基于机器学习的模拟空调在混凝土结构中的运行时间,贝叶斯值.
Changhwan Jang1, Hong-Gi Kim2, Byeong-Hun Woo2
1Department of Smart Construction and Environmental Engineering, Daejin University, 1007 Hoguk-ro, Pocheon-si 11159, Republic of Korea.
Materials (Basel, Switzerland)
|May 11, 2024
概括
混凝土结构中的相变材料 (PCM) 显著降低了32%的能源消耗. 这一创新有助于碳中和努力和夏季能源需求预测.
科学领域:
- 材料科学 材料科学 材料科学
- 可持续建筑 可持续建筑
- 人工智能的人工智能
背景情况:
- 高效的能源使用对于实现碳中和目标至关重要.
- 目前正在研究将相变材料 (PCM) 集成到混凝土结构中.
- 减少建筑物的能源消耗是可持续发展的一个关键挑战.
研究的目的:
- 模拟和预测结合相变材料 (PCM) 的混凝土结构的能耗.
- 评估PCM增强混凝土在减少整体能源消耗方面的有效性.
- 开发建筑物能源消耗的准确预测模型.
主要方法:
- 使用来自混凝土模型结构的数据进行了能源消耗模拟.
- 在值调查中采用贝叶斯式方法.
- 通过整合模块化尖端神经网络组件来预测能源消耗,开发了一个循环神经网络 (RNN).
- 在R2值为0.95或更高的情况下,实现了高精度数据预测.
主要成果:
- 开发的RNN模型在预测能源消耗方面表现出很高的准确性,达到0.95+的R2值.
- 与普通混凝土相比,含有PCM的混凝土结构显著降低了能源消耗.
- 含PCM混凝土的能源消耗比传统混凝土低32%.
结论:
- 将相变材料 (PCM) 集成到混凝土结构中是一种可行的战略,可以大幅节省能源.
- 开发的AI驱动模拟方法为预测能源消耗提供了可靠的方法,特别是在夏季.
- 这项研究为减少建筑行业的碳足迹和提高建筑能效提供了一个有希望的解决方案.
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