Hot, cold and in between

Klingensmith, Neil; Bomber, Joseph; Banerjee, Suman

doi:10.1145/2602044.2602049

Cited by 16 publications

References 6 publications

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Singular Perturbation Method for Smart Building Temperature Control Using Occupant Feedback

Gupta

Kar

Mishra

et al. 2017

Asian Journal of Control

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In this work we propose a framework for incorporating occupant feedback towards temperature control of multi-occupant spaces, and analyze it using singular perturbation theory. Such a system would typically have to accommodate occupants with different temperature preferences, and incorporate that with thermal correlation among multiple zones to obtain optimal control for minimization of occupant discomfort and energy cost. In current practice, an acceptable temperature set point for the occupancy level of the zone is estimated, and the control law is designed to maintain temperature at the corresponding set point irrespective of the changes in occupancy and the preferences of multiple occupants. Proposed algorithm incorporates active occupant feedback to minimize aggregate user discomfort and total energy cost. Occupant binary feedback in the form of hot/cold or thermal comfort preference input is used by the control algorithm. The control algorithm also takes the energy cost into account, trading it off optimally with the aggregate occupant discomfort. For convergence to the optimal, sufficient separation between the occupant feedback frequency and the temperature dynamics of system is necessary; in absence of which, the occupant feedback provided do not correctly reflect the effect of current control input value on occupant discomfort. Under sufficient time scale separation, using singular perturbation theory, we establish the stability condition of the system and show convergence of the proposed solution to the desired temperature that minimizes energy cost plus occupant discomfort. The occupants are only assumed to be rational in that they choose their own comfort range to minimize individual thermal discomfort. Optimization for a multi-zone building also takes into account the thermal correlation among different zones. Simulation study with parameters based on our test facility, and experimental study conducted in the same building demonstrates performance of the algorithm under different occupancy and ambient conditions.

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Singular Perturbation Method for Smart Building Temperature Control Using Occupant Feedback

Gupta

Kar

Mishra

et al. 2017

Asian Journal of Control

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Edge Computing in the Extreme for Sustainability

Banerjee

Klingensmith

Liu

et al. 2017

Lecture Notes in Computer Science

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Unmasking the role of remote sensors in comfort, energy, and demand response

Mulayim,

Severnini,

Bergés

2024

DCE

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In single-zone multi-node systems (SZMNSs), temperature controls rely on a single probe near the thermostat, resulting in temperature discrepancies that cause thermal discomfort and energy waste. Augmenting smart thermostats (STs) with per-room sensors has gained acceptance by major ST manufacturers. This paper leverages additional sensory information to empirically characterize the services provided by buildings, including thermal comfort, energy efficiency, and demand response (DR). Utilizing room-level time-series data from 1000 houses, metadata from 110,000 houses across the United States, and data from two real-world testbeds, we examine the limitations of SZMNSs and explore the potential of remote sensors. We discover that comfortable DR durations (CDRDs) for rooms are typically 70% longer or 40% shorter than for the room with the thermostat. When averaging, rooms at the control temperature’s bounds are typically deviated around −3 °F to 2.5 °F from the average. Moreover, in 95% of houses, we identified rooms experiencing notably higher solar gains compared to the rest of the rooms, while 85% and 70% of houses demonstrated lower heat input and poor insulation, respectively. Lastly, it became evident that the consumption of cooling energy escalates with the increase in the number of sensors, whereas heating usage experiences fluctuations ranging from −19% to +25%. This study serves as a benchmark for assessing the thermal comfort and DR services in the existing housing stock, while also highlighting the energy efficiency impacts of sensing technologies. Our approach sets the stage for more granular, precise control strategies of SZMNSs.

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Hot, cold and in between

Cited by 16 publications

References 6 publications

Singular Perturbation Method for Smart Building Temperature Control Using Occupant Feedback

Singular Perturbation Method for Smart Building Temperature Control Using Occupant Feedback

Edge Computing in the Extreme for Sustainability

Unmasking the role of remote sensors in comfort, energy, and demand response

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