基于KPCA-WLSSVM的公共建筑能耗预测

Prediction for Energy Consumption of Public Building Based on KPCA-WLSSVM

由于建筑能耗因子间存在非线性和高度冗余特性,传统预测方法很难消除数据之间冗余和捕捉非线性特征,导致预测精度较低。为了提高建筑能耗预测精度,建立了一种基于KPCA-WLSSVM的建筑能耗预测模型。利用核主元分析(KPCA)对输入变量进行数据压缩,消除变量之间的相关性,简化模型结构;进一步采用加权最小二乘支持向量机(WLSSVM)方法建立建筑能耗预测模型,同时结合一种新型混沌粒子群-模拟退火混合优化(CPSO-SA)算法对模型参数进行优化,以提高模型的预测性能及泛化能力。通过将KPCA-WLSSVM模型方法应用于某公共建筑能耗的预测中,并与WLSSVM、LSSVM及RBFNN模型相比,实验结果表明KPCA-WLSSVM模型方法能有效提高建筑能耗预测精度。

There are highly redundant features in affecting factors of building energy consumption,and the traditional method has low preditive accuracy. In order to improve the accuracy of building energy consumption forecasting,A prediction model based on KPCA-WLSSVM is proposed to forecast building energy consumption. The kernel principal component analysis（ KPCA） method could not only solve the linear correlation of the input and compress data but also simply the model structure. A novel hybrid chaos particle swarm optimization simulated annealing（ CPSO-SA）algorithm is applied to optimize the WLSSVM parameters to improve the learning performance and generalization ability of the model. Furthermore,the KPCA-WLSSVM model is applied to the energy consumption prediction for an office building,and the simulation results show that the KPCA-WLSSVM has better accuracy compared with WLSSVM model,LSSVM model and RBF neural network model,which is considered that the KPCA-WLSSVM is effective for building energy consumption prediction.