Application of Improved PSO-BP Neural Network in Cold Load Forecasting of Mall Air-Conditioning

被引：6

作者：

Yu, JunQi ^{[1
]}

Jing, WenQiang ^{[1
]}

Zhao, AnJun ^{[1
]}

Ren, YanHuan ^{[1
]}

Zhou, Meng ^{[1
]}

机构：

[1] Xian Univ Architecture & Technol, Xian 710055, Shaanxi, Peoples R China

来源：

JOURNAL OF CONTROL SCIENCE AND ENGINEERING | 2019年 / 2019卷

基金：

北京市自然科学基金;

关键词：

PIPELINE; DECOMPOSITION;

D O I：

10.1155/2019/2428176

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A combination of JMP, PSO-BP neural network, and Markov chain which aims at the low correlation between input and output data and the error of prediction model in the PSO-BP neural network prediction model is proposed. First, the JMP data processing software is used to process the input data and eliminate the samples with low coupling degree. Then, obtaining the cooling load prediction results relies on the training from the PSO-BP neural network. Finally, the final prediction results will be generated by eliminating the random errors using the Markov chain. The results show that the combination of the prediction methods has higher prediction accuracy and conforms to the change rule of the cooling load in shopping malls. Besides, the combination fits the actual application requirements as well.

引用

页数：20

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