Front. Psychiatry

Sec. Computational Psychiatry

Volume 15 - 2024 | doi: 10.3389/fpsyt.2024.1418969

This article is part of the Research Topic Deep Learning for High-Dimensional Sense, Non-Linear Signal Processing and Intelligent Diagnosis View all 5 articles

Provisionally accepted

Yubo Wang * Chengfeng Rao Qinghua Cheng Jiahao Yang Northeastern University, Shenyang, China

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The energy management and optimization of commercial buildings is of great significance for improving energy efficiency and reducing energy consumption. This paper presents a deep learning-based CGC-Net model designed to achieve accurate prediction and optimal control of building energy efficiency. The model combines convolutional neural network (CNN), gated cycle unit (GRU), and attention mechanism (CSA) through four different datasets (BDGP, CBECS, NEPB, and BEBDEE Dataset). The experimental results show that the CGC-Net model is better than the traditional methods and other models in terms of prediction accuracy and inference speed, showing high efficiency and stability. Moreover, ablation experiments and contrast experiments further demonstrate the advantages of this model. Overall, this study provides an innovative solution for the energy management of commercial buildings, aiming to promote the construction industry to achieve energy conservation and protection of the environment, while providing important technical support and theoretical guidance for improving building energy efficiency and reducing operating costs.

Keywords: deep learning, Patient Behavior Patterns, Health prediction, health monitoring, data analysis, cardiovascular disease

Received: 17 Apr 2024; Accepted: 05 Nov 2024.

Copyright: © 2024 Wang, Rao, Cheng and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Yubo Wang, Northeastern University, Shenyang, China

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