A forecasting method of air conditioning energy consumption based on extreme learning machine algorithm - Details

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Abstract：

This　paper　deals　with　the　issue　on　air　conditioning　energy　consumption　and　system　monitoring　of　different　data　in　building.　Various　environmental　parameters　inside　the　building　are　changed　in　real　time,　while　the　conventional　air　conditioning　energy　consumption　forecasting　with　the　load　simulation　software　cannot　adapt　to　these　variations.　Therefore,　the　air　conditioning　energy　consumption　forecasting　model　is　established　based　on　extreme　learning　machine　(ELM)　algorithm,　within　the　interior　environmental　parameters　of　the　building.　These　parameters　are　obtained　through　the　building　monitoring　system　which　takes　into　account　the　environmental　parameters,　number　of　people,　region　area　and　energy　consumption.　The　performance　and　effectiveness　of　the　proposed　forecasting　model　of　air　conditioning　energy　consumption　are　demonstrated　through　a　case　study　of　a　building　from　practical　engineering.　©　2017　IEEE.

Keyword：

Air conditioning Forecasting Knowledge acquisition Machine learning Energy conservation Buildings Monitoring Energy utilization Learning algorithms Computer software

Author Community：

[ 1 ] [Yang, Xu]School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing; 100083, China
[ 2 ] [Gao, Jingjing]School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing; 100083, China
[ 3 ] [Zhang, Lei]School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing; 100083, China
[ 4 ] [Li, Xiaoli]College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing; 100124, China
[ 5 ] [Gu, Liu]School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing; 100083, China
[ 6 ] [Cui, Jiarui]School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing; 100083, China
[ 7 ] [Tong, Chaonan]School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing; 100083, China

Reprint Author's Address：

[yang, xu]school of automation and electrical engineering, university of science and technology beijing, beijing; 100083, china

Email：

yangxu@ustb.edu.cn

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Source ：

Year： 2017

Page： 89-93

Language： English

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 5

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 2

Affiliated Colleges：

信息科学技术学院本学院/部未明确归属的数据

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