Annual Power Load Forecasting Using Support Vector Regression Machines: A Study on Guangdong Province of China 1985-2008
Load forecasting has always been the essential part of
an efficient power system operation and planning. A novel approach
based on support vector machines is proposed in this paper for annual
power load forecasting. Different kernel functions are selected to
construct a combinatorial algorithm. The performance of the new
model is evaluated with a real-world dataset, and compared with two
neural networks and some traditional forecasting techniques. The
results show that the proposed method exhibits superior performance.
[1] E. E. Elattar, J. Goulermas, and Q. H. Wu, "Electric load forecasting
based on locally weighted support vector regression," IEEE Transactions
on Systems, Man, and Cybernetics ÔÇö Part C: Applications And Reviews,
vol. 40, pp. 438-447, July 2010.
[2] Y. Zhang, Q. Zhou, C. X. Sun, S. L. Lei, Y. M. Liu, and Y. Song, "RBF
neural network and ANFIS-based short-term load forecasting approach in
real-time price environment," IEEE Transactions on Power Systems, vol.
23, pp. 853-858, August 2008.
[3] V. H. Hinojosa, and A. Hoese, "Short-term load forecasting using fuzzy
inductive reasoning and evolutionary algorithms," IEEE Transactions on
Power Systems, vol. 25, pp. 565-574, February 2010.
[4] Ng. Q. Thi, Tr. A. Dung, Ph. Th. Th. Binh, and Tr. Q. D. Khoa,
"Application wavelet for middle term demand forecasting," in Proc. 2005
IEEE/PES Transmission and Distribution Conference & Exhibition: Asia
and Pacific, 2005, pp. 1-6.
[5] A. Ghanbari A. Naghavi, S. F. Ghaderi, and M. Sabaghian, "Artificial
neural networks and regression approaches comparison for forecasting
Iran-s annual electricity load," in Proc. International Conference on
Power Engineering, Energy and Electrical Drives, 2009, pp. 675-679.
[6] P. Bunnoon, K. Chalermyanont, and C. Limsakul, "Mid term load
forecasting of the country using statistical methodology case study in
Thailand," in Proc. 2009 International Conference on Signal Processing
Systems, 2009, pp. 924-928.
[7] H. A. E. Salama, A. F. A. El-Gawad, S.M. Sakr, E. A. Mohamed, and H.
M. Mahmoud, "Applications on medium-term forecasting for loads and
energy scales by using artificial neural network," in Proc. 20th
International Conference on Electricity Distribution, June 2009.
[8] L. Pinto, and J. Szczupak, "Load forecast under uncertainty accounting
for the economic crisis impact," in Proc. 2009 IEEE Bucharest Power
Tech Conference, June 2009.
[9] H. M. Mahmoud, S. M. Elkhodary, S. El-Debeiky, M. Khafagy, and A. A.
Twijri, "Special features of energy forecast methodology in fast growing
countries," in Proc. 12th International Middle-East Power System
Conference, 2008, pp. 555-558.
[10] Z. Ji, P, Zhang, and Z. Zhao, "Application of wavelet neutral network and
rough set theory to forecast mid-long-term electric power load," in Proc.
2009 First International Workshop on Education Technology and
Computer Science, 2009, pp. 1104-1108.
[11] C. X. Li, and L. M. Meng, "Comprehensive model for mid-long term load
forecasting basing three-target quantities and RBFNN," in Proc. Fourth
International Conference on Natural Computation, 2008, pp. 486-491.
[12] M. A. Mamun, and K. Nagasaka, "Artificial neural networks applied to
long-term electricity demand forecasting," in Proc. the Fourth
International Conference on Hybrid Intelligent Systems, 2004.
[13] Y. Shi, H. Yang, Y. Ding, and N. Pang, "Research on long term load
forecasting based on improved genetic neural network," in Proc. 2008
IEEE Pacific-Asia Workshop on Computational Intelligence and
Industrial Application, 2008, pp. 80-84.
[14] A. Azadeh, S. F. Ghaderi, S. Tarverdian, and M. Saberi, "Integration of
artificial neural networks and genetic algorithm to predict electrical
energy consumption," Applied Mathematics and Computation, vol. 186,
pp. 1731-1741, 2007.
[15] Q. Ding, "Long-term load forecast using decision tree method," in Proc.
2006 IEEE Power System Conference and Exposition, 2006, pp.
1541-1543.
[16] D. Niu, B. Kou, and Y. Zhang, "Mid-long term load forecasting using
hidden markov model," in Proc. Third International Symposium on
Intelligent Information Technology Application, 2009, pp. 481-483.
[17] Q. Zhang, and T. Liu, "Research on the mid-long term electric load
forecasting based on fuzzy rules," in Proc. The 2nd IEEE International
Conference on Information Management and Engineering, 2010, pp.
461-463.
[18] V. N. Vapnik, "The nature of statistical learning theory," Springer, 1995.
[19] Z. Li, and M. Shen, "Analysis of dominant factors in Chinese power load
growth based on fuzzy association rules," in Proc. The 2nd International
Workshop on Intelligent Systems and Applications, May 2010, pp. 1-4.
[20] B. E. Bose, I. M. Guyon, and V. N. Vapnik. "A training algorithm for
optimal margin classifiers". In Proc. The 5th Annual ACM Workshop on
Computational Learning Theory, 1992.
[1] E. E. Elattar, J. Goulermas, and Q. H. Wu, "Electric load forecasting
based on locally weighted support vector regression," IEEE Transactions
on Systems, Man, and Cybernetics ÔÇö Part C: Applications And Reviews,
vol. 40, pp. 438-447, July 2010.
