Publicação: Short-Term Multinodal Load Forecasting Using a Fuzzy-ARTMAP Neural Network
Carregando...
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
Resumo
The prediction of electric charges is essential in the electric power system, because it establishes when and how much of generation, transmission and distribution capacity must be arranged to meet the expected load without interruptions in supply. Therefore, the more accurate, reliable and fast the results, the better quality the forecast will be. This paper proposes an approach that performs the forecast considering several points of the electricity network (multinodal forecast), where different types of consumers are considered (industrial, commercial and residential). In this problem is used an ARTMAP Fuzzy artificial neural network , that is based in the theory of resonance adaptative (ART). The main characteristic of neural networks of the ART family is the stability and plasticity that provide results quickly and accurately. In order to test the proposed forecast system, results of 24 hours (48 points) ahead are presented for nine substations of a New Zealand Electrical Company.
Descrição
Palavras-chave
Aggregate Load, Electrical System Distribution, Fuzzy ARTMAP Neural Network, Load Forecasting
Idioma
Inglês
Como citar
2019 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT Latin America 2019.
