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）

• 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

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.

美国每年花费超过4000亿美元用于建筑物的电力、供暖和制冷。此外，建筑物是环境排放的主要来源。因此，即使国家建筑物的能源效率略有提高，也会带来巨大的经济和环境效益。在这个项目中，重点是提高商业建筑的能源效率，因为这个部门代表了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（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模式相结合。该项目旨在设计、分析和测试一个网络物理和人在回路的控制系统，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）交互模型相结合，以实现商业建筑系统的智能控制。具体而言，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（BI 5 M），旨在减少建筑物中的能源使用; 2）评估物理建筑和居民模型之间的复杂相互关系，与节能行为相关的网络建筑-人的交互和智能控制模型; 3）在斯坦福大学校园和谷歌办公园区的试验台建筑中对模块和整个BI 5 M系统进行经验性测试和验证。该研究将测量（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（增强用户对插电负载设备、HVAC、照明的控制）融入BI 5 M系统。BI 5 M系统以建筑物的网络建筑信息管理（BIM）模型为中心，将包含严格的系统工程，将探索跨网络物理领域的关系，并为如何利用网络物理系统的科学原理通过居住者行为和智能控制来影响商业建筑的能源效率开发新的见解。本研究借由整合实体建筑物资讯及住客与电脑互动模式，提出一套适用于商业建筑物的人在回路控制模式。除了测试平台和经过验证的商业建筑CPS系统（BI 5 M）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;居民的复杂社会空间结构;深入了解建筑物与人以及人与人之间的互动如何影响居民的消费行为;以及新的控制模式，这些模式利用了对居民的能源使用、空间、社会和行为动态的投入。该项目的教育影响将扩展到参与者（学生，教师，测试台建筑中的Google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（斯坦福大学，格鲁吉亚理工学院和哥伦比亚）的课程/项目中，更广泛的学生群体。项目小组还将通过外联讲习班向在建筑物管理领域工作的从业人员/决策者传播成果。此外，该项目还将通过多元化的团队以及与非营利组织Girls Who Code合作，将项目数据集和工具整合到他们的活动中，从而扩大计算领域的参与。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Tropical climates and the interplay between IEQ and energy consumption in buildings: A review

• DOI: 10.1016/j.buildenv.2023.110551
• 发表时间: 2023-08
• 期刊: Building and Environment
• 影响因子: 7.4
• 作者: Ashutosh Verma;Vallary Gupta;Kopal Nihar;Arnab Jana;Rishee K. Jain;C. Deb

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
• 期刊: Proceeding BuildSys '18 Proceedings of the 5th Conference on Systems for Built Environments
• 作者: Sonta, Andrew J.;Jain, Rishee K.
• 通讯作者: Jain, Rishee K.

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
• 期刊: Data-Centric Engineering
• 作者: Sonta, Andrew;Jain, Rishee K.
• 通讯作者: Jain, Rishee K.

Data-driven optimization of building layouts for energy efficiency

• DOI: 10.1016/j.enbuild.2021.110815
• 发表时间: 2020-07
• 期刊: ArXiv
• 作者: A. Sonta;Thomas R. Dougherty;Rishee K. Jain

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

• DOI: 10.1109/les.2019.2937316
• 发表时间: 2020
• 期刊: IEEE Embedded Systems Letters
• 影响因子: 1.6
• 作者: Sonta, Andrew J.;Jain, Rishee K.
• 通讯作者: Jain, Rishee K.