| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | September 14, 2018 |
| Latest Amendment Date: | July 31, 2023 |
| Award Number: | 1837021 |
| Award Instrument: | Standard Grant |
| Program Manager: |
David Corman
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | October 1, 2018 |
| End Date: | September 30, 2024 (Estimated) |
| Total Intended Award Amount: | $479,932.00 |
| Total Awarded Amount to Date: | $479,932.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
926 DALNEY ST NW ATLANTA GA US 30318-6395 (404)894-4819 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
225 North Avenue Atlanta GA US 30332-0002 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | CPS-Cyber-Physical Systems |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The Building Information, Inhabitant, Interaction and Intelligent Integrated Modeling (BI5M) project was a multi-year project supported by the Cyber Physicals Systems (CPS) program. Over the course of the project, the team:
1. Established experimental test-beds at each of the project sites, including Stanford University, Columbia University, and Georgia Tech. These test-beds facilitated the collection of high-fidelity plug-load energy usage data.
2. Developed novel integrated cyber-physical frameworks, models, and methods to structure and analyze spatial, energy, social, and behavioral data.
3. Conducted experiments to investigate and comprehend the influence of various feedback dimensions, such as energy, spatial, and social factors, on occupant behavior, interactions with building systems, and subsequent energy usage.
4. Created and tested a human-in-the-loop smart building system that incorporates decision-making utilizing spatial, energy, social, and behavioral data.
5. Formed a collaborative partnership with IIT-Bombay to explore smart buildings in tropical environments as part of the Indo-US DCL supplement.
Contributions include:
Advances in BIM applications through novel BIM-integrated energy visualization approaches, providing insights into optimizing energy feedback strategies to enhance occupant engagement and energy efficiency in buildings. Exploration of augmented reality (AR) experiments to assess alternative feedback methods and analyze their impact on user comprehension and interaction. Investigation of AR's impact on energy feedback and scalable approaches, extending from individual buildings to city-wide analysis.
Advances in cyber-physical systems frameworks, models, and methods for human-infrastructure interactions decision-making by integrating real-time data including, Smart City Digital Twins (SCDTs) for spatiotemporal modeling, multi-scale decision-making, real-time monitoring, forecasting, and response; and NLP-based automation frameworks for improving cyber threat analysis, predicting cyberattack scenarios, and enhancing cybersecurity for critical infrastructure, such as the power grid.
Advances in smart building systems by integrating human-in-the-loop decision-making for HVAC optimization through an accelerated distributed model predictive control (MPC) strategy that optimizes energy efficiency and occupant comfort while reducing computational costs; MBRL-MC, a hybrid model-based reinforcement learning and MPC method that enhances control stability and performance; and a meta-learning approach for personalized thermal comfort modeling, which minimizes required user feedback while improving adaptation and prediction accuracy, demonstrating computational advantages, improved learning efficiency, and better personalization of thermal conditions.
We spend over $400 billion annually to power, heat and cool our buildings making buildings the largest producer of environmental emissions in the United States. Despite this massive environmental and financial expenditure, our buildings still fail to deliver adequate levels of energy performance (i.e., meeting occupant needs while consuming minimal energy), with even so called “high- performance” buildings receiving low scores for comfort and satisfaction. The new integrated cyber-physical and human-in-the-loop building management system (BI5M) developed as part of this project will have a significant impact on society by enhancing occupant comfort/productivity, reducing energy costs and minimizing our environmental impact.
Last Modified: 02/09/2025
Modified by: Neda Mohammadi
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