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- W2369528355 abstract "Study power load forecasting.Load and impact factor have complex nonlinear relation,and the traditional forecasting method can not describe the change rule,which leads to low accuracy of prediction.In order to improve the accuracy of load forecasting,the paper proposed an electric power load forecast model based on genetic optimization of support vector machine.The nonlinear approximation capability of least squares support vector machine was used to describe the power load and influence factors in complex nonlinear relation,and the adaptive genetic algorithm was used to optimize the parameters of least squares support vector machines.A province's power load data of 1990 ~ 2008 year were used for simulation test.The results show that the support vector machine can improve the precision of load forecast,and the average forecasting error is less than other contrast models." @default.
- W2369528355 created "2016-06-24" @default.
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- W2369528355 date "2012-01-01" @default.
- W2369528355 modified "2023-09-23" @default.
- W2369528355 title "Application of Support Vector Machine Optimized by Genetic Algorithm in Electric load Prediction" @default.
- W2369528355 hasPublicationYear "2012" @default.
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