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Application of the hybrid neural network model for energy consumption prediction of office buildings

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成果类型:
期刊论文
作者:
Wang, Lize;Xie, Dong;Zhou, Lifeng;Zhang, Zixuan
通讯作者:
Xie, D
作者机构:
[Zhang, Zixuan; Xie, D; Xie, Dong; Zhou, Lifeng; Wang, Lize] Univ South China, Sch Civil Engn, Hengyang 421001, Peoples R China.
[Zhang, Zixuan; Xie, Dong; Zhou, Lifeng; Wang, Lize] Natl & Local Joint Engn Res Ctr Airborne Pollutant, Hengyang 421001, Peoples R China.
通讯机构:
[Xie, D ] U
Univ South China, Sch Civil Engn, Hengyang 421001, Peoples R China.
语种:
英文
关键词:
Convolution;Energy utilization;Forecasting;Recurrent neural networks;Attention mechanisms;Bi-directional;Bi-directional gated recurrent unit;Building energy;Building energy prediction;Convolution neural network;Energy prediction;Hybrid neural networks;Prediction accuracy;Residual connection;Office buildings
期刊:
Journal of Building Engineering
ISSN:
2352-7102
年:
2023
卷:
72
页码:
106503
基金类别:
The authors gratefully acknowledge the support of this research by the National Natural Science Foundation of China (grant numbers 12275122 and U1867221 ) and the Scientific research project of the education department of Hunan Province, China (grant number 19C1568 ).
机构署名:
本校为第一且通讯机构
院系归属:
土木工程学院
摘要:
Accurate building energy consumption prediction is crucial to the rational planning of building energy systems. The energy consumption of buildings is influenced by various elements and is characterized by non-linearity and non-stationarity. To fully tap the time series characteristics of building energy consumption and heighten the model's prediction accuracy, this paper proposes a hybrid neural network prediction model combining attention mechanism, Bidirectional Gate Recurrent Unit (BiGRU), Convolutional Neural Networks (CNN), and the residu...

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