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Authors
Advisor(s)
Abstract(s)
This 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.
Description
Keywords
Electricity Price Forecast Risk Management Neural Networks Liberalized Energy Markets
Citation
Publisher
Institute of Electrical and Electronics Engineers