Back
 EPE  Vol.4 No.5 , September 2012
Long Term Load Forecasting and Recommendations for China Based on Support Vector Regression
Abstract: Long-term load forecasting (LTLF) is a challenging task because of the complex relationships between load and factors affecting load. However, it is crucial for the economic growth of fast developing countries like China as the growth rate of gross domestic product (GDP) is expected to be 7.5%, according to China’s 11th Five-Year Plan (2006-2010). In this paper, LTLF with an economic factor, GDP, is implemented. A support vector regression (SVR) is applied as the training algorithm to obtain the nonlinear relationship between load and the economic factor GDP to improve the accuracy of forecasting.
Cite this paper: S. Ye, G. Zhu and Z. Xiao, "Long Term Load Forecasting and Recommendations for China Based on Support Vector Regression," Energy and Power Engineering, Vol. 4 No. 5, 2012, pp. 380-385. doi: 10.4236/epe.2012.45050.
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.

 
 
Top