DocumentCode :
3158586
Title :
Electric load forecasting by SVR with chaotic ant swarm optimization
Author :
Hong, Wei-Chiang ; Lai, Chien-Yuan ; Hung, Wei-Mou ; Dong, Yucheng
Author_Institution :
Dept. of Inf. Manage., Oriental Inst. of Technol., Taipei, Taiwan
fYear :
2010
fDate :
28-30 June 2010
Firstpage :
102
Lastpage :
107
Abstract :
Support vector regression (SVR) has revealed the strong potential in accurate electric load forecasting, particularly by employing effective evolutionary algorithms to determine suitable values of its three parameters. Based on previous research results, these employed evolutionary algorithms themselves also have drawbacks, such as premature convergence, slowly reaching the global optimal solution, and trapping into a local optimum in parameters determination of a SVR model. This paper presents a short-term electric load forecasting model which applies a novel algorithm, namely chaotic ant swarm optimization (CAS), to improve the forecasting performance by searching suitable parameters combination in a SVR forecasting model. The proposed CAS combines with the chaotic behavior of single ant and self-organization behavior of ant colony in the foraging process to overcome premature local optimum. The empirical results indicate that the SVR model with CAS (SVRCAS) results in better forecasting performance than the other methods, namely SVRCPSO (SVR with chaotic PSO), SVRCGA (SVR with chaotic GA), regression model, and ANN model.
Keywords :
evolutionary computation; load forecasting; neural nets; particle swarm optimisation; power engineering computing; regression analysis; support vector machines; ANN model; SVR; chaotic PSO; chaotic ant swarm optimization; electric load forecasting; evolutionary algorithms; global optimal solution; premature convergence; premature local optimum; regression model; self-organization behavior; support vector regression; Artificial neural networks; Atmospheric modeling; Chaos; Content addressable storage; Evolutionary computation; Load forecasting; Particle swarm optimization; Predictive models; Technology management; Weather forecasting; Chaotic ant swarm optimization (CAS); Electric load forecasting; Support vector regression (SVR);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cybernetics and Intelligent Systems (CIS), 2010 IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-6499-9
Type :
conf
DOI :
10.1109/ICCIS.2010.5518572
Filename :
5518572
Link To Document :
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