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Automated Detection of Electric Energy Consumption Load Profile Patterns

Author

Listed:
  • Ignacio Benítez

    (Sustainability and Energy Efficiency Area, Fundación Valenciaport, Building III, Avda. Muelle del Túria s/n, 46023 Valencia, Spain)

  • José-Luis Díez

    (Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera s/n, 46023 Valencia, Spain)

Abstract

Load profiles of energy consumption from smart meters are becoming more and more available, and the amount of data to analyse is huge. In order to automate this analysis, the application of state-of-the-art data mining techniques for time series analysis is reviewed. In particular, the use of dynamic clustering techniques to obtain and visualise temporal patterns characterising the users of electrical energy is deeply studied. The performed review can be used as a guide for those interested in the automatic analysis and groups of behaviour detection within load profile databases. Additionally, a selection of dynamic clustering algorithms have been implemented and the performances compared using an available electric energy consumption load profile database. The results allow experts to easily evaluate how users consume energy, to assess trends and to predict future scenarios.

Suggested Citation

  • Ignacio Benítez & José-Luis Díez, 2022. "Automated Detection of Electric Energy Consumption Load Profile Patterns," Energies, MDPI, vol. 15(6), pages 1-26, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:2176-:d:772649
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    References listed on IDEAS

    as
    1. Minseok Jang & Hyun-Cheol Jeong & Taegon Kim & Sung-Kwan Joo, 2021. "Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs," Energies, MDPI, vol. 14(19), pages 1-12, September.
    2. Nakyoung Kim & Sangdon Park & Joohyung Lee & Jun Kyun Choi, 2018. "Load Profile Extraction by Mean-Shift Clustering with Sample Pearson Correlation Coefficient Distance," Energies, MDPI, vol. 11(9), pages 1-20, September.
    3. Katarina Košmelj & Vladimir Batagelj, 1990. "Cross-sectional approach for clustering time varying data," Journal of Classification, Springer;The Classification Society, vol. 7(1), pages 99-109, March.
    4. Min Ji & Fuding Xie & Yu Ping, 2013. "A Dynamic Fuzzy Cluster Algorithm for Time Series," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-7, April.
    Full references (including those not matched with items on IDEAS)

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