基于强化学习的高效和可靠的强化建筑HVAC控制与异质的专家指导培训
Shichao Xu1, Yangyang Fu2, Yixuan Wang3
1Northwestern University, Mccormick School of Engineering, Evanston, 60208, USA. shichaoxu2023@u.northwestern.edu.
Scientific reports
|March 5, 2025
概括
这项研究加速了深度强化学习 (DRL) 用于建筑采暖,通风和空调 (HVAC) 控制. 新方法显著减少了培训时间,使先进的HVAC控制变得更加实用.
科学领域:
- 建筑能源系统 建筑能源系统
- 人工智能在控制工程中的应用
- 可持续的建筑技术 可持续的建筑技术
背景情况:
- 建筑HVAC系统消耗大量能源 (在美国近50%) 并影响居住者的福祉.
- 无模型深度强化学习 (DRL) 提供了传统的空调控制的替代方案,但面临着长时间培训的挑战.
研究的目的:
- 开发一个系统的方法来加速在线强化学习的HVAC控制.
- 利用领域专业知识来克服DRL在HVAC应用中的培训时间限制.
主要方法:
- 从抽象的物理模型和使用线下强化学习的历史数据学习专家功能.
- 将专家职能与以规则为基础的指导培训和政策初始化指南相结合.
- 实施一个运行时屏蔽框架,以尽量减少温度违规.
主要成果:
- 与以前的方法相比,在DRL训练中实现了8.8倍的加快速度.
- 证明了低温度违规率,确保乘客舒适.
- 成功地将专家知识整合到DRL培训过程中.
结论:
- 提出的系统方法有效地加速了DRL对HVAC控制的培训.
- 这种方法增强了DRL在节能建筑管理中的实际部署.
- 专家知识的整合是提高HVAC系统DRL效率和性能的关键.
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