Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5936
Título: A Data-mining-based Methodology to support MV Electricity Customers' Characterization
Autor: Ramos, Sérgio
Duarte, João
Duarte, F. Jorge
Vale, Zita
Palavras-chave: Load profiling
Data Mining
Clustering Validity
Data: Mar-2015
Editora: Elsevier
Relatório da Série N.º: Energy and Buildings;Vol. 91
Resumo: This paper presents an electricity medium voltage (MV) customer characterization framework supportedby knowledge discovery in database (KDD). The main idea is to identify typical load profiles (TLP) of MVconsumers and to develop a rule set for the automatic classification of new consumers. To achieve ourgoal a methodology is proposed consisting of several steps: data pre-processing; application of severalclustering algorithms to segment the daily load profiles; selection of the best partition, corresponding tothe best consumers’ segmentation, based on the assessments of several clustering validity indices; andfinally, a classification model is built based on the resulting clusters. To validate the proposed framework,a case study which includes a real database of MV consumers is performed.
Peer review: yes
URI: http://hdl.handle.net/10400.22/5936
DOI: 10.1016/j.enbuild.2015.01.035
Versão do Editor: http://www.sciencedirect.com/science/article/pii/S0378778815000420
Aparece nas colecções:ISEP – GECAD – Artigos

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