Campus Building Energy Usage Analysis and Prediction:A SVR Approach based on Multi-scale RBF Kernels

摘要：

　　Building energy usage analysis and prediction has been very important to energy conservation and occupant comfort optimization.This paper focuses on hourly air conditioning energy usage for teaching buildings on campus.First,a detailed analysis is made on real world energy usage data from campus energy monitoring platform.Then an adaptive 24-hour ahead prediction model is proposed based on Support Vector Regression(SVR).We investigate the feasibility of a new kernel obtained by a linearly weighted combination of multiple radial basis functions(RBF).A problem-based particle swarm optimizer(PSO)is further implemented to facilitate model parameter estimation.The results of our experimental study indicate that the proposed model is a promising alternative in modeling hourly air conditioning energy performance for campus buildings.

关键词： Energy usage analysis and prediction Campus buildings Support vector regression Problem-based particle swarm optimizer

作者: Ya Huang Tun Lu Xianghua Ding Ning Gu

作者单位: School of Computer Science,Fudan University,Shanghai,China

会议类型: 国际会议

会议名称: The 9th International Conference on Pervasive Computing and Application(ICPCA 2014)(第九届全国普适计算学术会议、第九届全国人机交互联合学术会议)

会议地点: 南昌

会议语种:英文

页码: 1-12

在线出版日期: 2013-09-26（万方平台首次上网日期，不代表论文的发表时间）