通过使用深度学习来评估用相变材料增强的Trombe墙的性能
Adam Krechowicz1, Maria Krechowicz2, Joanna Krasoń3
1Faculty of Electrical Engineering, Automatic Control and Computer Science, Kielce University of Technology, Al. 1000-lecia Państwa Polskiego 7, Kielce, 25-314, Poland.
Scientific reports
|November 29, 2025
概括
一个新的深度学习模型准确地预测了相变材料 (PCM) 增强的Trombe壁 (TWPCM) 的热效率. 这种先进的模型减少了测试时间和成本,同时改善了建筑物的能源管理.
科学领域:
- 建筑科学与工程 建筑科学与工程
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 对建筑物外进行准确的热效率评估对于能源管理至关重要.
- 阶段变化材料 (PCM) 增强热储墙 (Trombe墙),但它们的性能预测是复杂的.
- 不准确的估计导致资源浪费和财务损失.
研究的目的:
- 开发一种可靠的深度学习模型,用于预测用相变材料 (TWPCM) 修改的Trombe壁的热效率.
- 将拟议模型的性能与其他七种机器学习模型进行比较.
- 通过使用真实世界的能源性能数据来验证模型的有效性.
主要方法:
- 开发了一个混合卷积神经网络 (CNN) 和长短期记忆 (LSTM) 深度学习模型 (CNN+LSTM).
- 该模型使用了8个输入变量,包括气象数据和时间,回顾时间为240小时.
- 该模型架构包括4个LSTM层和5个CNN层,在真实数据上进行训练和验证.
主要成果:
- 在CNN+LSTM模型实现了卓越的性能,确定系数为0.99891.1.
- 它显示了最低的平均绝对误差 (0.19188 W/m2) 和根平均平方误差 (0.26324 W/m2).
- 该模型有效地预测了TWPCM的热行为,显示出出色的概括能力.
结论:
- 拟议的深度学习模型准确地预测TWPCM的热行为,考虑到储热能力.
- 它提供了强大的概括能力,并使性能与其他建筑包裹进行比较.
- 该模型显著减少了直接测试时间和相关成本,有助于高效的能源管理.
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