Avaliação de métodos de previsão de cargas elétricas em curto prazo para aplicação em sistemas de distribuição inteligentes

• Ano de defesa: 2013
• Autor(a) principal: Garcia, Lidia Maria Dutra
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• dARK ID: ark:/26339/0013000007c64
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; BR; Engenharia Elétrica; UFSM; Programa de Pós-Graduação em Engenharia Elétrica
• Palavras-chave em Português: Previsão de demanda; Redes inteligentes; Sistemas elétricos de distribuição; Redes neurais artificiais; Neurofuzzy; Load forecast; Smart grids; Distribution electric system; Artificial neural network; Neurofuzzzy; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://repositorio.ufsm.br/handle/1/8532

In Electric Power Systems understand the future behavior of electric loads is crucial to make a decision. The long, medium and short term load forecast is essential power systems. Considering the gradual transformation of the traditional distribution systems to smart grids, where the electric system automation and online communication are effective, the forecast in very short term gets new challenges. Based on these facts, the objective of this thesis is to identify the most appropriate methods to accomplish these forecasts to contribute to decision-making in distribution systems operation. Various techniques of forecasting and simulations in different methods were studied, in order to identify which of these offers the best results regarding demand the forecast in the very short term. The quantities considered to make predictions and, which have more relevance to the horizon under study are electrical and climate. The methods used in the simulations were the Artificial Neural Networks (ANN) type recurrent Elman e NARX and Neurofuzzy.