Applying Principal Component Analysis and Weighted Support Vector Machine in Building Cooling Load Forecasting
In order to predict blended coals property accurately, a new kind of hybrid prediction model based on principal component analysis (PCA) and weighted support vector machine (WSVM) was established. PCA was used to transform the highdimensional and correlative influencing factors data to low-dimensional principal component subspace. These new features are then used as the inputs of WSVM to solve the load forecasting problem. The theoretical analysis and the simulation results show that PCA can efficiently extract the nonlinear feature of initial data. PCA-WSVM has powerful learning ability, good generalization ability and low dependency on sample data compared single SVR and PCA-SVM. It also indicates that the integration of PCA and WSVM forecast cooling load effectively and can be used in building cooling load prediction.
Building cooling prediction weighted support vector regression principal component analysis
Lv Jinhu Li Xuemei Ding Lixing Jiang Liangzhong
Institute of Built Environment and Control, Zhongkai University of Agriculture and Engineering, Guan Institute of Built Environment and Control, Zhongkai University of Agriculture and Engineering, Guan School of Mechanical & Automotive Engineering, South China University of Technology, Guangzhou, Chin
国际会议
成都
英文
434-437
2010-06-12（万方平台首次上网日期，不代表论文的发表时间）