RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment

IEEE Transactions on Power Systems

Volumn 23, Issue 3, 2008, Pages 853-858

  Yun, Zhang ^a   Quan, Zhou ^a   Caixin, Sun ^a   Shaolan, Lei ^b   Yuming, Liu ^b   Yang, Song ^c  

^a CHONGQING UNIVERSITY   (China)

^b CHONGQING COMMUNICATION INSTITUTE   (China)

^c GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Adaptive neural fuzzy inference system; Power system; Radial basis function neural network; Real time price; Short term load forecasting

Indexed keywords

FEEDFORWARD NEURAL NETWORKS; FORECASTING; FUZZY INFERENCE; FUZZY SYSTEMS; IMAGE CLASSIFICATION; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS;

ADAPTIVE NEURAL FUZZY INFERENCE SYSTEM; ELECTRIC ENERGIES; ELECTRICITY MARKETS; ELECTRICITY PRICING; FORECASTING ACCURACY; FORECASTING METHODS; LOAD FORECASTING; POWER SYSTEM; RADIAL BASIS FUNCTION NEURAL NETWORK; RADIAL BASIS FUNCTION NEURAL NETWORKS; RBF NETWORKS; RBF NEURAL NETWORK; REAL-TIME PRICE; SHORT-TERM LOAD FORECASTING; SYSTEM INTEGRATION;

ELECTRIC LOAD FORECASTING;

EID: 49249109272     PISSN: 08858950     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPWRS.2008.922249     Document Type: Article

References (17)

1. P. A. Mastorocostas, J. B. Theocharis, and A. G. Bakirtzis, "Fuzzy modeling for short-term load forecasting using the orthogonal least squares method," IEEE Trans. Power Syst., vol. 14, no. 1, pp. 29-36, Feb. 1999.
2. H. S. Hippert, C. E. Pedreira, and R. C. Souza, "Neural networks for short-term load forecasting: A review and evaluation," IEEE Trans. Power Syst., vol. 16, no. 1, pp. 44-55, Feb. 2001.
3. S.-J. Huang and K.-R. Shih, "Short-term load forecasting via ARMA model identification including non-Gaussian process considerations," IEEE Trans. Power Syst., vol. 18, no. 2, pp. 673-679, May 2003.
4. A. J. R. Reis and A. P. A. da Silva, "Feature extraction via multi-resolution analysis for short-term load forecasting," IEEE Trans. Power Syst., vol. 20, no. 1, pp. 189-198, Feb. 2005.
5. S. Fan and L. Chen, "Short-term load forecasting based on adaptive hybrid method," IEEE Trans. Power Syst., vol. 21, no. 1, pp. 392-401, Feb. 2006.
6. K. Alireza, Z. Enwang, and H. Elragal, "A neuro-fuzzy approach to short-term forecasting in a price-sensitive environment," IEEE Trans. Power Syst., vol. 17, no. 4, pp. 1273-1282, Nov. 2002.
7. M. Widjaja and W. Mielczarski, "A fuzzy-based approach to analyze system demand in the Australian electricity market," in Proc. IEEE Power Eng. Soc. Summer Meeting, Edmonton, AB, Canada, 1999, vol. 2, pp. 918-923.
8. V. Lyer, C. Chefung, and T. Gedeon, "A fuzzy-neural approach to electricity load and spot-price forecasting in a deregulated electricity market," in Proc. IEEE Region 10 Conf. Convergent Technology for the Asian-Pacific, Indian, 2003, vol. 4, pp. 1479-1482.
9. A. D. Papalexopoulos and T. C. Hesterberg, "A regression-based approach to short-term system load forecasting," IEEE Trans. Power Syst., vol. 5, no. 4, pp. 1535-1544, Nov. 1990.
10. M. T. Hagan and S. M. Behr, "The time series approach to short-term load forecasting," IEEE Trans. Power Syst., vol. 2, no. 3, pp. 785-791, Aug. 1987.
11. S. P. Michanos, A. C. Tsakoumis, P. Fessas, S. S. Vladov, and V. M. Mladenov, "Short-term load forecasting using a chaotic time series," in Proc. Int. Symp. Signals, Circuits and Systems, SCS, Jul. 10-11, 2003, vol. 2, pp. 437-440.
12. Q. C. Lu, W. M. Grady, M. M. Crawford, and G. M. Anderson, "An adaptive nonlinear predictor with orthogonal escalator structure for short- term load forecasting," IEEE Trans. Power Syst., vol. 4, no. 1, pp.158-165, Feb. 1989.
13. S. Rahmm and R. Bhamagar, "An expert system based algorithm for short-term load forecast," IEEE Trans. Power Syst., vol. 3, no. 2, pp. 392-399, May 1988.
14. A. Chen Hong, C. Claudio, and A. Singh, "ANN-based short-term load forecasting in electricity markets," in Proc. IEEE Power Eng. Soc. Winter Meeting, 2001, vol. 2, pp. 411-415.
15. C. S. Chang and Y. Minjun, "Real-time pricing related short-term load forecasting," in Proc. Int. Conf. Energy Management and Power Delivery, Raffles, Singapore, 1998, vol. 2, pp. 411-416.
16. J. S. R. Jang, "ANFIS: Adaptive-network-based fuzzy inference system," IEEE Trans. Syst., Man, Cybern., vol. 23, no. 3, pp. 665-685, May/Jun. 1993.
17. [Online]. Available: http://www.nemmco.com.au/data/market_data.htm.