模块化神经网络,结合未来建筑能源建模的物理先验
1Department of Mechanical and Aerospace Engineering, Syracuse University, Syracuse, NY 13244, USA.
Patterns (New York, N.Y.)
|September 5, 2024
概括
我们开发了一个模块化神经网络 (ModNN),简化了建筑能源建模 (BEM). 这种可扩展的方法将物理定律集成到数据驱动模型中,使能效的能源管理和可持续的建筑设计成为可能.
科学领域:
- 建筑科学 建筑科学
- 人工智能的人工智能
- 可持续能源 可持续能源
背景情况:
- 传统的建筑能源建模 (BEM) 是复杂的,需要大量的数据,专业知识,以及每个建筑物的时间.
- 这种复杂性限制了BEM在城市规划和能源管理等大规模应用中的可扩展性.
研究的目的:
- 开发一种可扩展和高效的建筑能源建模方法.
- 通过将物理原理与数据驱动方法相结合,克服传统BEM的局限性.
主要方法:
- 开发了一个模块化神经网络 (ModNN),它包含物理先验,包括热平衡方程和物理一致的约束.
- 设计了一个数据驱动的模块化结构,使多个建筑应用程序的模型共享和继承成为可能.
主要成果:
- 在四个不同的应用中展示了ModNN的可扩展性和有效性:负载预测,室内环境建模,建筑装修和能源优化.
- ModNN方法显著减少了对广泛的个案校准和专家知识的需求.
结论:
- ModNN通过将物理见解与数据驱动的灵活性相结合,为大规模建筑能源建模提供了一个新的框架.
- 这有助于广泛采用先进的BEM用于能源管理,弹性改造和建筑与电网集成,为可持续的城市化做出贡献.
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