Short Time Price Forecasting for Electricity Market Based on Hybrid Fuzzy Wavelet Transform and Bacteria Foraging Algorithm
محورهای موضوعی : Machine learningkeyvan Borna 1 , Sepideh Palizdar 2
1 - Department of Computer Science, Faculty of Mathematics and Computer Science, Kharazmi University, Tehran, Iran
2 - Department of Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
کلید واژه: prediction , wavelet transform , fuzzy logic , bacteria foraging algorithm , electricity market,
چکیده مقاله :
Predicting the price of electricity is very important because electricity can not be stored. To this end, parallel methods and adaptive regression have been used in the past. But because dependence on the ambient temperature, there was no good result. In this study, linear prediction methods and neural networks and fuzzy logic have been studied and emulated. An optimized fuzzy-wavelet prediction method is proposed to predict the price of electricity. In this method, in order to have a better prediction, the membership functions of the fuzzy regression along with the type of the wavelet transform filter have been optimized using the E.Coli Bacterial Foraging Optimization Algorithm. Then, to better compare this optimal method with other prediction methods including conventional linear prediction and neural network methods, they were analyzed with the same electricity price data. In fact, our fuzzy-wavelet method has a more desirable solution than previous methods. More precisely by choosing a suitable filter and a multiresolution processing method, the maximum error has improved by 13.6%, and the mean squared error has improved about 17.9%. In comparison with the fuzzy prediction method, our proposed method has a higher computational volume due to the use of wavelet transform as well as double use of fuzzy prediction. Due to the large number of layers and neurons used in it, the neural network method has a much higher computational volume than our fuzzy-wavelet method.
Predicting the price of electricity is very important because electricity can not be stored. To this end, parallel methods and adaptive regression have been used in the past. But because dependence on the ambient temperature, there was no good result. In this study, linear prediction methods and neural networks and fuzzy logic have been studied and emulated. An optimized fuzzy-wavelet prediction method is proposed to predict the price of electricity. In this method, in order to have a better prediction, the membership functions of the fuzzy regression along with the type of the wavelet transform filter have been optimized using the E.Coli Bacterial Foraging Optimization Algorithm. Then, to better compare this optimal method with other prediction methods including conventional linear prediction and neural network methods, they were analyzed with the same electricity price data. In fact, our fuzzy-wavelet method has a more desirable solution than previous methods. More precisely by choosing a suitable filter and a multiresolution processing method, the maximum error has improved by 13.6%, and the mean squared error has improved about 17.9%. In comparison with the fuzzy prediction method, our proposed method has a higher computational volume due to the use of wavelet transform as well as double use of fuzzy prediction. Due to the large number of layers and neurons used in it, the neural network method has a much higher computational volume than our fuzzy-wavelet method.
[1] International Energy Outlook 2005, Energy Information Administration; http://www.eia.doe.gov/iea.#
[2] Patel, M.R., ”Electricity price and solar power systems”, CRC Press LLC, 1999.#
[3] Global power source, Global electricity price energy council, http://www.gwec.net/.#
[4] Cota, A., ”A review on the young history of electricity price power short term prediction”, Journal on Renewable Energy Review, vol. 12, Issue 6, pp. 1725-1744, 2008.#
[5] Sideratos, G. Hatziargyriou, N.D. “An Advanced Statistical Method for Electricity price Power Forecasting”, IEEE Transaction on power systems, Nat. Tech. Univ. of Athens , vol. 22, Issue 1, pp. 258-265, Feb 2007.#
[6] Hui, L., Hong-Qi, T., Chao, C., Yan-fei, L. “A Hybrid Statistical to Predict Electricity price Speed and Electricity price Power”, Renewable energy, Science direct, December 2009.#
[7] Monfared, M., Rastegar, H., Kojabadi, H. M. “On Comparing Three Artificial Neural Networks for Electricity price Speed Forecasting”, Applied energy, Science direct, January 2010.#
[8] MATLAB, Mathematical Foundations of Multiple Linear Regressions, R2007a.#
[9] Sahin, A. D., Zekai, S., “First-order Markov chain approach to electricity price speed modeling”, Journal of wind Engineering and Industrial Aerodynamics 89 (2001) 263-269.#
[10] Shamshad, A., Bawadi, M.A., Wan Hussin, W.M.A., Majid, T.A., Sanusi, S.A.M., “First and second order Markov chain models for synthetic generation of electricity price speed time series”, Energy 30 (2005) 693-708.#
[11] Kennedy, J., and Eberhart, R., “Particle Swarm Optimization”, IEEE International Conference on Neural Networks, pp. 1942-1948, 1995.#
[12] M. Monfared, H. Rastegar , H. M. Kojabadi “A New Strategy for Electricity price Speed Forecasting Using Artificial Intelligent Methods”, Renewable energy, Science direct, Vol. 34, Issue 3, pp. 845-848, March 2009.#
[13] A. Motamedi, H. Zareipour, W.D. Rosehart, "Electricity Price and Demand Forecasting in Smart Grids", IEEE Transactions on Smart Grid, vol. 3, pp. 664-674, 2012.#
