Azevedo, FilipeVale, ZitaOliveira, P. B. Moura2013-05-032013-05-032007978-986-01-2607-5http://hdl.handle.net/10400.22/1508Long-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.engLiberalized electricity marketsParticle swarm optimizationPrice forecastRisk managementconference object2013-04-1110.1109/ISAP.2007.4441656