Figueiredo, VeraRodrigues, FátimaVale, ZitaGouveia, Joaquim Borges2017-06-292017-06-292005http://hdl.handle.net/10400.22/9979This paper presents an electricity consumer characterization framework based on a knowledge discovery in databases (KDD) procedure, supported by data mining (DM) techniques, applied on the different stages of the process. The core of this framework is a data mining model based on a combination of unsupervised and supervised learning techniques. Two main modules compose this framework: the load profiling module and the classification module. The load profiling module creates a set of consumer classes using a clustering operation and the representative load profiles for each class. The classification module uses this knowledge to build a classification model able to assign different consumers to the existing classes. The quality of this framework is illustrated with a case study concerning a real database of LV consumers from the Portuguese distribution company.engClassificationClusteringConsumer classesData miningDecision treesLoad profilesNeural networksjournal article10.1109/TPWRS.2005.846234