会议专题

An improved ARX model for hourly cooling load prediction of office buildings in different climates

  An attempt was made to develop an improved autoregressive with exogenous(ARX)model for office buildings cooling load prediction in five major climates of China.The cooling load prediction methods can be arranged into three categories: regression analysis,energy simulation,and artificial intelligence.Among them,the regression analysis methods using regression models are much simple and practical for real applications.However,traditional regression models are often helpless to manage multiparameter dynamic changes,making it not accurate as the other two categories.Many of the existing cooling load prediction studies use piecewise linearization to manage nonlinearity.To improve the prediction accuracy of regression analysis methods,higher order and interaction terms are included in improved ARX based on traditional ARX model.The improved ARX model consists of eight variables,with eleven coefficients accessed at a time.For applications and evaluations,an office building in major cities within each climatic zone was selected as a representation.These cities were Harbin,Beijing,Nanjing,Kunming and Guangzhou respectively.The coefficient of determination R2 is greater than 0.9 in five cities.The prediction results show that the improved ARX model can adapt to different climatic conditions,including those nonlinearity cases.

Chengliang Fan Yundan Liao Yunfei Ding

School of Civil Engineering,Guangzhou University,Guangzhou 510006,China School of Civil Engineering,Guangzhou University,Guangzhou 510006,China;Guangdong Provincial Key Lab

国际会议

2018年建筑、航空与环境工程国际学术论坛-物联网(IFCAE-IOT 2018)2018 International Forum on Construction, Aviation and Environmental Engineering-Internet of Things(IFCAE-IOT 2018)

广州

英文

1-5

2018-05-11(万方平台首次上网日期,不代表论文的发表时间)