CPS: Medium: Collaborative Research: Building Information, Inhabitant, Interaction and Intelligent Integrated Modeling (BI5M)

CPS：中：协作研究：建筑信息、居民、交互和智能集成建模（BI5M）

基本信息

批准号：
1836995
负责人：
Rishee Jain
金额：
$ 28万
依托单位：
Stanford University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1836995&HistoricalAwards=false
关键词：
CPS Medium Collaborative Research Building

项目摘要

Each year the nation spends over $400 billion to power, heat and cool its buildings. Moreover, buildings are a major source of environmental emissions. As a result, even a modest improvement in energy efficiency of the nation's building stock would result in substantial economic and environmental benefits. In this project, the focus is on improving energy efficiency in commercial buildings because this sector represents a substantial portion of the energy usage and costs within the overall building sector. Enhancing the energy efficiency of commercial buildings is a challenging problem, due to the fact that centralized building systems -- such as heating, ventilation and air conditioning (HVAC), or lighting -- must be synthesized and integrated with individual inhabitant behavior and energy consumption patterns. This project aims to design, analyze, and test a cyber-physical and human-in-the-loop enabled control system that can drive sustained energy savings in commercial buildings. It brings together expertise in computational building science, eco-feedback, network theory, data science, and control systems to integrate physical building information and inhabitants with cyber (building-human) interaction models to enable intelligent control of commercial building systems. Specifically, this project will: 1) design an integrated cyber-physical system (CPS), called Building Information, Inhabitant, Interaction, Intelligent Integrated Modeling (BI5M), aimed at reducing energy usage in buildings; 2) assess the complex inter-relationships between and across physical building and inhabitant models, cyber building-human interaction and intelligent control models related to energy conservation behavior; and 3) empirically test and validate modules and the overall BI5M system at test-bed buildings on Stanford's campus and Google's office park.This research incorporates measurement (geospatial building data, energy use data), dynamics (inhabitant social networks), and control (enhanced user control of: plug-load devices, HVAC, lighting) into the BI5M system. The BI5M system is centered on a cyber Building Information Management (BIM) model of the building, and will encompass rigorous systems engineering that will explore relationships across the cyber-physical domains and develop new insights for how the scientific principles of cyber-physical systems can be used to influence the energy efficiency of commercial buildings through both occupant behavior and intelligent control. By integrating physical building information and inhabitants with cyber interaction modeling, the research aims to introduce an integrated human-in-the-loop control paradigm for commercial buildings. In addition to a testbed and validated CPS system for commercial buildings (BI5M), this project targets fundamental knowledge on: ontological components required to integrate dynamic data streams and control information into static building models; complex socio-spatial structures of inhabitants; insights into how building-human and human-human interactions impact inhabitant consumption behavior; and new control models that leverage input on the energy usage, spatial, social and behavior dynamics of inhabitants. The educational impacts of this project will extend to participants (students, faculty, Google employees in the test-bed buildings), as well as a broader student population through the integration of key insights from this work into courses/projects at all three collaborating universities (Stanford, Georgia Tech, and Columbia). The project team will also disseminate results to practitioners/policy-makers working in the building management space through an Outreach Workshop. Additionally, this project will broaden participation in computing fields through a diverse team and by partnering with the Girls Who Code nonprofit to integrate project data sets and tools into their activities.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.

