Previsão de demanda no médio prazo utilizando redes neurais artificiais em sistemas de distribuição de energia elétrica

• Ano de defesa: 2016
• Autor(a) principal: Medeiros , Romero Álamo Oliveira de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Engenharia Elétrica; Programa de Pós-Graduação em Engenharia Elétrica; UFPB
• Palavras-chave em Português: Previsão de Demana; Distribuição de Enérgia Elétrica; Previsão de Séries Temporais; Redes Neurais Artificiais; Inteligência Artificial; Demand Forecasting; Electric Energy Distribution; Time Series Forecasting; Artificial neural Netwook; Artificial Intelligence; ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/tede/8795

The demand forecasting studies are of great importance for electricity companies, because there is a need to allocate their resources well in advance, requiring a medium and long- term p lanning. These resources can be the purchase of new equipment, the transmission line acquisition or construction, scheduled maintenance and the purchase and sale of energy. I n this work, a support tool has been developed for experts in strategic planning i n power distribution systems using artificial neural networks to demand forecasting. For the proposed method, it implemented a demand forecasting procedure in the medium term of the region fueled by three substations belonging to the power distribution sys tem managed by EnergisaPB, using a computer model based on Multilayer Perceptron (MLP) artificial neural networks with the assistance of Matlab ® environment. The database was structured by the measurements of active power from 2008 to 2014, provided by En ergisa/PB and the forecast achieved one year ahead (52 weeks) compared with the real data of 2014. In addition, it was possible to evaluate the performance of RNA and estimate the demand growth in the region supplied by each substation, which can assist th e distribution system expansion planning.