A Data Mining Framework for Electric Load Profiling
Data(s) |
04/05/2015
04/05/2015
01/04/2013
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Resumo |
This paper consists in the characterization of medium voltage (MV) electric power consumers based on a data clustering approach. It is intended to identify typical load profiles by selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The best partition is selected using several cluster validity indices. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ behavior. The data-mining-based methodology presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partitions. To validate our approach, a case study with a real database of 1.022 MV consumers was used. |
Identificador |
http://hdl.handle.net/10400.22/5901 10.1109/ISGT-LA.2013.6554489 |
Idioma(s) |
eng |
Publicador |
IEEE |
Relação |
PES;2013 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6554489&queryText%3DA+Data+Mining+Framework+for+Electric+Load+Profiling |
Direitos |
closedAccess |
Palavras-Chave | #Data mining #Clustering #Smart Grid #Typical load profiles |
Tipo |
conferenceObject |