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Load Profiling Tool to Support Smart Grid Operation Scenarios
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Orientador(es)
Resumo(s)
This paper presents the characterization of high
voltage (HV) electric power consumers based on a data
clustering approach. The typical load profiles (TLP) are
obtained selecting the best partition of a power consumption
database among a pool of data partitions produced by several
clustering algorithms. The choice of the best partition is
supported using several cluster validity indices. The proposed
data-mining (DM) based methodology, that includes all steps
presented in the process of knowledge discovery in databases
(KDD), presents an automatic data treatment application in
order to preprocess the initial database in an automatic way,
allowing time saving and better accuracy during this phase.
These methods are intended to be used in a smart grid
environment to extract useful knowledge about customers’
consumption behavior. To validate our approach, a case study
with a real database of 185 HV consumers was used.
Descrição
Palavras-chave
Data mining Clustering Smart grid Load profiling
Contexto Educativo
Citação
Editora
IEEE