Short-Term Forecasting and Application About Indoor Cooling Load Based on EDA-PSO-BP Algorithm

被引：0

作者：

Huang, ZhiWei ^{[1
]}

Yan, Li ^{[2
]}

Peng, XinYi ^{[3
]}

Tan, Jia ^{[4
]}

机构：

[1] South China Univ Technol, Architectural Design Inst, Guangzhou 510640, Guangdong, Peoples R China

[2] South China Univ Technol, Sch Foreign Language, Guangzhou 510640, Guangdong, Peoples R China

[3] South China Univ Technol, Sch Software, Guangzhou 510800, Guangdong, Peoples R China

[4] South China Univ Technol, Sch Comp, Guangzhou 510800, Guangdong, Peoples R China

来源：

Web Technologies and Applications: APWeb 2016 Workshops, WDMA, GAP, and SDMA | 2016年 / 9865卷

关键词：

PSO; BP neural network; Cooling load prediction; EDA;

D O I：

10.1007/978-3-319-45835-9_12

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In order to improve the precision of cooling load prediction, the authors of this essay proposes neural network model based on EDA-PSO-BP algorithm. We used PSO optimization algorithm combined with BP neural network to do cooling load prediction experiments of indoor sample data of a building. The results showed that compared with other three kinds of prediction algorithms, the error of this algorithm is minimum and its running speed is the fastest.

引用

页码：129 / 135

页数：7