Application of Artificial Neural Network to Predict the Hourly Cooling Load of an Office Building
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.
Lei Shi Jin Wang
School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
国际会议
三亚
英文
528-530
2009-04-24（万方平台首次上网日期，不代表论文的发表时间）