Forecasting price of electricity emphasizing prices jumps using combination of neural and fuzzy network with particle swarm optimization

After deregulation in electricity markets, huge amount of studies were done especially in designing new systems and energy pricing in order to improve efficiency of power systems and increase investors’ profit. Investment’s profit could be increased by better contracts and better price bidding for buying and selling energy in electricity market, as a consequence price forecasting is essential. The main goal of this paper is to predict price of electricity in Iran’s electricity market, using combined of fuzzy-neural network with Particle Swarm Optimization (PSO). In this paper, past prices, past loads, working and nonworking days, day hours and effect of seasons in 1394 have been taken into account as effective factors in forecasting mechanism. Combined model, is more precise in contrast to other methods like ARIMA,neural network, neural-fuzzy network and combination of fuzzy-neural with genetic algorithm. In following, process of price fluctuations has discussed for improving and increasing effectiveness of bidding. Results of simulation revealed that price forecasting is much more precise with price process mechanism.