Identifying optimal clustering structures for residential energy consumption patterns using competency questions

Toussaint, W and Moodley, D (2020) Identifying optimal clustering structures for residential energy consumption patterns using competency questions, Proceedings of SAICSIT '20: Conference of the South African Institute of Computer Scientists and Information Technologists 2020, 14-16 September 2020, ACM.

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Abstract

Traditional cluster analysis metrics rank clustering structures in terms of compactness and distinctness of clusters. However, in real world applications this is usually insufficient for selecting the optimal clustering structure. Domain experts and visual analysis are often relied on during evaluation, which results in a selection process that tends to be adhoc, subjective and difficult to reproduce. This work proposes the use of competency questions and a cluster scoring matrix to formalise expert knowledge and application requirements for qualitative evaluation of clustering structures. We show how a qualitative ranking of clustering structures can be integrated with traditional metrics to guide cluster evaluation and selection for generating representative energy consumption profiles that characterise residential electricity demand in South Africa. The approach is shown to be highly effective for identifying usable and expressive consumption profiles within this specific application context, and certainly has wider potential for efficient, transparent and repeatable cluster selection in real-world applications.

Item Type:	Conference paper
Subjects:	Computing methodologies > Machine learning Hardware > Power and energy > Energy distribution
Date Deposited:	21 Dec 2020 10:48
Last Modified:	21 Dec 2020 10:48
URI:	http://pubs.cs.uct.ac.za/id/eprint/1388

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