Modelo de previsão de demanda de ordens de manutenção emergenciais baseada em fatores climáticos em um sistema de distribuição de energia elétrica

• Ano de defesa: 2020
• Autor(a) principal: Basso, Lucas Machado
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; Brasil; Engenharia de Produção; UFSM; Programa de Pós-Graduação em Engenharia de Produção; Centro de Tecnologia
• Palavras-chave em Português: Previsão de demanda; Modelos preditivos; Manutenção emergencial; Mineração de dados; Demand forecast; Predictive models; Emergency maintenance; Data mining; CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO
• Link de acesso: http://repositorio.ufsm.br/handle/1/21046

The National Electric Energy Agency (ANEEL) regulates the electric energy distribution market in Brazil and establishes targets and deadlines for meeting requests from consumers to energy utilities. Failure to meet these deadlines generates fines to be paid by the concessionaires. The first step in solving this problem is to establish a model for forecasting the demand for hours in emergency maintenance orders that will serve as a basis for planning the capacity required to answer these calls. The objective of this study is to develop a demand forecasting model for emergency maintenance orders in an electricity distribution system considering climatic factors that may affect this demand. The methodology used in the project follows five steps: (i) bibliographic review in the areas of demand forecasting and electricity distribution systems; (ii) study of the characteristics of the demand for emergency maintenance orders and their relationship with climatic factors through historical data; (iii) choosing the most appropriate demand forecasting method; (iv) development of a demand forecast model for emergency orders that considers climatic factors; and (v) forecasting this demand from the data collected. As a result of this research project, there is a demand forecasting model for emergency maintenance orders based on climatic factors in an electricity distribution system. Forecasting the demand for maintenance orders is essential for planning the manpower, materials and infrastructure resources needed to answer calls and ensure the availability of the electricity distribution system.