Cited by:

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    2. Li, Kehua & Yang, Rebecca Jing & Robinson, Duane & Ma, Jun & Ma, Zhenjun, 2019. "An agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library b," Energy, Elsevier, vol. 174(C), pages 735-748.
    3. Mario Flor & Sergio Herraiz & Ivan Contreras, 2021. "Definition of Residential Power Load Profiles Clusters Using Machine Learning and Spatial Analysis," Energies, MDPI, vol. 14(20), pages 1-15, October.
    4. Kang, J. & Reiner, D., 2021. "Identifying residential consumption patterns using data-mining techniques: A large-scale study of smart meter data in Chengdu, China," Cambridge Working Papers in Economics 2143, Faculty of Economics, University of Cambridge.
    5. 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.
    6. Yang, Ting & Ren, Minglun & Zhou, Kaile, 2018. "Identifying household electricity consumption patterns: A case study of Kunshan, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 861-868.

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