CAJ | 학술논문

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。
위강저건축능모영향인소간복잡상관성대모형성능적영향，건립료일충기우 KPCA-WLSSVM 적건축능모예측모형。이용핵주원분석（KPCA）대수입변량진행수거압축，소제변량지간적상관성，간화모형결구；진일보채용가권최소이승지지향량궤（WLSSVM）방법건립건축능모예측모형，동시결합일충신형혼돈입자군-모의퇴화혼합우화（CPSO-SA）산법대모형삼수진행우화，이제고모형적예측성능급범화능력。통과장 KPCA-WLSSVM 모형방법응용우모판공건축능모적예측중，병여 WLSSVM、LSSVM 급 RBFNN 모형상비，실험결과표명，KPCA-WLSSVM 모형방법능유효제고건축능모예측정도。
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.