SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting
AbstractAccurate electric load forecasting has become the most important issue in energy management; however, electric load demonstrates a seasonal/cyclic tendency from economic activities or the cyclic nature of climate. The applications of the support vector regression (SVR) model to deal with seasonal/cyclic electric load forecasting have not been widely explored. The purpose of this paper is to present a SVR model which combines the seasonal adjustment mechanism and a chaotic immune algorithm (namely SSVRCIA) to forecast monthly electric loads. Based on the operation procedure of the immune algorithm (IA), if the population diversity of an initial population cannot be maintained under selective pressure, then IA could only seek for the solutions in the narrow space and the solution is far from the global optimum (premature convergence). The proposed chaotic immune algorithm (CIA) based on the chaos optimization algorithm and IA, which diversifies the initial definition domain in stochastic optimization procedures, is used to overcome the premature local optimum issue in determining three parameters of a SVR model. A numerical example from an existing reference is used to elucidate the forecasting performance of the proposed SSVRCIA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the ARIMA and TF-ε-SVR-SA models, and therefore the SSVRCIA model is a promising alternative for electric load forecasting.
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Hong, W.-C.; Dong, Y.; Lai, C.-Y.; Chen, L.-Y.; Wei, S.-Y. SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting. Energies 2011, 4, 960-977.
Hong W-C, Dong Y, Lai C-Y, Chen L-Y, Wei S-Y. SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting. Energies. 2011; 4(6):960-977.Chicago/Turabian Style
Hong, Wei-Chiang; Dong, Yucheng; Lai, Chien-Yuan; Chen, Li-Yueh; Wei, Shih-Yung. 2011. "SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting." Energies 4, no. 6: 960-977.