Building-Occupant Network Dynamics

Building-Occupant Network Dynamics

Approximately 40% of all energy consumed in the United States is consumed by the built environment, with the associated carbon dioxide emissions contributing to over 75% of the carbon footprint in dense urban areas. The current presidential administration has pledged that all buildings reduce energy use by over 80% by 2050. This research examines the role of networks in influencing energy use in the built environment, the flow of energy use practices through building occupant networks, and develops integrated information systems to connect building occupants with energy use information.

National Science Foundation #1837021

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 in test-bed buildings.

Acknowledgment and Disclaimer: This material is based upon work supported by the National Science Foundation under Grant No. 1837021. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Center for the Development and Application of Internet of Things

The research objective of this project is to develop a virtual reality (VR) analytics platform that explores space-time interdependencies underlying the dynamics of cities at different scales, in which Internet of Things (IoT) is the communication paradigm. This project will be completed in three phases, constituting the essential components for a Smart City Digital Twin. By enabling immersive interactivity with both human and sensor-generated data in a digital replica of the city, this project is a step toward understanding and improving how cities’ infrastructure systems influence one another in time and space in relation to citizens’ interactions with these systems. Harnessing IoT to create a Smart City Digital Twin is critical to enable the smart city vision for the City of Atlanta.

National Science Foundation #1639266

The research objective of this I-CORPS project is to develop a hardware+software solution that enables building HVAC control systems to anticipate occupant needs based on detected behavior. This enables better understanding and modeling of occupant behavior in commercial buildings. Such information can facilitate a smoother and more efficient operation of building cooling and heating system while improving occupants comfort in buildings. Learning occupants’ behavior in commercial buildings can enable development of occupant-based predictive building automation systems that not only considers local optimization of energy consumption and occupant comfort trade-off, but also can provide a global solution to a grid level energy efficiency through consideration of various demand-response programs, building energy consumption, and occupant comfort.

Acknowledgment and Disclaimer: This material is based upon work supported by the National Science Foundation under Grant No. 1142379. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

BioBuild, IGEP Virginia Tech

The BioBuild program is a new Interdisciplinary Graduate Education Program (IGEP) funded by the Graduate School at Virginia Tech. It was created to address pressing societal needs with interdisciplinary expertise informed by natural systems. The pedagogical objective of the BioBuild IGEP is to create and sustain a doctoral program at the confluence of the built environment and biology and foster competitive graduates with abilities to view grand challenges in the built and natural environments through an interdisciplinary lens.   The objective of this BioBuild sub-project is to draw inspiration from the plant kingdom to improve our understanding of mutually-influenced spatially-proximal buildings from the perspectives of energy supply and efficiency.  We examine integrated and adaptive building networks through both numerical and experimental analyses.

Department of Energy

Commercial and residential buildings account for a significant portion of U.S. electricity consumption (35% and 39% respectively), and present a large opportunity for achieving significant energy savings. Academic research on systems that influence behavior by providing building occupants with various forms of energy consumption information (i.e. eco-feedback systems) has shown that such systems yield energy savings ranging from 5-55%. Assuming a conservative building energy savings rate of 10%, eco-feedback systems deployed at scale can potentially save the United States up to 4.033 Quadrillion Btu of energy, the equivalent of 563.4 Million Metric Tons of carbon dioxide emissions (close to 1% of total global emissions), each year. This research project develops a next generation smart building social energy management platform (BizWatts) that will empower employees to conserve energy at the workplace.

Acknowledgment and Disclaimer: This material is based upon work supported by the Department of Energy. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Department of Energy.

National Science Foundation #1142379

The research objective of this Faculty Early Career Development (CAREER) program award is to understand, improve and predict the dynamics that occur in networks of building occupants when individuals in these networks can view energy utilization of their peers. In pursuing this objective, this research aims to address the grand challenge of reducing building energy consumption and associated greenhouse gas emissions. Four energy utilization experiments in residential and administrative buildings will be performed to empirically assess and model the complex inter-relationships between occupants and occupant networks in influencing energy use decisions. These experimental results will be integrated into an agent-based simulation model to predict the impact of building occupant network dynamics on achieving sustained energy conservation in and across buildings. The pedagogical objective of this CAREER program award is to combine in-class experiments and pedagogical simulations to achieve critical thinking and higher order learning that fosters the dynamic engineer of the future called for by the National Academy of Engineering and the American Society of Civil Engineers.

