CPS: Medium: A meta-learning approach to enable autonomous buildings

CPS：中：一种支持自主建筑的元学习方法

• 批准号: 2038410
• 负责人: Panagiota Karava
• 金额: $ 99.46万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038410&HistoricalAwards=false
• 关键词: CPS; Medium; meta; learning; approach

Buildings are vitally important because they contribute to the well-being and productivity of their occupants - however, these benefits come at a high environmental cost. Collectively, buildings account for 40% of the US primary energy usage and CO2 emissions and 70% of the electricity consumption. Furthermore, buildings put a tremendous strain on the power grid as they are largely responsible for the peaks in energy demand. Making buildings smarter through the deployment of sensors, actuators, and controllers, which collectively serve as the backbone of building cyber-physical systems (CPS), can achieve more than 30% annual energy savings and can also significantly smooth peak demand. Thus, smart buildings are vital to a sustainable energy future. However, the road to large-scale realization of smart buildings is inhibited by their heterogeneity, which requires engineering customized, site-specific, and, thereby, costly solutions. The goal of this project is to develop a CPS solution for autonomous buildings that will enable non-expert building managers to deploy asset-specific, smart control policies. The advantage of the proposed solution relies on the fact that the approach can be applied on a large-scale even without any human intervention. The resulting software solution is the Artificial-Intelligence-Enabled Building Energy Expert (AI-BEE) and it will be demonstrated using simulations and experiments at the Center for High Performance Buildings at Purdue University. The proposed research will result in foundational contributions in core CPS areas, including machine learning and control, that will be translational to other application areas, such as large-scale energy systems (power grid), transportation, civil infrastructure, and unmanned vehicles.The technical details of our approach are as follows. First, a taxonomy of building types is being developed. The idea is that the energy behavior of every building should be completely specified by a finite set of variables in a machine-readable format. Second, each complete building description is associated with a set of dynamical systems that describes the energy consumption. In this way, non-experts will be able to specify building characteristics and get a set of plausible dynamical systems that include a description of the building. This set of dynamical systems is what is called the relevant model universe to the building at hand. Third, meta reinforcement learning is being used to discover a self-improving control algorithm that works well for all dynamical models in the relevant model universe. The final step is to deploy the discovered algorithm to the building and let it self-improve further.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

建筑至关重要，因为它们有助于居住者的福祉和生产力——然而，这些好处是以高昂的环境成本为代价的。总的来说，建筑占美国一次能源使用量和二氧化碳排放量的40%，占电力消耗的70%。此外，建筑给电网带来了巨大的压力，因为它们在很大程度上造成了能源需求的高峰。通过部署传感器、执行器和控制器（它们共同构成了建筑网络物理系统（CPS）的支柱），使建筑变得更加智能，每年可以节省30%以上的能源，还可以显著缓解高峰需求。因此，智能建筑对可持续能源的未来至关重要。然而，智能建筑的大规模实现受到其异质性的限制，这需要工程定制，特定于场地，因此需要昂贵的解决方案。该项目的目标是为自主建筑开发一种CPS解决方案，使非专业建筑管理人员能够部署特定于资产的智能控制策略。所提出的解决方案的优点在于，该方法可以在没有任何人为干预的情况下大规模应用。由此产生的软件解决方案是人工智能建筑能源专家（AI-BEE），它将在普渡大学高性能建筑中心通过模拟和实验进行演示。拟议的研究将导致核心CPS领域的基础贡献，包括机器学习和控制，这将转化为其他应用领域，如大规模能源系统（电网）、交通、民用基础设施和无人驾驶车辆。我们方法的技术细节如下。首先，建筑类型的分类正在发展。这个想法是，每座建筑的能源行为应该完全由一组有限的变量以机器可读的格式指定。其次，每个完整的建筑描述都与一组描述能耗的动态系统相关联。通过这种方式，非专家将能够指定建筑物的特征，并得到一组合理的动力系统，其中包括建筑物的描述。这组动力系统被称为与手边建筑相关的模型宇宙。第三，元强化学习被用于发现一种自我改进的控制算法，该算法适用于相关模型领域中的所有动态模型。最后一步是将发现的算法部署到建筑物中，并让它进一步自我改进。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Discrete-Time Switching System Analysis of Q-Learning

Q-Learning的离散时间切换系统分析

• DOI: 10.1137/22m1489976
• 发表时间: 2023
• 期刊: SIAM Journal on Control and Optimization
• 影响因子: 2.2
• 作者: Lee, Donghwan;Hu, Jianghai;He, Niao
• 通讯作者: He, Niao

Distributed Off-Policy Temporal Difference Learning Using Primal-Dual Method

• DOI: 10.1109/access.2022.3211395
• 发表时间: 2022
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Donghwan Lee;Do Wan Kim;Jianghai Hu

Optimal stabilizing rates of switched linear control systems under arbitrary known switchings

任意已知切换下切换线性控制系统的最优稳定率

• DOI: 10.1016/j.automatica.2023.111331
• 发表时间: 2024
• 期刊: Automatica
• 影响因子: 6.4
• 作者: Hu, Jianghai;Shen, Jinglai;Lee, Donghwan
• 通讯作者: Lee, Donghwan