Electricity load forecasting based on adaptive quantum-behaved particle swarm optimization and support vector machines on global level

Proceedings of the 2008 International Symposium on Computational Intelligence and Design, ISCID 2008

Volumn 1, Issue , 2008, Pages 233-236

  Wang, Jingmin ^a   Liu, Zejian ^a   Lu, Pan ^a  

^a NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTRIC POWER; ELECTRICITY DEMANDS; ELECTRICITY LOADS; ELECTRONIC INDUSTRIES; EMPIRICAL RESULTS; FREE PARAMETERS; GLOBAL CONVERGENCE ABILITIES; GLOBAL LEVELS; LEARNING MACHINES; LOAD FORECASTING; NATIONAL POWER SYSTEMS; NON LINEARITIES; NON-LINEAR REGRESSIONS; NUMERICAL EXPERIMENTS; OPERATION TIME; QUANTUM-BEHAVED PARTICLE SWARM OPTIMIZATIONS; SHORT-TERM LOAD FORECASTING; STRATEGY MANAGEMENTS; SUPPORT VECTORS; SVM MODELS; VECTOR MACHINES;

ADAPTIVE ALGORITHMS; ARTIFICIAL INTELLIGENCE; CONVERGENCE OF NUMERICAL METHODS; ELECTRIC GENERATORS; ELECTRIC POWER TRANSMISSION NETWORKS; INTELLIGENT CONTROL; PARTICLE SWARM OPTIMIZATION (PSO); POWER TRANSMISSION; STRATEGIC PLANNING; SUPPORT VECTOR MACHINES; TIME SERIES;

ELECTRIC LOAD FORECASTING;

EID: 61349125581     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISCID.2008.31     Document Type: Conference Paper

References (16)

1. H. M. Zhang, Z. N. Wei, "A short-term load forecasting approach based on PSO support vector machine", RELAY, 2006,34(3):28-31.
2. Amjady, N., "Short-term hourly load forecasting using time-series modeling with peak load estimation capability", IEEE Transactions on Power Systems, 2001,16:498-505.
3. Shyh-Jier Huang and Kuang-Rong Shih, "Short-term load forecasting via ARMA model identification including non-Gaussian process considerations", IEEE Transactions on Power Systems,2003,18: 673-679.
4. H.M. Al-Hamadi and S.A. Soliman, "Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model", Electric Power Systems Research,2004,68: 47-59.
5. K.H. Kim, J.K. Park, K.J.Hwang and S.H. Kim, "Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert-systems", IEEE Transactions on Power Systems, 1995, 10:1534-1539.
6. B. Satish, K. S. Swarup, S. Srinivas, and A. H. Rao, "Effect of temperature on short term load forecasting using an integrated ANN", Electric Power Systems Research, 2004,72:95-101.
7. V. Vapnik,"The Nature of statistical Learning Theory", New York: Springer-Verlag, 1995.
8. L. J. Cao,Francis E. H. Tay, "Support Vector Machine With Adaptive Parameters in Financial Time Series Forecasting",IEEE ransactions Networks, 2003,14(6):1506-1518.
9. Y.Shan,W.B.Xu,J.Sun,"Training Support Vector Machines with Quantum-behaved Particle Swarms Optimization", Application Research of Computers, 2007,24(7):94-96.
10. J.Sun, W.B.Xu,B.Feng, "A Global Search Strategy of Quantum-Behaved Particle Swarm Optimization", Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, 2004,pp.111-116.
11. Y.Tan,Y.P.Yang,J.C.Zeng, "An Enhanced Hybrid Quadratic Particle Swarm Optimization",Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications,2006.
12. M. Clerc, "The Swarm and Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization",Proc.Congress on Evolutionary Computation 1999, 1999, pp.1951-1957.
13. Y. Shi and R. Eberhart, "Empirical study of particle swarm optimization",Proc. Of Congress on Evolutionary Computation, 1999,pp.945-1950.
14. L.D.Sun,J.Sun,W.B.Xu," Adaptive Quantum-behaved Particle Swarm Optimization on global leve1",Computer Engineering and Application, 2007,43(26):50-53.
15. P.F.Pai,W.C.Hong,"Support vector machines with simulated annealing algorithms in electricity load forecasting", Energy Conversion and Management, 2005,pp.2669-2688.
16. H.X.Wu,Z.J.Hou," A Short-Term Load Forecasting Approach based on Immune Support Vector Machines", Power System Technology, 2004,28(23):47-51.