Validating Clustering Frameworks for Electric Load Demand Profiles

Mayank Jain, Tarek Alskaif, Soumyabrata Dev

Research output: Contribution to journalArticleAcademicpeer-review


Large-scale deployment of smart meters has made it possible to collect sufficient and high-resolution data of residential electric demand profiles. Clustering analysis of these profiles is important to further analyze and comment on electricity consumption patterns. Although many clustering techniques have been proposed in the literature over the years, it is often noticed that different techniques fit best for different datasets. To identify the most suitable technique, standard clustering validity indices are often used. These indices focus primarily on the intrinsic characteristics of the clustering results. Moreover, different indices often give conflicting recommendations which can only be clarified with heuristics about the dataset and/or the expected cluster structures -- information which is rarely available in practical situations. This paper presents a novel scheme to validate and compare the clustering results objectively. Additionally, the proposed scheme considers all the steps prior to the clustering algorithm, including the pre-processing and dimensionality reduction steps, in order to provide recommendations over the complete framework. Accordingly, the proposed strategy is shown to provide better, unbiased, and uniform recommendations as compared to the standard Clustering Validity Indices.

Original languageEnglish
JournalIEEE Transactions on Industrial Informatics
Publication statusE-pub ahead of print - 23 Feb 2021


  • Clustering algorithms
  • clustering framework
  • clustering validation
  • Covariance matrices
  • Dimensionality reduction
  • dimensionality reduction
  • electric demand profiles
  • Informatics
  • Principal component analysis
  • Standards
  • Task analysis

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