Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5901
Título: A Data Mining Framework for Electric Load Profiling
Autor: Ramos, Sérgio
Duarte, João
Duarte, F. Jorge
Vale, Zita
Faria, Pedro
Palavras-chave: Data mining
Smart Grid
Typical load profiles
Data: Abr-2013
Editora: IEEE
Relatório da Série N.º: PES;2013
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.
URI: http://hdl.handle.net/10400.22/5901
DOI: 10.1109/ISGT-LA.2013.6554489
Versão do Editor: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6554489&queryText%3DA+Data+Mining+Framework+for+Electric+Load+Profiling
Aparece nas colecções:ISEP – GECAD – Comunicações em eventos científicos

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