32 Papers
120 Citations
Li Xuemei is an academic researcher from Zhongkai University of Agriculture and Engineering. The author has contributed to research in topics: Cooling load & Support vector machine. The author has an hindex of 8, co-authored 29 publications.
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Papers
Building Cooling Load Forecasting Model Based on LS-SVM
Li Xuemei,Lu Jin-hu,Ding Lixing,Xu Gang,Li Jibin +4 more
- 18 Jul 2009
TL;DR: A new hourly cooling load prediction model and method based on Least Square Support Vector Machine (LS-SVM) is proposed and a comparison of the performance of LSSVM with back propagation neural network (BPNN) is carried out.
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A Novel Hybrid Approach of KPCA and SVM for Building Cooling Load Prediction
Li Xuemei,Ding Lixing,Lv Jinhu,Xu Gang,Li Jibin +4 more
- 09 Jan 2010
TL;DR: KPCA is an improved PCA, which possesses the property of extracting optimal features by adopting a nonlinear kernel function method, and has powerful learning ability, good generalization ability and low dependency on sample data compared with PCA-SVR and SVR.
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Building cooling load forecasting using fuzzy support vector machine and fuzzy C-mean clustering
Li Xuemei,Deng Yuyan,Ding Lixing,Jiang Liangzhong +3 more
- 12 Jun 2010
TL;DR: The results of experiment indicate that the proposed fuzzy C-mean clustering algorithm and fuzzy support vector machines method can be used as an attractive and effective means for short-term cooling load forecasting.
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Support vector regression and ant colony optimization for HVAC cooling load prediction
Ding Lixing,Lv Jinhu,Li Xuemei,Li Lanlan +3 more
- 05 May 2010
TL;DR: The results showed that the proposed approach, by comparing with back-propagation neural network model, was an efficient way to model building cooling load with good predictive accuracy and provide a useful tool for maximizing the combustion efficiency of cooling load.
28
Applying principal component analysis and weighted support vector machine in building cooling load forecasting
Lv Jinhu,Li Xuemei,Ding Lixing,Jiang Liangzhong +3 more
- 12 Jun 2010
TL;DR: The theoretical analysis and the simulation results show that PCA can efficiently extract the nonlinear feature of initial data and the integration of PCA and WSVM forecast cooling load effectively and can be used in building cooling load prediction.
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