Metodologia estocástica para previsão de demanda de serviços emergenciais em concessionárias de energia elétrica

• Ano de defesa: 2016
• Autor(a) principal: Guimarães, Iochane Garcia
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; BR; Engenharia de Produção; UFSM; Programa de Pós-Graduação em Engenharia de Produção
• Palavras-chave em Português: Ordem de serviço emergencial; Previsão de demanda; Concessionárias de energia elétrica; Order emergency service; Demand forecasting; Electric power concessionaires; CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO
• Link de acesso: http://repositorio.ufsm.br/handle/1/8378

The goal of the electricity distribution companies is to provide consumers with a continuous supply of energy and quality. This dissertation addresses the Vehicle Routing Problem, specifically the partially dynamic routing with static entries, where some events that occur stochastically are dynamically incorporated during the execution of the service. In this sense, we sought to develop a methodology to provide the emergency service events that arise randomly during the working day, taking into account attributes of location, time of service and time of occurrence, to minimize the travel time of vehicles on scheduled routes. For that, a sequence of steps has been developed and described for the structuring of a demand forecasting system, which should be able to design patterns and trends analyzed data from past demands. Intending to meet these assumptions, the study sought support in two forecasting methods: exponential smoothing and prediction from conditional probabilities. The study also sought to identify the main variables that influence the way aleatótia the occurrence of emergency orders. The results obtained with these methods, assisted in the capture of the stochasticity of the order process emergency orders, as well as in forecasting service demand. The work seeks to identify the input variables for routing, providing subsidies for the analyzed company that does not have this information.