PFI-RP: Architectural design and intelligent control tools for decarbonizing space cooling and heating in buildings

该创新研究伙伴关系（PFI-RP）项目的更广泛影响/商业潜力将通过创建新工具来实现，以大幅提高建筑物的能源独立性，提高停电期间的热耐受性，以及减少能源需求和温室气体排放。空间加热和冷却是美国建筑物中最大的能源最终用途，导致气候变化。目前正在大力设计、建造、改造和运营新一代绿色建筑。绿色建筑行业是美国增长最快的市场之一。最近的研究表明，精心设计的，智能控制的“动态被动”系统，使用遮阳，自然通风和夜间隔热来吸收气候资源，如太阳能热，冷空气和寒冷的夜空，可以减少美国各地的空间加热和冷却负荷。这些动态被动系统有助于迫切需要的气体减排工作，并提供对热浪和寒流的恢复能力。虽然这些潜在的变革系统的价值得到了认可，但缺乏有效的工具来优化其设计和控制。因此，本项目将开发所需的工具，通过创建新的适销对路的设计软件、设计服务和动态无源系统的系统控制硬件。拟议的项目将解决动态无源系统设计和控制的复杂性，这些复杂性是由气候资源的多样性产生的，例如，太阳能加热，冷空气和寒冷的夜空，在美国不同地区。美国所有的气候都拥有广泛的资源，在不同的季节进行被动加热和冷却，为全国提供了宝贵的机会。同时，这些系统需要特定于气候的材料、配置和控制逻辑。第一个项目目标是开发一个实用的设计工具，用于指定系统组件。这一目标将通过将结构化、优先化的数值优化程序与现有的建筑能源建模程序相结合来实现。第二个目标是开发一个强大的，实用的控制工具，用于操作系统的动态元素。这一目标将通过使用深度强化学习方法来训练和控制气候，天气和占用响应代理来实现。由此产生的策略将用于设计，以通知配置优化，并在操作中，以提醒手动系统中的乘员或自动系统中的信号执行器。该项目将产生概念验证和原型。该奖项反映了NSF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。

The broader impact/commercial potential of this Partnerships for Innovation - Research Partnerships (PFI-RP) project will be realized through the creation of new tools to dramatically increase the energy independence of buildings, improve their thermal survivability during power outages, as well as reduce their energy demands and greenhouse gas emissions. Space heating and cooling comprise the single greatest energy end-use in US buildings, contributing to climate change. Vigorous efforts are now underway to design, build, retrofit, and operate a new generation of green buildings. The green building sector is one of the fastest-growing markets in the US. Recent work has shown that well-designed, intelligently controlled ‘dynamic passive’ systems, which use shading, natural ventilation, and night insulation to recruit climatic resources such as solar heat, cool air, and cold night skies, can reduce space heating and cooling loads extensively across US. These dynamic passive systems contribute to the urgently needed gas emission reduction effort and provide resilience to heatwaves and cold snaps. Although the value of these potentially transformative systems is recognized, effective tools for optimizing their design and control are lacking. This project will, therefore, develop the needed tools, by creating new marketable design software, design services, and system control hardware for dynamic passive systems.The proposed project will address the complexity of dynamic passive systems design and control generated by the diversity of climatic resources, e.g., solar heat, cool air, and cold night skies, in different US regions. All US climates possess extensive resources for passive heating and cooling in different seasons, presenting valuable opportunities nationally. At the same time, these systems require climate-specific materials, configurations, and control logics. The first project goal is to develop a practical design tool for specifying system components. This goal will be accomplished by integrating structured, prioritized numerical optimization procedures with existing building energy modeling programs. The second goal is to develop a robust, practical control tool for operating the systems’ dynamic elements. This goal will be accomplished by using deep reinforcement learning methods to train and control climate-, weather-, and occupant-responsive agents. The resulting policies will be used both in design, to inform configuration refinements, and in operation, to alert occupants in manual systems or to signal actuators in automatic systems. The project will generate proof-of-concept and prototypes. The prototypes will be evaluated as the tools’ abilities to reduce heating and cooling loads in simulations and testbeds.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.