Acknowledgment and Disclaimer: This material is based upon work supported by the National Science Foundation under Grant No. 1142379. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

National Science Foundation #0903597

This research transforms design approaches to contemporary urban expansion. Until recently, strategies for urban growth were able to assume inexpensive energy, unlimited access to safe drinking water and an environment that could absorb all the waste products of urban civilization: None of these assumptions remains valid. This research examines the new urban requirements for; (1) adaptability – designing buildings, power, transport, water and sanitation infrastructure with flexibility to be repurposed as urban needs evolve and the urban environment changes, (2) ecology – designing to address environmental performance of urban buildings, landscapes, land uses and infrastructure, and (3) resilience – designing buildings and infrastructure anticipating the impact of climate changes on sea level, and the possibility of man-made and natural disasters. The most important contribution of this research is the examination of the interdependent requirements of designing holistically for adaptability, ecology and resilience.

Acknowledgment and Disclaimer: This material is based upon work supported by the National Science Foundation under Grant No. 0903597. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Earth Institute #4-10552

This research involves developing a model and user interface to share information about building energy use in a multi-family residential building. The toolkit was implemented in a pilot study across the building to examine energy use consequences of different information sharing approaches. We found that coupling energy use with occupants resulted in substantial savings in energy use (27.3%). However, toward the end of the study the majority of the participants exhibited a relapse in conservation behavior. The group that exhibited a statistically robust response and reduced consumption the most was the group exposed to peer utilization data.

lightbulb

FUNDING

National Science Foundation – Cyber-Physical Systems Program – Grant #1837021
Amount: $479,932   
Duration
: 2018 – 2021
Title: Building Information, Inhabitant, Interaction and Intelligent Integrated Modeling (BI5M)

Center for the Development and Application of Internet of Things (Georgia Tech)
Amount: $50,000   
Duration
: 2018 – 2019
Title: Harnessing IoT to VR-enable Smart City Digital Twins

National Science Foundation – ICORPS Program – Grant #1639266
Amount: $50,000   
Duration
: 2016 – 2017
Title: Conceptualizing and Validating an Occupant-aware Predictive Control System

Interdisciplinary Graduate Education Program (Virginia Tech)
Amount: $450,000   
Duration
: 2013 – 2017
Title: BioBuild – New PhD Program in Bio-inspired Building

Department of Energy
Amount: $74,960   
Duration
: 2013 – 2015
Title: BizWatts – Empowering Employees to Conserve Energy

National Science Foundation – Civil Infrastructure Systems Program – Grant #1142379
Amount: $400,000   
Duration
: 2011 – 2016
Title: Building Occupant Network Dynamics

National Science Foundation – IGERT Program – Grant #0903597
Amount: $2,959,966   
Duration
: 2009 – 2014
Title: Solving Urbanization Challenges by Design

Earth Institute #4-10552 
Amount: $31,985
Duration: 2008 – 2009
Title: Coupling Technology and Organizational Dynamics to Induce Energy Efficient Behavior

COLLABORATORS

FACULTY

Gisele Bennett
Senior Vice President and Professor, Florida Institute of Technology
Jiayu Chen
Assistant Professor, City University of Hong Kong
Patricia Culligan
Professor, Columbia University
Rishee Jain
Professor, Stanford University
Neda Mohammadi
Post-doctoral Fellow, Georgia Tech
Ryan Wang
Assistant Professor, Northeastern University
Ying Zhang
Associate Professor, Georgia Tech

STUDENTS

Abby Francisco
PhD Student, Georgia Tech
Pragadeesh Muthah
MS Student, Georgia Tech
Lei Xu
PhD Student, Georgia Tech