Long Term Load Forecasting and Recommendations for China Based on Support Vector Regression

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References

[1] L. Ghods, M. Kalantar and Ieee, “Methods for Long- Term Electric Load Demand Forecasting; A Comprehensive Investigation,” IEEE International Conference on Industrial Technology, Chengdu, 2008, pp. 855-858.

[2] H. M. Al-Hamadi and S. A. Soliman, “One Year Long- Term Electric Load Forecasting Based on Multiple Regression Models and Kalman Filtering Algorithm,” Engineering Intelligent Systems for Electrical Engineering and Communications, Vol. 14, 2006, pp. 79-88.

[3] H. M. Al-Hamadi and S. A. Soliman, “Long-Term/Mid- Term Electric Load Forecasting Based on Short-Term Correlation and Annual Growth,” Electric Power Systems Research, Vol. 74, No. 3, 2005, pp. 353-361.
doi:10.1016/j.epsr.2004.10.015

[4] L. D. Duan, D. X. Niu and Z. H. Gu, “Long and Medium Term Power Load Forecasting with Multi-Level Recursive Regression Analysis,” 2nd International Symposium on Intelligent Information Technology Application, Shanghai, 20-22 December 2008, pp. 514-518.
doi:10.1109/IITA.2008.397

[5] O. Carpinteiro, I. Lima, R. C. Leme, A. de Souza, E. M. Moreira and C. Pinheiro, “A Hierarchical Neural Model with Time Windows in Long-Term Electrical Load Forecasting,” Neural Computing & Applications, Vol. 16, No. 4-5, 2007, pp. 465-470. doi:10.1007/s00521-006-0072-8

[6] O. Carpinteiro, R. C. Leme, A. de Souza, C. Pinheiro and E. M. Moreira, “Long-Term Load Forecasting via a Hierarchical Neural Model with Time Integrators,” Electric Power Systems Research, Vol. 77, No. 3-4, 2007, pp. 371-378. doi:10.1016/j.epsr.2006.03.014

[7] O. Carpinteiro, I. Lima, R. C. Leme, A. de Souza, E. M. Moreira and C. Pinheiro, “A Hybrid Neural Model in Long-Term Electrical Load Forecasting,” Artificial Neural Networks—ICANN 2006, Vol. 4132, 2006, pp. 717- 725.

[8] A. A. Abou El-Ela, A. A. El-Zeftawy, S. M. Allam and G. Atta, “Long-Term Load Forecasting and Economical Operation of Wind Farms for Egyptian Electrical Network,” Electric Power Systems Research, Vol. 79, No. 7, 2009, pp. 1032-1037. doi:10.1016/j.epsr.2009.01.003

[9] O. A. S. Carpinteiro, I. Lima, R. C. Leme, A. C. Z. de Souza, E. M. Moreira and C. A. M. Pinheiro, “A Hybrid Neural Model in Long-Term Electrical Load Forecasting,” 16th International Conference on Artificial Neural Networks (ICANN 2006), Athens, 2006, pp. 717-725.

[10] N. X. Jia, R. Yokoyama, Y. C. Zhou and Z. Y. Gao, “A Flexible Long-Term Load Forecasting Approach Based on New Dynamic Simulation Theory—GSIM,” International Journal of Electrical Power & Energy Systems, Vol. 23, No. 7, 2001, pp. 549-556.
doi:10.1016/S0142-0615(00)00078-8

[11] D. Niu, J. Li, J. Li and D. Liu, “Middle-Long Power Load Forecasting Based on Particle Swarm Optimization,” Computers and Mathematics with Applications, Vol. 57, No. 11-12, 2009, pp. 1883-1889.
doi:10.1016/j.camwa.2008.10.044

[12] Y. Lu, Z. Yao, X. Huifan and Z. Qing, “The Fuzzy Logic Clustering Neural Network Approach for Middle and Long Term Load Forecasting,” Grey Systems and Intelligent Services, 2007, pp. 963-967.

[13] M. M. Dalvand, S. B. Z. Azami, H. Tarimoradi and Ieee, “Long-Term Load Forecasting of Iranian Power Grid Using Fuzzy and Artificial Neural Networks,” 43rd International-Universities-Power-Engineering Conference, Padova, 2008, pp. 559-562.

[14] D. X. Niu, J. R. Jia, J. L. Lv, Y. Zhang and S. O. C. Ieee Computer, “Study on Intelligent Optimization Model Based on Grey Relational Grade in Long-Medium Term Power Load Rolling Forecasting,” 2nd International Conference on Risk Management and Engineering Management, Beijing, 2008, pp. 227-232.

[15] S. Yingling, Y. Hongsong, D. Yawei and P. Nansheng, “Research on Long Term Load Forecasting Based on Improved Genetic Neural Network,” Computational Intelligence and Industrial Application, 2008, pp. 80-84.

[16] L. M. Ao, Y. C. Wang and Q. Zhang, “Application of a Hybrid Model on Short-Term Load Forecasting Based on Support Vector Machines (SVM),” New Zealand Journal of Agricultural Research, Vol. 50, No. 5, 2007, pp. 567- 572. doi:10.1080/00288230709510324

[17] W. C. Hong, “Chaotic Particle Swarm Optimization Algorithm in a Support Vector Regression Electric Load Forecasting Model,” Energy Conversion and Management, Vol. 50, No. 1, 2009, pp. 105-117.
doi:10.1016/j.enconman.2008.08.031

[18] W. C. Hong, “Electric Load Forecasting by Support Vector Model,” Applied Mathematical Modelling, Vol. 33, No. 5, 2009, pp. 2444-2454.
doi:10.1016/j.apm.2008.07.010

[19] J. Shen, Y. Syau and E. S. Lee, “Support Vector Fuzzy Adaptive Network in Regression Analysis,” Computers & Mathematics with Applications, Vol. 54, No. 11-12, 2007, pp. 1353-1366. doi:10.1016/j.camwa.2007.03.006

[20] V. Vapnik, “The Nature of Statistical Learning Theory,” Springer, New York, 1995.