Forecasting the Electricity Price Emphasizing Prices Jumps by Using Combination of Neural and Fuzzy Network with Particle Swarm Optimization Algorithm

Document Type: Research Paper


Department of Economics, Kharazmi University, Tehran, Iran


After deregulation in electricity markets, lots of studies were conducted 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 objective of this paper is to predict the price of electricity in Iran’s electricity market by using a combination of fuzzy-neural network and particle swarm optimization (PSO). In this paper, past prices, past loads, working and nonworking days, day hours and effect of seasons in 2015 have been taken into account as the effective factors in forecasting mechanism. The combined model is more precise than other methods like ARIMA, neural network, neural-fuzzy network, and a combination of fuzzy-neural and genetic algorithm. In the following, the process of price fluctuations has been discussed for increasing effectiveness of bidding. Results of simulation revealed that price forecasting is much more precise with price process mechanism.


Main Subjects

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