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Evaluation of Clustering Techniques for Generating Household Energy Consumption Patterns in a Developing Country

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posted on 18.03.2021, 13:01 by Wiebke ToussaintWiebke Toussaint, Deshen Moodley, Thomas Andreas Meyer
This work compares and evaluates clustering techniques for generating representative daily load profiles that are characteristic of residential energy consumers in South Africa. The input data captures two decades of metered household consumption, covering 14 945 household years and 3 295 848 daily load patterns of a population with high variability across temporal, geographic, social and economic dimensions.Different algorithms, normalisation and pre-binning techniques are evaluated to determine the best clustering structure. The study shows that normalisation is essential for producing good clusters. Specifically, unit norm produces more usable and more expressive clusters than the zero-one scaler, which is the most common method of normalisation used in the domain. While pre-binning improves clustering results for the dataset, the choice of pre-binning method does not significantly impact the quality of clusters produced. Data representation and especially the inclusion or removal of zero-valued profiles is an important consideration in relation to the pre-binning approach selected. Like several previous studies, the k-means algorithm produces the best results. Introducing a qualitative evaluation framework facilitated the evaluation process and helped identify a top clustering structure that is significantly more useable than those that would have been selected based on quantitative metrics alone. The approach demonstrates how explicitly defined qualitative evaluation measures can aid in selecting a clustering structure that is more likely to have real-world application. To our knowledge this is the first work that uses cluster analysis to generate customer archetypes from representative daily load profiles in a highly variable, developing country context.

References:
https://doi.org/10.25828/56nh-fw77
https://doi.org/10.25828/mf8s-hh79

Funding

Centre for Artificial Intelligence Research (CAIR)

History

Department/Unit

Computer Science