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
Main Author: Figueiredo, Vera (author)
Other Authors: Rodrigues, Fátima (author), Vale, Zita (author), Gouveia, Joaquim Borges (author)
Format: article
Language:eng
Published: 2017
Subjects:
Classification
Clustering
Consumer classes
Data mining
Decision trees
Load profiles
Neural networks
Online Access:http://hdl.handle.net/10400.22/9979
Country:Portugal
Oai:oai:recipp.ipp.pt:10400.22/9979
Description
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.

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