Deriving the optimal number of clusters in the electricity consumer segmentation procedure
Ioannis P. Panapakidis, Minas C. Alexiadis, Grigoris K. Papagiannis · 2013
This study examines a set of methods that determine the optimal number of clusters in the electricity consumer segmentation procedure. For the purpose of clustering the load curves of the consumers, we involve two algorithms of different concept and complexity, namely the Minimum Variance Method (MVM) hierarchical agglomerative algorithm and the Fuzzy C-Means (FCM). A parametric analysis takes place in order to optimize the FCM` s parameters. Apart from the two clustering algorithms, we introduce in the load profiling studies two other methods that provide indications of the number of clusters within a data sample, namely the Max-Min and the Chain-map methods. For the sake of assessing the algorithm effectiveness, we utilize the ratio of Within Cluster sum of squares to Between Cluster variation (WCBCR) adequacy measure and the Bayesian Information Criterion (BIC). We also propose an improved version of the WCBCR.