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Short-Term Nodal Congestion Price Forecasting In A Large-Scale Power Market Using Ann With Genetic Optimization Training, Majid Moazzami, Rahmat Allah Hooshmand

Turkish Journal of Electrical Engineering and Computer Sciences

In a daily power market, price and load forecasting are the most important signals for the market participants. In this paper, an accurate feed-forward neural network model with a genetic optimization Levenberg-Marquardt back propagation training algorithm is employed for short-term nodal congestion price forecasting in different zones of a large-scale power market. The use of genetic algorithms for neural network training optimization has a remarkable effect on the accuracy of price forecasting in a large-scale power market. The necessary data for neural network training are obtained by solving optimal power flow equations that take into account all effective constraints at …