Sajad Rezaei - Department of Electrical Engineering, College of Engineering, Kermanshah Branch, Islamic Azad.University, Kermanshah, Iran.
Hamdi Abdi - Department of Electrical Engineering, College of Engineering, Kermanshah Science and Research, Branch, Islamic Azad University, Kermanshah, Iran
Shahram Karimi - Department of Electrical Engineering, College of Engineering, Kermanshah Branch, Razi University, Kermanshah, Iran

The power industry was regarded as the beating heart of industry and a public service. This industry was also monopolized, while nowadays it has become competitive. In the present study, a new idea is utilized, which using the genetic algorithm, the optimized number of hidden layer neurons in a neural network is determined to forecast more accurately the electricity price in competitive market. The results of the simulation indicate that, by using the proposed algorithm, it is feasible to provide the electricity market players with more accurate information. Although use of the genetic algorithm results in the more accurate forecast of electricity price, it can also prolong the duration of calculations and consequently lengthen the time to forecast electricity price

Optimized Neural Network; GA; Price Forecasting; Hidden Layer Neuron