Title :
An improved load forecasting method of warship based on GA-SVR
Author :
Li DongLiang ; Zhang Xiaofeng ; Qiao Mingzhong ; Cheng Gang
Author_Institution :
Inst. of Simulationmachine, Naval Univ. of Eng., Wuhan, China
Abstract :
An improved forecasting method base on genetic algorithm and support vector machine for warship short-term load forecasting was presented and tested. The new influencing factors of warship power load were used in modeling which is different with the land grid and civilian vessels grid. Theory of genetic algorithm and Support vector machine was discussed first, and the method of genetic algorithm was improved to have the ability of adaptive parameter optimization. and the method of support vector machine was improved by the adaptive GA optimizational method. then a new adaptive short-term load forecasting model was established by the adaptive GA-SVM method. finally Through simulation results show that the adaptive GA-SVM method is highly feasible to predict with high accuracy and high generalization capability.
Keywords :
genetic algorithms; load forecasting; marine power systems; military vehicles; naval engineering; parameter estimation; regression analysis; ships; support vector machines; GA-SVR; adaptive GA optimizational method; adaptive parameter optimization; civilian vessels grid; generalization capability; genetic algorithm; land grid; support vector machine; warship power load; warship short-term load forecasting method; Genetic algorithms; Load forecasting; Load modeling; Optimization; Predictive models; Support vector machines; Training; genetic algorithm; load forecasting; support vector machine;
Conference_Titel :
Transportation, Mechanical, and Electrical Engineering (TMEE), 2011 International Conference on
Conference_Location :
Changchun
Print_ISBN :
978-1-4577-1700-0
DOI :
10.1109/TMEE.2011.6199491