基于人工智能的热桥分析方法
Marta Pomada1, Krzysztof Cpałka1, Piotr Lacki1
1Czestochowa University of Technology, 69 Dabrowskiego St., Czestochowa, 42-201, Poland.
Scientific reports
|August 25, 2025
概括
这项研究引入了人工智能 (AI) 方法,使用模糊系统 (FS) 来分析建筑物中的热桥. 人工智能模型准确地估计了热损失系数,减少了复杂传统分析的需要.
科学领域:
- 建筑科学
- 人工智能
- 能源效率问题
背景情况:
- 建筑物是全球能源消耗和二氧化碳排放的主要贡献者.
- 解决建筑能效问题对于缓解气候变化至关重要.
- 建筑包裹中的热桥,就像窗到墙的连接,代表了显著的热损失途径.
研究的目的:
- 提出和评估基于人工智能 (AI) 的方法来分析窗口与墙壁之间的热桥.
- 使用模糊系统 (FS) 作为估计线性传热系数 (Ψ) 的通用近似值.
- 展示人工智能方法对新案例的概括和预测 Ψ 值的能力.
主要方法:
- 使用TRISCO程序训练传统热桥分析数据的模糊系统的开发.
- 应用训练后的FS来估计线性传热系数 (Ψ).
- 在未见的数据上验证FS模型的性能和概括能力.
主要成果:
- 通过人工智能驱动的模糊系统在估计线性传热系数方面取得了卓越的表现.
- 经过训练的FS准确地预测了最初的训练数据集中不包括的窗到墙连接的 Ψ 值.
- 与其他人工智能模型相比,人工智能方法显示出更高的可解释性.
结论:
- 拟议的人工智能方法为建筑物中的热桥分析提供了一种高效准确的方法.
- 这种人工智能驱动的技术可以显著减少对耗时的传统计算和实验的依赖.
- 实施这种人工智能方法可以简化设计师选择最佳建筑解决方案的过程.
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