Annual electric load forecasting by a least squares support vector machine with a fruit fly optimization algorithm

Energies

Volumn 5, Issue 11, 2012, Pages 4430-4445

  Li, Hongze ^a   Guo, Sen ^a   Zhao, Huiru ^a   Su, Chenbo ^a   Wang, Bao ^a  

^a NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

Author keywords

Annual electric load forecasting; Fruit fly optimization algorithm (FOA); Least squares support vector machine (LSSVM); Optimization problem

Indexed keywords

ELECTRIC POWER PLANT LOADS; ELECTRIC POWER SYSTEM ECONOMICS; ELECTRIC POWER SYSTEMS; EVOLUTIONARY ALGORITHMS; FORECASTING; FRUITS; HEURISTIC ALGORITHMS; NEURAL NETWORKS; OPTIMAL SYSTEMS; OPTIMIZATION; REGRESSION ANALYSIS; SIMULATED ANNEALING; SUPPORT VECTOR MACHINES;

ANNUAL ELECTRICITY CONSUMPTION; ECONOMIC AND SOCIAL BENEFITS; FRUIT FLY OPTIMIZATION ALGORITHM (FOA); GENERALIZED REGRESSION NEURAL NETWORK(GRNN); LEAST SQUARES SUPPORT VECTOR MACHINES; META HEURISTIC ALGORITHM; OPTIMIZATION PROBLEMS; SIMULATED ANNEALING ALGORITHMS;

ELECTRIC LOAD FORECASTING;

EID: 84870745105     PISSN: None     EISSN: 19961073     Source Type: Journal    
DOI: 10.3390/en5114430     Document Type: Article

References (46)

