DISCOVERING AND LABELLING OF TEMPORAL GRANULARITY PATTERNS IN ELECTRIC POWER DEMAND WITH A BRAZILIAN CASE STUDY

• Autor(a) principal: Servidone,Gabriela
• Data de Publicação: 2016
• Outros Autores: Conti,Dante
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Pesquisa operacional (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382016000300575
• topic: data mining; electricity consumption; load curves; clustering; patterns; time granularity

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.