Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5949
Título: Load Profiling Tool to Support Smart Grid Operation Scenarios
Autor: Ramos, Sérgio
Praça, Isabel
Vale, Zita
Sousa, Tiago
Faria, Vera
Palavras-chave: Data mining
Clustering
Smart grid
Load profiling
Data: 14-Abr-2014
Editora: IEEE
Relatório da Série N.º: PES;2014
Resumo: 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.
URI: http://hdl.handle.net/10400.22/5949
DOI: 10.1109/TDC.2014.6863352
Versão do Editor: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6863352&queryText%3DLoad+Profiling+Tool+to+Support+Smart+Grid+Operation+Scenarios
Aparece nas colecções:ISEP – GECAD – Comunicações em eventos científicos

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