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Clustering Analysis for Active and Reactive Energy Consumption Data Based on AMI Measurements

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  • Oscar A. Bustos-Brinez

    (EM&D Research Group, Electrical and Electronics Engineering Department, Faculty of Engineering, Universidad Nacional de Colombia, Bogota 111321, Colombia
    MindLab Research Group, Systems and Industrial Engineering Department, Faculty of Engineering, Universidad Nacional de Colombia, Bogota 111321, Colombia)

  • Javier Rosero Garcia

    (EM&D Research Group, Electrical and Electronics Engineering Department, Faculty of Engineering, Universidad Nacional de Colombia, Bogota 111321, Colombia)

Abstract

Electrical data analysis based on smart grids has become a fundamental tool used by electrical grid stakeholders to understand the energy consumption patterns of users, although many proposals in this area do not consider reactive energy as another source of useful information regarding distribution costs and threats to the grid. In this regard, the analysis of reactive energy patterns can become an extremely useful addition to existing electrical data analysis frameworks. This work shows the application of a series of clustering techniques over measurements of both active and reactive energy consumption measured for end users from the Colombian electrical network, including an analysis of the efficiency of the network measured by calculating the ratio of active energy to total consumption (power factor) per user. This allows a detailed characterization of users to be compiled, based on the identification of different active and reactive energy consumption behaviors, which could help grid operators to improve overall grid management and to increase the efficiency of their reactive energy compensation strategies.

Suggested Citation

  • Oscar A. Bustos-Brinez & Javier Rosero Garcia, 2025. "Clustering Analysis for Active and Reactive Energy Consumption Data Based on AMI Measurements," Energies, MDPI, vol. 18(1), pages 1-22, January.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:1:p:221-:d:1561256
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    References listed on IDEAS

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    1. 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.
    2. Daiva Stanelyte & Virginijus Radziukynas, 2019. "Review of Voltage and Reactive Power Control Algorithms in Electrical Distribution Networks," Energies, MDPI, vol. 13(1), pages 1-26, December.
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