No abstract

This paper presents a short review on Data Mining (DM), with a focus on the characterization of electricity customers supported on knowledge discovery in database (KDD) process. The study includes several steps: first, few concepts of the KDD process are presented, including data selection, pre-processing, DM phase, data evaluation and data knowledge; following, a short review of clustering algorithms is presented including partitional, hierarchical, fuzzy, evolutionary methods, and Self-Organizing Maps; finally, the main concepts and methods for load classification are summarized. The main purpose of this work is to present a short review of DM techniques applied to the characterization of typical load profiles in electrical systems and new customers' classification.