Pattern recognition of load profiles in managing electricity distribution

• Autor(a) principal: Ferreira, Adonias Magdiel Silva
• Data de Publicação: 2013
• Outros Autores: Fontes, Cristiano Hora de Oliveira, Maranbio, Jorge Eduardo Soto, Cavalcante, Carlos Arthur Mattos Teixeira
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFBA
• Texto Completo: http://www.repositorio.ufba.br/ri/handle/ri/8603
• topic: Typing load profiles; clustering; electricity sector

This works presents a method of selection, classification and clustering load curves (SCCL) which is able to identify a greater diversity of consumption patterns existing in the electricity distribution sector. The method was developed to estimate the features of a sample of load curves so as to identify the consumption behavior of a population of consumers. The algorithm comprises four steps that extract essential features of a load curve of residential users, seasonal and temporal profils in particular. The method was successfully implemented and tested in the context of an energy efficiency program developed by a company associated to the electricity distribution sector (Electric Company of Maranhão, Brazil). This program comprised the analysis of the impact of replacing refrigerators in a universe of low-income consumers in some towns in the state of Maranhão (Brazil). Patterns of load profiles using the typing method developed were applied and the results were compared with a well known method of time series clustering already established in the literature, the Fuzzy C-Means (FCM). Based on the main features of a load profile, the analysis confirmed that the SCCL method was capable of identifying a greater diversity of patterns, demonstrating the potential of this method for better characterization of types of demand. This is an important aspect for the process of decision making in the energy distribution sector. Furthermore, a well known index (Silhouette index) was also adopted to quantify the level of uniformity within and between clusters.