Simulated annealing theory based particle swarm optimization for support vector machine model in short-term load forecasting

Proceedings - 2009 International Conference on Information Engineering and Computer Science, ICIECS 2009

Volumn , Issue , 2009, Pages

  Lu, Ning ^a,b   Zhou, Jianzhong ^a   He, Yaoyao ^a   Liu, Ying ^c  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b WUHAN UNIVERSITY OF TECHNOLOGY   (China)

^c CHINA THREE GORGES UNIVERSITY   (China)

Author keywords

Load forecasting; Paritcle swarm optimization; Simulated annealing; Support vector machine

Indexed keywords

FORECASTING ELECTRICITY; IMPROVED PSO; KEY PROBLEMS; LOAD FORECASTING; LOAD FORECASTING MODEL; NEW MODEL; NON-LINEAR REGRESSION; PARITCLE SWARM OPTIMIZATION; PARTICLE SWARM OPTIMIZATION ALGORITHM; PREMATURE CONVERGENCE; PSO ALGORITHMS; SEARCHING ABILITY; SHORT TERM LOAD FORECASTING; SIMULATION RESULT; STATISTICAL LEARNING THEORY; SVM MODEL; SWARM OPTIMIZATION;

ALGORITHMS; ANNEALING; COMPUTER SCIENCE; CONVERGENCE OF NUMERICAL METHODS; ELECTRIC LOAD FORECASTING; ELECTRIC POWER SYSTEMS; GLOBAL OPTIMIZATION; PARTICLE SWARM OPTIMIZATION (PSO); SPACECRAFT; SUPPORT VECTOR MACHINES; TIME SERIES; VECTORS;

SIMULATED ANNEALING;

EID: 77949586395     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIECS.2009.5366400     Document Type: Conference Paper

References (12)

1. R.J. Piers, K.Adamson, "Methodologies for load forecasting", Intelligent Systems, 2006 3rd International IEEE Conference , Sept. model is applied in short-term load forecasting of electric 2006, pp. 800-806
2. L.H. Ma, S.G.Zhou, M.Lin, "Support vector machine optimized with genetic algorithm for short-term load forecasting" Knowledge Acquisition and Modeling, 2008. KAM '08. International Symposium on 21-22 Dec. 2008 pp:654-657.
3. J. Kennedy and R. C. Eberhart, "A discrete binary version of the particle swarm algorithm," Proceedings of the World ' Proceedings of IEEE International Conference on Systemics, man and Cybernetics,12-15 Oct.1997, Vol. 5, pp:4104-4108.
4. J. Kennedy, R. Eberhart,"Swarm Intelligence," Morgan Kaufmann Publishers, Inc.,San Francisco,CA,2001.
5. J. Kennedy, R. Eberhart,, "Particle swarm optimization," Micro Machine and Human Science, 1995. MHS '95., Proceedings of the Proceedings of the Sixth International Symposium on 4-6 Oct. 1995 , pp.39-43.
6. D.S.Johnson, C.R Aragon, L.A. McGeoch, and C. Schevon, "Optimization using Simnlated Annealing,"An experimental evaluation, Operation Research, Vol. 37, pp. 865-892, 1982.
7. S.Kirkpatrick, C.D.Gelatt Jr., and M.P. Vecchi, "Optimization by Simulated Annealing, "Science, Vol. 220, pp. 671- 680, 1983..
8. S.Gao,L.Mei, "Combined forecasting model of electricity price based on support vector machine", Electric Power Automation Equipment,Vol 28(2008),pp.50-51.
9. V.N. Vapnik, S.E. Golowich, A.J. Smola (1996). "Support vector machine for function approximation, regression estimation and signal procession", Neural Information Procession System, Vol. 9, pp.281-287.
10. N. Cristianini, J. Shawe-Taylor, "An Introduction to Support Vector Machines, Cambridge University Press, Cambridge, UK, 2000.
11. V.N. Vapnik, " Statistical learning theory". New York: Wiley, 1998.
12. V.N. Vapnik (1995), "The nature of statistical learning theory",New York: Springer.