Comparison of clustering techniques for residential load profiles in South Africa

Toussaint, W and Moodley, D (2019) Comparison of clustering techniques for residential load profiles in South Africa, Proceedings of South African Forum for Artificial Intelligence Research, 4-6 December 2019, Cape town, South Africa, 2540, 117-132, CEUR.

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This work compares techniques for clustering metered residential energy consumption data to construct representative daily load profiles in South Africa. The input data captures a population with high variability across temporal, geographic, social and economic dimensions. Different algorithms, normalisation and pre-binning techniques are evaluated to determine their effect on producing a good clustering structure. A Combined Index is developed as a relative score to ease the comparison of experiments across different metrics. The study shows that normalisation, specifically unit norm and the zero-one scaler, produce the best clusters. Pre-binning appears to improve clustering structures as a whole, but its effect on individual experiments remains unclear. Like several previous studies, the k-means algorithm produces the best results. To our knowledge this is the first work that rigorously compares state of the art cluster analysis techniques in the residential energy domain in a developing country context.

Item Type: Conference paper
Subjects: Computing methodologies > Machine learning > Learning paradigms > Unsupervised learning
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Date Deposited: 05 Mar 2020 11:45
Last Modified: 05 Mar 2020 11:45

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