CAJ | 학술논문

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，提出一种将主成分分析（ principal component analysis， PCA）和径向基函数（radial basic function， RBF）神经网络相结合的建筑能耗预测方法（PCA－RBF）。利用PCA消除建筑能耗高维变量数据的相关性，并按累积贡献率提取主成分，将主成分作为RBF神经网络的输入进行训练学习。通过PCA避免了模型过多的输入导致的训练耗时长及预测精度较低的不足。通过将PCA－RBF模型方法应用于某办公建筑能耗的预测中，并与RBF神经网络及BP神经网络模型相比，实验结果表明PCA－RBF模型方法能有效提高建筑能耗预测精度。
유우건축능모인자간존재비선성화고도용여특성，전통예측방법흔난소제수거지간용여화포착비선성특정，도치예측정도교저。위료제고건축능모예측정도，제출일충장주성분분석（ principal component analysis， PCA）화경향기함수（radial basic function， RBF）신경망락상결합적건축능모예측방법（PCA－RBF）。이용PCA소제건축능모고유변량수거적상관성，병안루적공헌솔제취주성분，장주성분작위RBF신경망락적수입진행훈련학습。통과PCA피면료모형과다적수입도치적훈련모시장급예측정도교저적불족。통과장PCA－RBF모형방법응용우모판공건축능모적예측중，병여RBF신경망락급BP신경망락모형상비，실험결과표명PCA－RBF모형방법능유효제고건축능모예측정도。
There are highly redundant features in affecting factors of building energy consumption, and the traditional method has low predictive accuracy.In order to improve the accuracy of building energy consumption forecasting, a model method for energy consumption based on principal component analy-sis ( PCA) and radial basic function ( RBF) neural network is proposed, which combines the abilities of PCA to de-correlate the variables and reduce the dimensionality of the data with that of neural network to approximate any complex nonlinear function.The PCA-RBF model is applied to the ener-gy consumption prediction for an office building, and the simulated results show that the PCA-RBF has better accuracy compared with RBF neural network model and BP neural network model, which is considered that the PCA-RBF is effective for building energy consumption prediction.