A parametric bootstrap algorithm for cluster number determination of load pattern categorization

Xing Luo, Xu Zhu*, Eng Gee Lim

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


The latest development of smart grid technologies gives rise to big load data and requires load pattern categorization (LPC). How to determine a precise cluster number and choose an appropriate clustering algorithm are critical and still remain challenging in LPC. In this work, we propose a novel parametric bootstrap (PB) algorithm to address the cluster number determination problem in load pattern analysis. The proposed PB algorithm is more robust against dimensionality of data and more applicable for the load demand data which is usually of high dimensionality. The PB algorithm is also general and independent of data type, resulting in a more precise cluster number determined than existing methods with little fluctuation. Moreover, an effective cascade clustering scheme is proposed to categorize load demand data and analyze load patterns, based on the PB algorithm and the K-means++ clustering algorithm. The results indicate the feasibility and the superiority of the proposed approach.

Original languageEnglish
Pages (from-to)50-60
Number of pages11
Publication statusPublished - 1 Aug 2019


  • Cascade clustering
  • Cluster number determination
  • Load pattern categorization
  • Parametric bootstrap algorithm

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