Papers › Validating Clustering Frameworks for Electric Load Demand Profiles
Validating Clustering Frameworks for Electric Load Demand Profiles
Mayank Jain, Tarek Alskaif, Soumyabrata Dev
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 that 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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections