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Journal of Computers EIMEDLINEScopus

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篇名 Study of an Algorithm to Predict Future Building Cooling Load With Neural Networks
卷期 28:6
作者 Dong Wei 、 Kun Liu
頁次 257-268
關鍵字 air-conditioning system 、 Bayesian regularization algorithm 、 BP Neural Networks 、 load prediction 、 EI 、 MEDLINE 、 Scopus
出刊日期 201712
DOI 10.3966/199115992017122806023

中文摘要

英文摘要

The accurate cooling load prediction of an air conditioning system is the basis for energy saving optimization. To solve the problems of low accuracy of prediction, and most load predictions focusing on short-time prediction that causes reducing the practical significance, the application of improved BP neural networks prediction model is presented in this paper. Training and testing data for prediction model have been generated from DeST (Designer’s Simulation Toolkits) with climate data of Beijing. The generalization ability of the model has been strongest based on Bayesian regularization algorithm to train data. A case study shows that high accuracy is achieved by using the BPNN prediction model based on Bayesian regularization method with the prediction error of 1.18% in predicting the building load for longer time.

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