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k-means based load estimation of domestic smart meter measurements

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  • Al-Wakeel, Ali
  • Wu, Jianzhong
  • Jenkins, Nick

Abstract

A load estimation algorithm based on k-means cluster analysis was developed. The algorithm applies cluster centres – of previously clustered load profiles – and distance functions to estimate missing and future measurements. Canberra, Manhattan, Euclidean, and Pearson correlation distances were investigated. Several case studies were implemented using daily and segmented load profiles of aggregated smart meters. Segmented profiles cover a time window that is less than or equal to 24h. Simulation results show that Canberra distance outperforms the other distance functions. Results also show that the segmented cluster centres produce more accurate load estimates than daily cluster centres. Higher accuracy estimates were obtained with cluster centres in the range of 16–24h. The developed load estimation algorithm can be integrated with state estimation or other network operational tools to enable better monitoring and control of distribution networks.

Suggested Citation

  • Al-Wakeel, Ali & Wu, Jianzhong & Jenkins, Nick, 2017. "k-means based load estimation of domestic smart meter measurements," Applied Energy, Elsevier, vol. 194(C), pages 333-342.
    • Handle: RePEc:eee:appene:v:194:y:2017:i:c:p:333-342
    DOI: 10.1016/j.apenergy.2016.06.046
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    References listed on IDEAS

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