| dc.creator | Servidone | |
| dc.creator | Gabriela; Conti | |
| dc.creator | Dante | |
| dc.date | 2016 | |
| dc.date | 2017-08-30T17:36:13Z | |
| dc.date | 2017-08-30T17:36:13Z | |
| dc.date.accessioned | 2018-03-29T05:28:38Z | |
| dc.date.available | 2018-03-29T05:28:38Z | |
| dc.identifier | Pesquisa Operacional. Sociedade Brasileira De Pesquisa Operacional, v. 36, n. 3, p. 575 - 595 | |
| dc.identifier | 0101-7438 | |
| dc.identifier | S0101-74382016000300575 | |
| dc.identifier | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382016000300575 | |
| dc.identifier | http://repositorio.unicamp.br/jspui/handle/REPOSIP/324395 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1358613 | |
| dc.description | 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. | |
| dc.description | 36 | |
| dc.description | 3 | |
| dc.description | 575 | |
| dc.description | 595 | |
| dc.language | Ingles | |
| dc.publisher | Sociedade Brasileira de Pesquisa Operacional | |
| dc.relation | Pesquisa Operacional | |
| dc.rights | aberto | |
| dc.source | Scielo | |
| dc.subject | Data Mining | |
| dc.subject | Electricity Consumption | |
| dc.subject | Load Curves | |
| dc.subject | Clustering | |
| dc.subject | Patterns | |
| dc.subject | Time Granularity | |
| dc.title | Discovering And Labelling Of Temporal Granularity Patterns In Electric Power Demand With A Brazilian Case Study | |
| dc.type | Artículos de revistas | |
