Short-term Electricity Price Forecasting Using Optimal Tsk Fuzzy Systems
Saeid Eslahi Tatafi
- Department of Electrical Engineering, Sowmesara branch, Islamic Azad University, Sowmesara, Iran.
Gholam Ali Heydari
- Department of Mathematics, Shahid Bahonar University of Kerman, Kerman, Iran.
Ali Akbar Gharaveisi
- Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
Since all financial transactions in restructured power markets are based on electricity prices, it is necessary that the price of electric power be predicted precisely. Some particular features such as: nonlinearity, non-stationary behaviors, as well as volatility of electricity prices make such a prediction a very challenging task. In this paper, a new structure of TSK fuzzy systems is presented that provides high order TSK fuzzy systems from lower orders which have capability of modeling and forecasting chaotic time series. The method used for optimization of fuzzy systems is the Interior point method. Using this method for forecasting electricity price is useful because of its chaotic behavior. The results are compared with RBF neural network and TSK fuzzy system presents better results.
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Saeid Eslahi Tatafi, Gholam Ali Heydari, Ali Akbar Gharaveisi, Short-term Electricity Price Forecasting Using Optimal Tsk Fuzzy Systems, Journal of Mathematics and Computer Science, 13 (2014), no. 3, 238-246
Tatafi Saeid Eslahi, Heydari Gholam Ali, Gharaveisi Ali Akbar, Short-term Electricity Price Forecasting Using Optimal Tsk Fuzzy Systems. J Math Comput SCI-JM. (2014); 13(3):238-246
Tatafi, Saeid Eslahi, Heydari, Gholam Ali, Gharaveisi, Ali Akbar. "Short-term Electricity Price Forecasting Using Optimal Tsk Fuzzy Systems." Journal of Mathematics and Computer Science, 13, no. 3 (2014): 238-246
- TSK fuzzy systems
- Interior Point Method
- RBF neural network.
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