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

CPS 媒介：协作研究：建筑物中被动和混合空调系统的物理信息学习和控制

• 批准号: 2241796
• 负责人: Alexandra Rempel
• 金额: $ 30万
• 依托单位: University of Oregon Eugene
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2241796&HistoricalAwards=false
• 关键词: CPS; Medium; Collaborative; Research; Physics

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.

该网络物理系统（CPS）项目将开发先进的人工智能和机器学习（AI/ML）技术，以利用建筑物中直接太阳能供暖，自然通风以及建筑物中的辐射和蒸发冷却的广泛未开发的民用资源。尽管这些用于构建环境条件的机制被称为“被动”，但它们的性能在很大程度上取决于对可操作元素（例如Windows和Shading）以及混合系统中的粉丝的智能控制。为了实现这一目标，该项目将根据室内和室外环境，天气和能源森林，居住者以及居住者的偏好，为气候和占用者响应性策略创建设计方法，以与现有的建筑物供暖通风和空调系统进行协调，以与现有建筑物供暖通风和空调系统进行协调，以与现有建筑物供暖通风和空调系统的协调智能地控制这些可操作的元素。该项目中开发的解决方案可能会导致空间加热，冷却和通风产生的温室气体排放大大减少。开发的技术在正常条件下降低能源成本并改善了极端事件和停电期间的被动生存能力，可能特别有价值。特别是，该项目将创建智能的被动和混合条件系统，以最佳的方式利用气候资源，以温度户外空气和太阳的需求，在建筑物内部和重新分配构建和重新分配式的驾驶和重新分配的范围，以使其在户外空气和范围内进行响应。该项目将推动针对由当地能源和大众保护法律管辖的系统的一类物理学的机器学习模型的基础分析和设计工具。这些所谓的本地互动双线性流模型在该项目中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，研究人员将建立用于为此类型系统设计的学习和控制算法的分析证书，弥合纯粹的数据驱动策略和基于物理的模型之间的差距。最后，该项目将提供一种系统的机制来评估通过被动系统的智能操作可用的气候资源，从而在当前理解中占据关键差距。占领建筑物中的演示将提供关键的见解和证据，以支持现实世界中研究工具的适用性。这项努力还将开发并提出教育模块，以吸引中学生，以通过RPI工程大使计划鼓励可持续工程的职业；同时，项目成果还将通过俄勒冈大学可持续城市年度计划支持社区与科学技术的参与。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准通过评估来诚实地诚实地支持我们的支持。