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

这个网络物理系统（CPS）项目将开发先进的人工智能和机器学习（AI/ML）技术，以利用广泛的未开发的气候资源，用于建筑物中的直接太阳能加热，自然通风以及辐射和蒸发冷却。虽然这些建筑环境调节机制通俗地称为“被动”，但它们的性能在很大程度上取决于可操作元件的智能控制，例如窗户和遮阳，以及混合系统中的风扇。为了实现这一目标，该项目将创建气候和占用响应策略的设计方法，这些策略将根据室内和室外环境的传感器测量，天气和能源预测，占用和占用者偏好，与现有的建筑供暖通风和空调系统协调，智能地控制这些可操作的元素。该项目开发的解决方案可能会大幅减少空间加热，冷却和通风产生的温室气体排放。开发的技术可能在经济适用房中特别有价值，可以在正常条件下降低能源成本，并提高极端事件和停电期间的被动生存能力。具体而言，该项目将创建智能被动和混合空调系统，以最佳方式利用温和的室外空气和阳光形式的气候资源，在建筑物的围护结构中利用这些资源，并在建筑物的微气候中重新分配它们，并学会应对不断变化的天气和不断变化的居住者需求。该项目将推进基础分析和设计工具，用于一类物理信息机器学习模型，用于受局部能量和质量守恒定律约束的系统。这些所谓的局部交互式双线性湍流模型具有广泛的适用性，超出了本项目中研究的特定物理建筑系统。从基本的网络物理系统的角度来看，研究人员将建立为这类系统设计的学习和控制算法的分析证书，弥合纯数据驱动策略和基于物理模型之间的差距。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。在被占用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真实的世界中的适用性。这项工作还将开发和提出教育模块，以吸引初中和高中学生，鼓励通过RPI工程大使计划从事可持续工程事业;同时，本发明还公开了一种用于该方法的装置，项目成果还将通过俄勒冈州大学可持续城市年计划支持社区参与科学和技术。该奖项反映了NSF的法定使命，并通过评估被认为值得支持使用基金会的知识价值和更广泛的影响审查标准。

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.