A data-mining-based methodology to support MV electricity customers’ characterization

This paper presents an electricity medium voltage (MV) customer characterization framework supported by knowledge discovery in database (KDD). The main idea is to identify typical load profiles (TLP) of MV consumers and to develop a rule set for the automatic classification of new consumers. To achieve our goal a methodology is proposed consisting of several steps: data pre-processing; application of several clustering algorithms to segment the daily load profiles; selection of the best partition, corresponding to the best consumers’ segmentation, based on the assessments of several clustering validity indices; and finally, a classification model is built based on the resulting clusters. To validate the proposed framework, a case study which includes a real database of MV consumers is performed.