Energy Consumption of a Building by using Long Short-Term Memory Network: A Forecasting Study

被引：6

作者：

Barzola-Monteses, Julio ^{[1
,2
]}

Espinoza-Andaluz, Mayken ^{[3
]}

Mite-Leon, Monica ^{[4
]}

Flores-Moran, Manuel ^{[4
]}

机构：

[1] Univ Guayaquil, Artificial Intelligence & Informat Technol Res Gr, Guayaquil, Ecuador

[2] Univ Granada, Escuela Tecn Super Ingn Informat & Telecomunicac, Dept Comp Sci & Artificial Intelligence, Granada, Spain

[3] Escuela Super Politecn Litoral, Fac Ingn Mecan & Ciencias Prod, Ctr Energias Renovables & Alternat, Guayaquil, Ecuador

[4] Univ Guayaquil, Fac Math & Phys Sci, Guayaquil, Ecuador

来源：

2020 39TH INTERNATIONAL CONFERENCE OF THE CHILEAN COMPUTER SCIENCE SOCIETY (SCCC) | 2020年

关键词：

energy efficiency building; electric load time series; long short-term memory; feed-forward neural networks;

D O I：

10.1109/sccc51225.2020.9281234

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Buildings have a dominant presence in energy consumption for the transition to clean energy. During 2017, construction and operation of buildings worldwide represented more than a third (36%) of final energy used and 40% of the emissions of carbon dioxide. Hence, there is great interest in reducing energy use in this sector, and energy efficiency in buildings to enhance energy performances is a suitable way. In this paper, black-box approaches based on artificial neural networks to predict the electric load of a selected educational building are proposed. The potential and robustness of long short-term memory (LSTM) applied to a dataset with a limited number of days of observations are analyzed. The results in our scenario showed that the LSTM surpasses in accuracy

引用

页数：6

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