Please use this identifier to cite or link to this item:
Type: Artigo
Title: Discovering And Labelling Of Temporal Granularity Patterns In Electric Power Demand With A Brazilian Case Study
Author: Servidone
Gabriela; Conti
Abstract: Clustering is commonly used to group data in order to represent the behaviour of a system as accurately as possible by obtaining patterns and profiles. In this paper, clustering is applied with partitioning-clustering techniques, specifically, Partitioning around Medoids (PAM) to analyse load curves from a city of South-eastern Brazil in São Paulo state. A top-down approach in time granularity is performed to detect and to label profiles which could be affected by seasonal trends and daily/hourly time blocks. Time-granularity patterns are useful to support the improvement of activities related to distribution, transmission and scheduling of energy supply. Results indicated four main patterns which were post-processed in hourly blocks by using shades of grey to help final-user to understand demand thresholds according to the meaning of dark grey, light grey and white colours. A particular and different behaviour of load curve was identified for the studied city if it is compared to the classical behaviour of urban cities.
Subject: Data Mining
Electricity Consumption
Load Curves
Time Granularity
Editor: Sociedade Brasileira de Pesquisa Operacional
Citation: Pesquisa Operacional. Sociedade Brasileira De Pesquisa Operacional, v. 36, n. 3, p. 575 - 595
Rights: aberto
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File SizeFormat 
S0101-74382016000300575.pdf2.84 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.