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Long-term price range forecast applied to risk management using regression models
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Orientador(es)
Resumo(s)
Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find
the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the
obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in
detail.
Descrição
Palavras-chave
Liberalized electricity markets Particle swarm optimization Price forecast Risk management
Contexto Educativo
Citação
Editora
IEEE