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A comparative study of clustering techniques for electrical load pattern segmentation

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  • Rajabi, Amin
  • Eskandari, Mohsen
  • Ghadi, Mojtaba Jabbari
  • Li, Li
  • Zhang, Jiangfeng
  • Siano, Pierluigi

Abstract

Smart meters have been widely deployed in power networks since the last decade. This trend has resulted in an enormous volume of data being collected from the electricity customers. To gain benefits for various stakeholders in power systems, proper data mining techniques, such as clustering, need to be employed to extract the underlying patterns from energy consumptions. In this paper, a comparative study of different techniques for load pattern clustering is carried out. Different parameters of the methods that affect the clustering results are evaluated and the clustering algorithms are compared for two data sets. In addition, the two suitable and commonly used data size reduction techniques and feature definition/extraction methods for load pattern clustering are analysed. Furthermore, the existing studies on clustering of electricity customers are reviewed and the main results are highlighted. Finally, the future trends and major applications of clustering consumption patterns are outlined to inform industry practitioners and academic researchers to optimize smart meter operational use and effectiveness.

Suggested Citation

  • Rajabi, Amin & Eskandari, Mohsen & Ghadi, Mojtaba Jabbari & Li, Li & Zhang, Jiangfeng & Siano, Pierluigi, 2020. "A comparative study of clustering techniques for electrical load pattern segmentation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    • Handle: RePEc:eee:rensus:v:120:y:2020:i:c:s1364032119308354
    DOI: 10.1016/j.rser.2019.109628
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    6. Thiago Eliandro de Oliveira Gomes & André Ross Borniatti & Vinícius Jacques Garcia & Laura Lisiane Callai dos Santos & Nelson Knak Neto & Rui Anderson Ferrarezi Garcia, 2023. "Clustering Electrical Customers with Source Power and Aggregation Constraints: A Reliability-Based Approach in Power Distribution Systems," Energies, MDPI, vol. 16(5), pages 1-20, March.
    7. Soyoung Yoo & Jiyong Eom & Ingoo Han, 2020. "Factors Driving Consumer Involvement in Energy Consumption and Energy-Efficient Purchasing Behavior: Evidence from Korean Residential Buildings," Sustainability, MDPI, vol. 12(14), pages 1-23, July.
    8. Santiago Bañales & Raquel Dormido & Natividad Duro, 2021. "Smart Meters Time Series Clustering for Demand Response Applications in the Context of High Penetration of Renewable Energy Resources," Energies, MDPI, vol. 14(12), pages 1-22, June.
    9. Senfeng Cen & Jae Hung Yoo & Chang Gyoon Lim, 2022. "Electricity Pattern Analysis by Clustering Domestic Load Profiles Using Discrete Wavelet Transform," Energies, MDPI, vol. 15(4), pages 1-18, February.
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