[2] Y. Zhang, Q. Zhou, C. X. Sun, S. L. Lei, Y. M. Liu, and Y. Song, "RBF
neural network and ANFIS-based short-term load forecasting approach in
real-time price environment," IEEE Transactions on Power Systems, vol.
23, pp. 853-858, August 2008.
[3] V. H. Hinojosa, and A. Hoese, "Short-term load forecasting using fuzzy
inductive reasoning and evolutionary algorithms," IEEE Transactions on
Power Systems, vol. 25, pp. 565-574, February 2010.
[4] Ng. Q. Thi, Tr. A. Dung, Ph. Th. Th. Binh, and Tr. Q. D. Khoa,
"Application wavelet for middle term demand forecasting," in Proc. 2005
IEEE/PES Transmission and Distribution Conference & Exhibition: Asia
and Pacific, 2005, pp. 1-6.
[5] A. Ghanbari A. Naghavi, S. F. Ghaderi, and M. Sabaghian, "Artificial
neural networks and regression approaches comparison for forecasting
Iran-s annual electricity load," in Proc. International Conference on
Power Engineering, Energy and Electrical Drives, 2009, pp. 675-679.
[6] P. Bunnoon, K. Chalermyanont, and C. Limsakul, "Mid term load
forecasting of the country using statistical methodology case study in
Thailand," in Proc. 2009 International Conference on Signal Processing
Systems, 2009, pp. 924-928.
[7] H. A. E. Salama, A. F. A. El-Gawad, S.M. Sakr, E. A. Mohamed, and H.
M. Mahmoud, "Applications on medium-term forecasting for loads and
energy scales by using artificial neural network," in Proc. 20th
International Conference on Electricity Distribution, June 2009.
[8] L. Pinto, and J. Szczupak, "Load forecast under uncertainty accounting
for the economic crisis impact," in Proc. 2009 IEEE Bucharest Power
Tech Conference, June 2009.
[9] H. M. Mahmoud, S. M. Elkhodary, S. El-Debeiky, M. Khafagy, and A. A.
Twijri, "Special features of energy forecast methodology in fast growing
countries," in Proc. 12th International Middle-East Power System
Conference, 2008, pp. 555-558.
[10] Z. Ji, P, Zhang, and Z. Zhao, "Application of wavelet neutral network and
rough set theory to forecast mid-long-term electric power load," in Proc.
2009 First International Workshop on Education Technology and
Computer Science, 2009, pp. 1104-1108.
[11] C. X. Li, and L. M. Meng, "Comprehensive model for mid-long term load
forecasting basing three-target quantities and RBFNN," in Proc. Fourth
International Conference on Natural Computation, 2008, pp. 486-491.
[12] M. A. Mamun, and K. Nagasaka, "Artificial neural networks applied to
long-term electricity demand forecasting," in Proc. the Fourth
International Conference on Hybrid Intelligent Systems, 2004.
[13] Y. Shi, H. Yang, Y. Ding, and N. Pang, "Research on long term load
forecasting based on improved genetic neural network," in Proc. 2008
IEEE Pacific-Asia Workshop on Computational Intelligence and
Industrial Application, 2008, pp. 80-84.
[14] A. Azadeh, S. F. Ghaderi, S. Tarverdian, and M. Saberi, "Integration of
artificial neural networks and genetic algorithm to predict electrical
energy consumption," Applied Mathematics and Computation, vol. 186,
pp. 1731-1741, 2007.
[15] Q. Ding, "Long-term load forecast using decision tree method," in Proc.
2006 IEEE Power System Conference and Exposition, 2006, pp.
1541-1543.
[16] D. Niu, B. Kou, and Y. Zhang, "Mid-long term load forecasting using
hidden markov model," in Proc. Third International Symposium on
Intelligent Information Technology Application, 2009, pp. 481-483.
[17] Q. Zhang, and T. Liu, "Research on the mid-long term electric load
forecasting based on fuzzy rules," in Proc. The 2nd IEEE International
Conference on Information Management and Engineering, 2010, pp.
461-463.
[18] V. N. Vapnik, "The nature of statistical learning theory," Springer, 1995.
[19] Z. Li, and M. Shen, "Analysis of dominant factors in Chinese power load
growth based on fuzzy association rules," in Proc. The 2nd International
Workshop on Intelligent Systems and Applications, May 2010, pp. 1-4.
[20] B. E. Bose, I. M. Guyon, and V. N. Vapnik. "A training algorithm for
optimal margin classifiers". In Proc. The 5th Annual ACM Workshop on
Computational Learning Theory, 1992.
@article{"International Journal of Electrical, Electronic and Communication Sciences:49655", author = "Zhiyong Li and Zhigang Chen and Chao Fu and Shipeng Zhang", title = "Annual Power Load Forecasting Using Support Vector Regression Machines: A Study on Guangdong Province of China 1985-2008", abstract = "Load forecasting has always been the essential part of
an efficient power system operation and planning. A novel approach
based on support vector machines is proposed in this paper for annual
power load forecasting. Different kernel functions are selected to
construct a combinatorial algorithm. The performance of the new
model is evaluated with a real-world dataset, and compared with two
neural networks and some traditional forecasting techniques. The
results show that the proposed method exhibits superior performance.", keywords = "combinatorial algorithm, data mining, load
forecasting, support vector machines", volume = "4", number = "11", pages = "1586-4", }
