Abstract—Power utilities collect a large amount of metering data from substations and customers. This data can provide insights for planning outages, making network investment decisions, predicting future load growth and predictive maintenance. One of the requirements is the ability to group similar behaving loads together. This paper provides a comparison between different similarity measures, used in the k-means clustering algorithm, to group daily load profiles together based on metering data. The various methods are compared using two well-known cluster evaluation metrics and the results are then analysed by subject matter experts to determine the validity of the findings. The results, from our particular data set, indicate that various speed improvement techniques can be considered that complement the k-means algorithm without sacrificing intra-to inter-cluster accuracy. A small increase in the optimal number of clusters, using domain expertise, allowed for additional profiles to be extracted that were not explained by algorithmic evaluations. Interplay between both theoretical evaluations and domain knowledge facilitated a preferred number of clusters for practical purposes.

Index Terms—daily load profiles, customer segmentation, k-means clustering, similarity measures, non-uniform binary split

Cite: J. du Toit, R. Davimes, A. Mohamed, K. Patel, and J. M. Nye, "Customer Segmentation Using Unsupervised Learning on Daily Energy Load Profiles," Vol. 7, No. 2, pp. 69-75, May, 2016. doi: 10.12720/jait.7.2.69-75