Application of Artificial Neural Network to Predict the Hourly Cooling Load of an Office Building

Author

Shi, Lei ; Wang, Jin

Author_Institution

Sch. of Civil Eng., Beijing Jiaotong Univ., Beijing, China

Volume

1

fYear

2009

fDate

24-26 April 2009

Firstpage

528

Lastpage

530

Abstract

According to meteorological element data of test reference year (TRY), a dynamic simulation program calculates the hourly cooling loads of an office building from April to September. Then, a general Visual Basic program is developed based on the error back-propagation (BP) algorithm of artificial neural network (ANN). The network is trained and tested by the obtained data. The results are presented and discussed. The results show that the predicted data is in good harmony with the calculated data, which indicates artificial neural network is a novel and reliable method to predict cooling load.

Keywords

HVAC; Visual BASIC; backpropagation; building management systems; building simulation; neural nets; office environment; power engineering computing; HVAC; Visual Basic program; artificial neural network; data analysis; dynamic simulation program; error back-propagation algorithm; hourly cooling load; office building; test reference year; Artificial neural networks; Civil engineering; Cooling; Load modeling; Neurons; Predictive models; Temperature; Testing; Thermal loading; Weather forecasting; artificial neural network; cooling load prediction; test reference year; thermal energy engineering;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on

Conference_Location

Sanya, Hainan

Print_ISBN

978-0-7695-3605-7

Type

conf

DOI

10.1109/CSO.2009.145

Filename

5193752