基于BIM和深度学习方法的精确能源建模和现有建筑物的绿色改造优化
Xiyang Ge1,2, Sharifah Akmam Syed Zakaria1, Chenyu Wang1,2
1School of Civil Engineering, Universiti Sains Malaysia, Penang, Malaysia.
PloS one
|December 12, 2025
概括
本研究介绍了一种使用变压器和图形神经网络 (GNN) 进行建筑能效预测的新型深度学习方法. 通过先进的数据集成和优化策略,该方法可将能源节约提高近4%.
科学领域:
- 可持续的城市发展
- 建筑的能源效率 建筑的能源效率
- 建筑中的人工智能
背景情况:
- 建筑行业对全球能源消耗和温室气体排放作出了重大贡献.
- 提高建筑能效对于实现节能和减排目标至关重要.
- 智慧城市倡议需要优化建筑能效,以实现可持续的城市增长.
研究的目的:
- 开发和验证一种新的深度学习方法,以改善建筑能效预测和优化.
- 将建筑信息建模 (BIM) 数据与先进的人工智能模型集成,以进行增强的空间和时间分析.
- 探索使用生成对抗网络 (GANs) 来优化绿色装修计划.
主要方法:
- 集成变压器模型和图形神经网络 (GNN) 进行能源效率预测.
- 使用建筑信息建模 (BIM) 数据来建模空间结构和能源消耗模式.
- 采用生成对抗网络 (GAN) 来产生各种绿色翻新策略.
主要成果:
- 拟议的综合深度学习方法与传统模型相比,显示出更高的性能.
- 实验验证显示,使用BIM数据,节能增加了近4%.
- 该方法有效地捕捉复杂的空间和时间关系,以准确预测.
结论:
- 与BIM数据集成的深度学习为有效的能源效率预测和优化提供了巨大的潜力.
- 开发的方法为智能城市的建筑设计和运营提供了有价值的决策支持.
- 这种方法促进了绿色建筑的实现,并促进了可持续的城市发展.
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