Title :
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
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;
Conference_Titel :
Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on
Conference_Location :
Sanya, Hainan
Print_ISBN :
978-0-7695-3605-7
DOI :
10.1109/CSO.2009.145
