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An Electric Energy Consumer Characterization Framework Based on Data Mining Techniques
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Orientador(es)
Resumo(s)
This 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.
Descrição
Palavras-chave
Classification Clustering Consumer classes Data mining Decision trees Load profiles Neural networks
Contexto Educativo
Citação
Editora
Institute of Electrical and Electronics Engineers