Scholarlink

<p>The accurate prediction of building energy consumption provides technology and data support for the construction of intelligent building energy systems. Moreover, it is also a crucial means of responding to the national “Carbon Peaking and Carbon Neutrality Goals.” Traditional methods can yield poor results because they fail to consider the nonlinear, nonstationary, and multi-seasonal characteristics of the building energy consumption data. To overcome these limitations, this paper proposes an asymmetric energy consumption prediction approach based on the encoder–decoder architecture. The proposed approach employs the CEEMDAN algorithm for data preprocessing to enhance the reliability of building energy consumption data. Subsequently, the convolutional gated recurrent unit (Conv-GRU) model is utilized to extract high-dimensional features and capture nonlinear relationships from the input energy consumption data. Finally, by employing the GRU-Attention algorithm to assign feature weights, this approach enhances the accuracy of building energy consumption prediction. Experimental evaluations conducted on real datasets demonstrate the superiority of the proposed approach over the existing classic methods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Research on Building Energy Consumption Prediction Based on Hybrid GRU Neural Network

  • 期刊论文

  • 发表日期：

    2025年4月13日

查看全文
  • Zhiyuan Gao,
  • Xuewei Zhang,
  • Changsheng Wang,
  • Jianchun Xing,
  • Zhongkai Deng,
  • Tao Chen

摘要

The accurate prediction of building energy consumption provides technology and data support for the construction of intelligent building energy systems. Moreover, it is also a crucial means of responding to the national “Carbon Peaking and Carbon Neutrality Goals.” Traditional methods can yield poor results because they fail to consider the nonlinear, nonstationary, and multi-seasonal characteristics of the building energy consumption data. To overcome these limitations, this paper proposes an asymmetric energy consumption prediction approach based on the encoder–decoder architecture. The proposed approach employs the CEEMDAN algorithm for data preprocessing to enhance the reliability of building energy consumption data. Subsequently, the convolutional gated recurrent unit (Conv-GRU) model is utilized to extract high-dimensional features and capture nonlinear relationships from the input energy consumption data. Finally, by employing the GRU-Attention algorithm to assign feature weights, this approach enhances the accuracy of building energy consumption prediction. Experimental evaluations conducted on real datasets demonstrate the superiority of the proposed approach over the existing classic methods.