Azevedo, FilipeVale, Zita2017-03-0820051-59975-028-7http://hdl.handle.net/10400.22/9581This paper provides a different approach for electricity price forecast from risk management point of view. Making use of neural networks, the methodology presented here has as main concern finding the maximum and the minimum System Marginal Price (SMP) for a specific programming period, with a certain confidence level. To train the neural network, probabilistic information from past years is used. This approach was developed with the objective of integrating a decisionsupport system that uses Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data are presented and discussed in detail.engElectricity Price ForecastRisk ManagementNeural NetworksLiberalized Energy Marketsconference object10.1109/ISAP.2005.1599249