An electric energy consumer characterization framework based on data mining techniques

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 un...

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Bibliographic Details
Published in:IEEE transactions on power systems 2005-05, Vol.20 (2), p.596-602
Main Authors: Figueiredo, V., Rodrigues, F., Vale, Z., Gouveia, J.B.
Format: Article
Language:English
Subjects:
Classification
clustering
Construction
consumer classes
Data mining
Decision trees
Delta modulation
Electricity
Electricity supply industry
Energy consumption
Knowledge engineering
Learning
load profiles
Modules
Neural networks
Profiling
Protocols
Supervised learning
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Summary: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.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2005.846234