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《Building Energy Efficiency》 2013-02
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Least Squares Support Vector Machine(LS-SVM) Applied in Predicting Air-conditioning Load

TANG Li,TANG Zhong-hua,JIN Jun-jie(Southwest University of Science and Technology,Mianyang 621010,Sichuan,China)  
A new algorithm for short-term air-conditioning load forecasting is expounded based on the Least Squares Support Vector Machine(LS-SVM) method,establishing a prediction model for air-conditioning load with the FORTRAN software.The LS-SVM prediction model is used for predicting air-conditioning load of an office building in summer months in Mianyang area.Simulated results show that the LS-SVM method enjoys better forecasting accuracy and speed,which proved to be feasible and practical.
【Fund】: 西南科技大学创新基金(12ycjj27);; 四川省科技厅重点科研项目(2010JY0165);; 绵阳市重点专项(09Y003-13);; 四川省教育厅重点科研项目(2003A112)
【CateGory Index】: TU831.2;TP18
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