A Short-Term Load Forecasting Model Based on LS-SVM Optimized by Dynamic Inertia Weight Particle Swarm Optimization Algorithm
Short-term load forecasting is very important for power system, how to improve the accuracy of load forecasting is the keystone people pay attention to. A combined model of least squares support vector machines optimized by an improved particle swarm. Optimization algorithm is proposed in this paper to do the short-term load forecasting. Least squares support vector machines (LS-SVM) are new kinds of support vector machines (SVM) which regress faster than the standard SVM, they are adopt to do the forecasting here, and an improved particle swarm optimization (PSO) algorithm is employed to optimize the parameters gamma and sigma of LS-SVM, the new PSO outperforms the standard PSO especially in the search of orientation because of the dynamic inertia weight. A real case is experimented with to test the performance of the model, the result shows that the proposed algorithm can reduce training error and testing error of LS-SVM model, so to improve the accuracy of load forecasting.
KeywordsDynamic inertia weight PSO LS-SVM Short-term load forecasting
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- 5.Li, Y.C., Fang, T.J., Yu, E.K.: Study of Support Vector Machines for Short-term Load Forecasting. In: Proceedings of the CSEE, vol. 23, pp. 55–59 (2003)Google Scholar
- 7.Zhao, D.F., Pang, W.C., Zhang, J.S., Wang, X.F.: Based on Bayesian Theory and Online Learning SVM for Short Term Load Forecasting. In: Proceedings of the CSEE, vol. 25, pp. 8–13 (2005)Google Scholar
- 9.Eberhart, R., Kennedy, J.: New Optimizer Using Particle Swarm Theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–73 (1995)Google Scholar
- 11.Habib, S.J., Al-kazemi, B.S.: Comparative Study between the Internal Behavior of GA and PSO through Problem-specific Distance Functions. In: Evolutionary Computation, The 2005 IEEE Congress, September 2-5, vol. 3, pp. 2190–2195 (2005)Google Scholar
- 12.Wang, Q.F., Wang, Z.J., Wang, S.T.: A Modified Particle Swarm Optimizer Using Dynamic Inertia Weight. China Mechanical Engineering 16, 945–948 (2005)Google Scholar