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

建筑物至关重要，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。总体而言，建筑物占美国一次能源使用量的40%，二氧化碳排放量和电力消耗量的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要为能源需求的高峰期负责。通过部署传感器、执行器和控制器使建筑变得更智能，这些设备共同充当建筑网络物理系统(CP)的主干，每年可以实现30%以上的节能，还可以显著平稳高峰需求。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之路受制于它们的异构性，这需要工程定制、特定地点的解决方案，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CPS解决方案，使非专业建筑经理能够部署特定于资产的智能控制策略。拟议解决方案的优势在于，即使没有任何人为干预，该方法也可以大规模应用。由此产生的软件解决方案是人工智能启用的建筑能源专家(AI-BEE)，它将在普渡大学高性能建筑中心使用模拟和实验进行演示。这项拟议的研究将在包括机器学习和控制在内的核心CPS领域产生基础性贡献，并将转化为其他应用领域，如大型能源系统(电网)、交通、民用基础设施和无人驾驶汽车。首先，建筑类型的分类正在制定中。其理念是，每座建筑的能源行为应该完全由机器可读格式的有限变量集来指定。其次，每个完整的建筑描述都与一组描述能源消耗的动力系统相关联。通过这种方式，非专家将能够指定建筑特征，并获得一组合理的动力系统，其中包括对建筑的描述。这组动力系统就是我们所说的与手边建筑相关的模型宇宙。第三，元强化学习被用来发现一种自我改进的控制算法，该算法适用于相关模型宇宙中的所有动力学模型。最后一步是将发现的算法部署到建筑中，让它进一步自我改进。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.