CPS Medium: Collaborative Research: Physics-Informed Learning and Control of Passive and Hybrid Conditioning Systems in Buildings

这个数字物理系统(CPS)项目将开发先进的人工智能和机器学习(AI/ML)技术，以利用现有的大量未开发的气候资源，用于建筑中的直接太阳能加热、自然通风以及辐射和蒸发冷却。尽管这些用于建筑环境调节的机制俗称为“被动”，但它们的性能在很大程度上依赖于对可操作元素的智能控制，如窗户和遮阳板，以及混合系统中的风扇。为了实现这一目标，该项目将根据室内和室外环境的传感器测量、天气和能源预报、居住情况和居住偏好，为气候和居住者反应战略创造设计方法，以与现有建筑供暖通风和空调系统协调，智能地控制这些可操作的要素。该项目中开发的解决方案可能会大幅减少空间供暖、制冷和通风所产生的温室气体排放。开发的技术在经济适用房中可能特别有价值，因为它在正常情况下降低了能源成本，并在极端事件和停电时提高了被动生存能力。具体地说，该项目将创建智能被动和混合空调系统，以温和的室外空气和阳光的形式最佳地利用气候资源，在建筑围护结构中利用这些资源，并在建筑的微气候中重新分配它们，并学习如何应对不断变化的天气和不断变化的居住者需求。该项目将推进一类受当地能量和质量守恒定律管辖的系统的物理信息机器学习模型的基本分析和设计工具。这些所谓的局部交互双线性flow模型在本项目研究的特定物理建筑系统之外具有广泛的适用性。从基本的网络物理系统的角度来看，研究人员将为为这类系统设计的学习和控制算法建立分析证书，弥合纯粹数据驱动的战略和基于物理的模型之间的差距。最后，该项目将提供一个系统的机制，通过被动系统的智能操作来评估可用的气候资源，弥合目前认识上的一个关键差距。在被占领的建筑物中的演示将提供关键的见解和证据，以支持所研究的工具在现实世界中的适用性。这项工作还将开发和提供教育模块，以吸引初中生通过RPI工程大使计划鼓励在可持续工程领域从事职业；同时，项目成果还将通过俄勒冈大学可持续城市年计划支持社区参与科学技术。该奖项反映了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。

This Cyber-Physical Systems (CPS) project will develop advanced artificial intelligence and machine-learning (AI/ML) techniques to harness the extensive untapped climatic resources that exist for direct solar heating, natural ventilation, and radiative and evaporative cooling in buildings. Although these mechanisms for building environment conditioning are colloquially termed "passive," their performance depends strongly on the intelligent control of operable elements such as windows and shading, as well as fans in hybrid systems. Towards this goal, this project will create design methodologies for climate- and occupant-responsive strategies that control these operable elements intelligently in coordination with existing building heating ventilation and air conditioning systems, based on sensor measurements of the indoor and outdoor environments, weather and energy forecasts, occupancy, and occupant preferences. The solutions developed in this project can potentially result in substantial reduction in greenhouse gas emissions generated from space heating, cooling, and ventilation. The developed techniques may be particularly valuable in affordable housing by reducing energy costs under normal conditions and improving passive survivability during extreme events and power outages.Specifically, this project will create intelligent passive and hybrid conditioning systems that optimally leverage climatic resources in the form of temperate outdoor air and sunlight, harness these resources at the building envelope and redistribute them within the building’s microclimates, and learn to respond to changing weather and evolving occupant needs. The project will advance foundational analysis and design tools for a class of physics-informed machine learning models for systems governed by local energy and mass conservation laws. These so-called locally interactive bilinear flow models have broad applicability beyond the specific physical building systems studied in this project. From a fundamental cyber physical systems standpoint, the researchers will establish analytical certificates for learning and control algorithms designed for this class of systems, bridging the gap between purely data-driven strategies and physics-based models. Finally, the project will provide a systematic mechanism to evaluate climate resources available through the intelligent operation of passive systems, bridging a key gap in current understanding. Demonstrations in occupied buildings will provide key insights and evidence to support the applicability of the researched tools in the real world. This effort will also develop and present educational modules to attract middle and high school students to encourage careers in sustainable engineering through the RPI Engineering Ambassadors program; at the same time, project outcomes will also support community engagement with science and technology through the University of Oregon Sustainable City Year program.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.