1. Wang, J.J.; Li, L.; Niu, D.X.; Tan, Z.F. An annual load forecasting model based on support vector regression with differential evolution algorithm. Appl. Energy 2012, 94, 65-70.
2. Pappas, S.S.; Ekonomou, L.; Karamousantas, D.C.; Chatzarakis, G.E.; Katsikas, S.K.; Liatsis, P. Electricity demand loads modeling using auto regressive moving average (ARMA) models. Energy 2008, 33, 1353-1360.
3. Dong, R.J.; Pedrycz, W. A granular time series approach to long-term forecasting and trend forecasting. Phys. A Stat. Mech. Appl. 2008, 387, 3253-3270.
4. Pappas, S.S.; Ekonomou, L.; Karampelas, P.; Karamousantas, D.C.; Katsikas, S.K.; Chatzarakis, G.E.; Skafidas, P.D. Electricity demand load forecasting of the Hellenic power system using an ARMA model. Electr. Power Syst. Res. 2010, 80, 256-264.
5. Ai-Hamadi, H.M.; Soliman, S.A. Long-term/mid-term electric load forecasting based on short-term correlation and annual growth. Electr. Power Syst. Res. 2005, 74, 353-361.
6. Sorjamaa, A.; Hao, J.; Reyhani, N.; Ji, Y.N.; Lendasse, A. Methodology for long-term prediction of time series. Neurocomputing 2007, 70, 861-869.
7. Niu, D.X.; Li, J.C.; Li, J.Y.; Liu, D. Middle-long power load forecasting based on particle swarm optimization. Comput. Math. Appl. 2009, 57, 1883-1889.
8. Xia, C.H.; Wang, J.; McMenemy, K. Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks. Int. J. Electr. Power 2010, 32, 743-750.
9. Hsu, C.C.; Chen, C.Y. Regional load forecasting in Taiwan-Applications of artificial neural networks. Energy Convers. Manag. 2003, 44, 1941-1949.
10. Abou El-Ela, A.A.; El-Zeftawy, A.A.; Allam, S.M.; Atta, G.M. Long-term load forecasting and economical operation of wind farms for Egyptian electrical network. Electr. Power Syst. Res. 2009, 79, 1032-1037.
11. Meng, M.; Niu, D.X. Annual electricity consumption analysis and forecasting of China based on few observations methods. Energy Convers. Manag. 2011, 52, 953-957.
12. Chen, T. A collaborative fuzzy-neural approach for long-term load forecasting in Taiwan. Comput. Ind. Eng. 2012, 63, 66-70.
13. Kandil, M.S.; El-Debeiky, S.M.; Hasanien, N.E. The implementation of long-term forecasting strategies using a knowledge-based expert system: Part-II. Electr. Power Syst. Res. 2001, 58, 1-5.
14. Hong, W.C. Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm. Energy 2011, 36, 556-578.
15. Pai, P.F.; Hong, W.C. Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Convers. Manag. 2005, 46, 266-688.
16. Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn. 1995, 20, 273-297.
17. Van Gestel, T.; Suykens, J.A.K.; Baesens, B.; Viaene, S.; Vanthienen, J.; Dedene, G.; de Moor, B.; Vandewalle, J. Benchmarking least squares support vector machine classifiers. Mach. Learn. 2001, 54, 5-32.
18. Vong, C.M.; Wong, P.K.; Li, Y.P. Prediction of automotive engine power and torque using least squares support vector machines and Bayesian inference. Eng. Appl. Artif. Intel. 2006, 19, 277-287.
19. Yeganeha, B.; Motlagh, S.P.; M.; Rashidi, Y.; Kamalan, H. Prediction of CO concentrations based on a hybrid partial least square and support vector machine model. Atmos. Environ. 2012, 55, 357-365.
20. Yeganeha, B.; Motlagh, S.P.; M.; Rashidi, Y.; Kamalan, H. Prediction of CO concentrations based on a hybrid partial least square and support vector machine model. Atmos. Environ. 2012, 55, 357-365.
21. Zhao, X.H.; Wang, G.; Zhao, K.K.; Tan, D.J. On-line least squares support vector machine algorithm in gas prediction. Min. Sci. Technol. 2009, 19, 194-198.
22. Wu, Q. Hybrid model based on wavelet support vector machine and modified genetic algorithm penalizing Gaussian noises for power load forecasts. Expert Syst. Appl. 2011, 38, 379-385.
23. Espinoza, M.; Suykens, J.A.K.; de Moor, B. Fixed-size least squares support vector machines: A large Scale application in electrical load forecasting. Comput. Manag. Sci. 2006, 3, 113-129.
24. Espinoza, M.; Suykens, J.A.K.; Belmans, R.; de Moor, B. Electric load forecasting-Using kernel based modeling for nonlinear system identification. IEEE Control Syst. 2007, 27, 43-57.
25. Lin, K.P.; Pai, P.F.; Lu, Y.M.; Chang, P.T. Revenue forecasting using a least-squares support vector regression model in a fuzzy environment. Inf. Sci. 2011, 14, 196-209.
26. Kisi, O.; Cimen, M. Precipitation forecasting by using wavelet-support vector machine conjunction model. Eng. Appl. Artif. Intel. 2012, 25, 783-792.
27. Zhou, J.Y.; Shi, J.; Li, G. Fine tuning support vector machines for short-term wind speed forecasting. Energy Convers. Manag. 2011, 52, 1990-1998.
28. Wang, S.; Yu, L.; Tang, L.; Wang, S.Y. A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China. Energy 2011, 36, 6542-6554.
29. Dos Santosa, G.S.; Justi Luvizottob, L.G.; Marianib, V.C.; Dos Santos, L.C. Least squares support vector machines with tuning based on chaotic differential evolution approach applied to the identification of a thermal process. Expert Syst. Appl. 2012, 39, 4805-4812.
30. Sulaimana, M.H.; Mustafab, M.W.; Shareefc, H.; Abd-Khalid, S.N. An application of artificial bee colony algorithm with least squares supports vector machine for real and reactive power tracing in deregulated power system. Int. J. Electr. Power 2012, 37, 67-77.
31. Li, J.; Liu, J.P.; Wang, J.J. Mid-long term load forecasting based on simulated annealing and SVM algorithm. Proc. CSEE 2011, 31, 63-66.
32. Pan, W.T. A new fruit fly optimization algorithm: Taking the financial distress model as an example. Knowl. Based Syst. 2012, 26, 69-74.
33. Suykens, J.A.K.; Vandewalle, J. Least squares support vector machine classifiers. Neural Process Lett. 1999, 9, 293-300.
34. Suykens, J.A.K.; van Gestel, T.; de Brabanter, J.; de Moor, B.; Vandewalle, J. Least Squares Support Vector Machines; World Scientific: Singapore, 2002.
35. Keerthi, S.S.; Lin, C.J. Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput. 2003, 15, 1667-1689.
36. Shi, D.Y.; Lu, J.; Lu, L.J. A judge model of the impact of lane closure incident on individual vehicles on freeways based on RFID technology and FOA-GRNN method. J. Wuhan Univ. Technol. 2012, 34, 63-68.
37. Xu, Z.H.; Wang, F.L.; Sun, D.D.; Wang, J.Q. A forecast of export trades based on the FOA-RBF neural network [in Chinese]. Math. Pract. Theor. 2012, 42, 16-21.
38. Xiao, Z.A. Design of analog filter based on fruit fly optimization algorithm. J Hubei Univ. Educ. 2012, 29, 26-29.
39. China National Bureau of Statistics. China Energy Statistical Yearbook 2011; China Statistics Press: Beijing, China, 2011.
40. Electricity Power Supply and Demand of 2011 in China Released by China Electricity Council [in Chinese]. Available online: http: //tj.cec.org.cn/fenxiyuce/yunxingfenxi/gongxufenxiyuce/2012-02-03/79721.html (accessed on 1 September 2012).
41. De Souza, X.S.; Suykens, J.A.K.; Vandewalle, J.; Bolle, D. Coupled simulated annealing. IEEE Trans. Syst. Man Cybern. Part B Cybern. 2010, 40, 320-335.
42. Specht, D.F. A general regression neural network. IEEE Trans. Neural Netw. 1991, 2, 568-576.
43. Amiri, M.; Davande, H.; Sadeghian, A.; Chartier, S. Feedback associative memory based on a new hybrid model of generalized regression and self-feedback neural networks. Neural Netw. 2010, 23, 69-74.
44. LS-SVMlab, version 1.8; Department of Electrical Engineering (ESAT), Katholieke Universiteit Leuven: Leuven, Belgium, 2011. Available online: http://www.esat.kuleuven.be/sista/lssvmlab/(accessed on 1 September 2012).
45. De Brabanter, K.; Karsmakers, P.; Ojeda, F.; Alzate, C.; de Brabanter, J.; Pelckmans, K.; de Moor, B.; Vandewalle, J.; Suykens, J.A.K. LS-SVMlab Toolbox User's Guide Version 1.8; World Scientific: Singapore, 2002; pp. 10-146.
46. Niu, D.X.; Wang, Y.L.; Wu, D.D. Power load forecasting using support vector machine and ant colony optimization. Expert Syst. Appl. 2010, 37, 2531-2539.