基于 KPCA-WLSSVM 的建筑能耗预测模型
A prediction model for energy consumption of building based on KPCA-WLSSVM
赵超 1戴坤成 1王贵评1
作者信息
- 1. 福州大学 节能技术研究中心，福州 350108
- 折叠
摘要
为降低建筑能耗影响因素间复杂相关性对模型性能的影响，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 模型方法应用于某办公建筑能耗的预测中，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。
Abstract
The correlations among the building energy consumption factors can corrupt the prediction model’ s performance,and get undesirable results.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 simplify the model structure.A novel hybrid chaos particle swarm optimization simulated annealing (CPSO-SA)algorithm is applied to optimize WLSSVM parameters to improve 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 results show that the KPCA-WLSSVM has better accuracy compared with WLSSVM model,LSSVM model and RBF neural network model.and the KPCA-WLSSVM is effective for building energy consumption prediction.
关键词
建筑能耗/预测/核主元分析/支持向量机Key words
energy consumption of building/forecasting/kernel principal component analysis/support vector machines引用本文复制引用
基金项目
国家自然科学基金(6080402)
国家自然科学基金(61374133)
高校博士点专项科研基金(20133314120004)
出版年
2015
NETL
NSTL
万方数据