该国每年花费超过 4000 亿美元用于建筑物的供电、供暖和制冷。此外，建筑物是环境排放的主要来源。因此，即使国家建筑能源效率的小幅提高也会带来巨大的经济和环境效益。该项目的重点是提高商业建筑的能源效率，因为该部门占整个建筑部门能源使用和成本的很大一部分。提高商业建筑的能源效率是一个具有挑战性的问题，因为集中式建筑系统（例如供暖、通风和空调 (HVAC) 或照明）必须与个人居民行为和能源消耗进行综合和整合模式。该项目旨在设计、分析和测试网络物理和人机交互控制系统，该系统可以推动商业建筑的持续节能。它汇集了计算建筑科学、生态反馈、网络理论、数据科学和控制系统方面的专业知识，将物理建筑信息和居民与网络（建筑-人类）交互模型相集成，从而实现商业建筑系统的智能控制。具体来说，该项目将：1）设计一个集成的信息物理系统（CPS），称为建筑信息、居民、交互、智能集成建模（BI5M），旨在减少建筑物的能源使用； 2）评估物理建筑和居住模型、网络建筑与人类交互以及与节能行为相关的智能控制模型之间复杂的相互关系； 3) 在斯坦福大学校园和谷歌办公园区的试验台建筑上对模块和整体 BI5M 系统进行实证测试和验证。这项研究结合了测量（地理空间建筑数据、能源使用数据）、动态（居民社交网络）和控制（增强了用户对以下各项的控制：将负载设备、HVAC、照明）插入 BI5M 系统。 BI5M 系统以建筑物的网络建筑信息管理 (BIM) 模型为中心，并将包含严格的系统工程，探索网络物理领域之间的关系，并为网络物理系统的科学原理如何能够发挥作用提供新的见解。通过居住者行为和智能控制来影响商业建筑的能源效率。通过将物理建筑信息和居民与网络交互建模相结合，该研究旨在为商业建筑引入集成的人机交互控制范例。除了用于商业建筑 (BI5M) 的测试平台和经过验证的 CPS 系统外，该项目还针对以下方面的基础知识：将动态数据流和控制信息集成到静态建筑模型中所需的本体组件；居民复杂的社会空间结构；深入了解建筑与人以及人与人的互动如何影响居民的消费行为；以及新的控制模型，利用对居民的能源使用、空间、社会和行为动态的输入。该项目的教育影响将延伸到参与者（测试台大楼中的学生、教师、谷歌员工），以及通过将这项工作的关键见解整合到所有三所合作大学的课程/项目中，扩大到更广泛的学生群体（斯坦福大学、佐治亚理工学院和哥伦比亚大学）。项目团队还将通过外展研讨会向建筑管理领域的从业者/政策制定者传播结果。此外，该项目将通过多元化团队并与非营利组织 Girls Who Code 合作，将项目数据集和工具整合到其活动中，扩大对计算领域的参与。该奖项反映了 NSF 的法定使命，并通过使用评估结果被认为值得支持。基金会的智力价值和更广泛的影响审查标准。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Learning socio-organizational network structure in buildings with ambient sensing data

利用环境传感数据学习建筑物中的社会组织网络结构

DOI：
10.1017/dce.2020.9
发表时间：
2020-01
期刊：
Data-Centric Engineering
影响因子：
0
作者：
Sonta, Andrew;Jain, Rishee K.
通讯作者：
Jain, Rishee K.

Inferring occupant ties: automated inference of occupant network structure in commercial buildings

推断住户关系：商业建筑中住户网络结构的自动推断

DOI：
10.1145/3276774.3276779
发表时间：
2018-11
期刊：
Proceeding BuildSys '18 Proceedings of the 5th Conference on Systems for Built Environments
影响因子：
0
作者：
Sonta, Andrew J.;Jain, Rishee K.
通讯作者：
Jain, Rishee K.

Simulation-aided occupant-centric building design: A critical review of tools, methods, and applications

模拟辅助的以居住者为中心的建筑设计：对工具、方法和应用程序的严格审查

DOI：
10.1016/j.enbuild.2020.110292
发表时间：
2020-10-01
期刊：
Energy and Buildings
影响因子：
6.7
作者：
Elie Azar;W. O’brien;S. Carlucci;S. Carlucci;Tianzhen Hong;A. Sonta;Joyce Kim;Maedot S. Andargie;Tareq Abuimara;M. E. Asmar;Rishee K. Jain;M. Ouf;F. Tahmasebi;Jin Zhou
通讯作者：
Jin Zhou

Building Relationships: Using Embedded Plug Load Sensors for Occupant Network Inference

建立关系：使用嵌入式插头负载传感器进行乘员网络推理