Human-centered Systems for Cyber-enabled Sustainable Buildings

In the U.S., the building sector accounts for about 40% of primary energy usage, 71% of electricity and 38% of carbon dioxide emissions. For this reason, development of efficient solutions to reduce energy consumption and environmental impact of buildings is of critical societal importance. Occupants play a significant role in energy use of office buildings, affecting up to 30% of the energy use. To manage occupants’ energy impact due to their presence and behavior, environmental control systems (e.g., HVAC, shading, lighting) have been automated based on the use of “widely acceptable” visual and thermal comfort metrics. However, occupants have a strong preference for customized indoor climate, and there is a strong relationship between occupants’ perception of control over their environment and productivity, health and well-being. To address the challenge of customized control of the environment, the objective of this project is to realize a new paradigm for human-centered sustainable buildings, enabled by conducting research with computing innovations in probabilistic methods and machine learning, linked to sustainability, and with broader impacts in multiple domains of science and engineering. Broader impacts are: (1) New computing methods and algorithms on probabilistic classification, inference and optimal control that may impact a number of scientific communities, including Architectural, Mechanical, Electrical, Computer and Industrial/Human Factors Engineering, Computer and Psychological Sciences. Potential application areas include genomics, traffic flow prediction, infrastructure systems including power, transportation, etc. (2) Integration of the project’s modeling, simulation, and experimental platforms into new teaching modules and experiential learning activities that support the curriculum and workforce development in four engineering schools (Civil, Mechanical, Electrical and Computer, Industrial Engineering) and the Department of Psychological Sciences. (3) Dissemination of research outcomes to the academic community and to the industry through publications, workshops, conferences and a customized external evaluation process. (4) Creation of outreach and engagement initiatives for K-12 teachers and students in STEM learning and research.

Three different clusters of thermal comfort being discovered from experimental data. From Lee et al. 2017.

This project takes a multidisciplinary approach that is grounded in (1) new algorithms for automated identification of the relevant human perception-attributes of buildings; and (2) new concepts for intelligent and self-tuned comfort delivery systems for customized thermal and visual environments in buildings. The research includes: (1) Laboratory and field studies with human test-subjects that map indoor environment conditions, thermal and visual perception and comfort, occupant-building interactions and control actions, as well as corresponding space performance for perimeter building zones. (2) Probabilistic classification of human perception, comfort, and satisfaction profiles for a typical population. (3) Computationally-efficient inference algorithms for online learning of individual and population-level human preferences. (4) Optimal control algorithms and simulation tools for implementation in building management systems. The research outcomes will be integrated into a new cyber-enabled technological solution for self-tuned comfort delivery devices (thermostats, shading and lighting actuators). The experimental prototypes and field demonstrations will achieve improved performance with quantified building energy use reduction and occupant satisfaction, as well as robustness to uncertainty due to the reduction in the frequency of overrides.

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Nimish Awalgaonkar
Ph.D. Student

Ph.D student