An Online LS-SVM prediction model of building space cooling load

被引：1

作者：

Cao, Shuanghua ^{[1
]}

Zhang, Jiangtao ^{[1
]}

Liu, Fang ^{[1
]}

Li, Minsi ^{[1
]}

机构：

[1] Univ Shanghai Sci & Technol, Sch Environm & Architecture, Shanghai, Peoples R China

来源：

PROGRESS IN POWER AND ELECTRICAL ENGINEERING, PTS 1 AND 2 | 2012年 / 354-355卷

关键词：

space cooling load; prediction; online Least Squares Support Vector Machine;

D O I：

10.4028/www.scientific.net/AMR.354-355.789

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Building space cooling load affected by lots of factors,was transient, multi-dimensional and highly interactive. A model of online least squares support vector machine (LS-SVM) was established to forecast the space cooling load of building, and correlation analysis was used to find out the main influencing factors, such as, dry-bulb temperature, solar irradiance, and so on. As an example, the hourly space cooling load of an office building in shanghai was investigated. The hourly space cooling load was firstly calculated by the simulation software of DEST, and then, an online LS-SVM model was presented to forecast the load. The simulation results showed that the online LS-SVM model was effective for space cooling load prediction.

引用

页码：789 / 793

页数：5