Spatial pattern recognition of urban sprawl using a geographically weighted regression for spatial electric load forecasting
Carregando...
Arquivos
Fontes externas
Fontes externas
Data
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
Tipo
Trabalho apresentado em evento
Direito de acesso
Acesso aberto
Arquivos
Fontes externas
Fontes externas
Resumo
Distribution utilities must perform forecasts in spatial manner to determine the locations that could increase their electric demand. In general, these forecasts are made in the urban area, without regard to the preferences of the inhabitants to develop its activities outside the city boundary. This may lead to errors in decision making of the distribution network expansion planning. In order to identify such preferences, this paper presents a geographically weighted regression that explore spatial patterns to determines the probability of rural regions become urban zones, as part of the urban sprawl. The proposed method is applied in a Brazilian midsize city, showing that the use of the calculated probabilities decreases the global error of spatial load forecasting in 6.5% of the load growth.
Descrição
Palavras-chave
Distribution network planning, geographically weighted regression, spatial electric load forecasting, spatial regression
Idioma
Inglês
Citação
2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015.
