Research on neural network optimization algorithm for building energy consumption prediction

被引：8

作者：

Chen, Song ^{[1
,2
,3
]}

Ren, Ting-Ting ^{[1
]}

Wu, Zhong-Cheng ^{[1
]}

机构：

[1] Chinese Acad Sci, High Field Magnet Lab, Hefei, Anhui, Peoples R China

[2] Univ Sci & Technol China, Hefei, Anhui, Peoples R China

[3] Anhui Jianzhu Univ, Coll Mech & Elect Engn, Hefei, Anhui, Peoples R China

来源：

JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING | 2018年 / 18卷 / 03期

基金：

中国国家自然科学基金;

关键词：

BP neural network; optimization algorithm; energy consumption prediction;

D O I：

10.3233/JCM-180820

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Building energy consumption prediction per month is an important content of building energy consumption management and company's financial budget. BP neural network with parameter optimization, network optimized by mind evolutionary algorithm, network optimized by genetic algorithm, network optimized by particle swarm algorithm and network optimized by adaptive weight particle swarm algorithm are used to forecast the energy consumption. The optimal values of the learning rate and hidden layer node number are choosen. The characteristics of various kinds of optimization algorithm are compared. The neural network optimized by adaptive weight particle swarm algorithm is proved to be the most accurate in predicting energy consumption.

引用

页码：695 / 707

页数：13

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