Multiple time-scale model predictive control for thermal comfort in buildings

Kalaimani, Rachel Kalpana; Keshav, Srinivasan; Rosenberg, Catherine

doi:10.1145/2939912.2942355

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…In [27], MPC-based control strategies are adopted to individually optimize thermal comfort and energy savings. Reference [28] is a preliminary paper where we investigate the idea of two time-scale HVAC control.…”

Section: Related Workmentioning

confidence: 99%

On the interaction between personal comfort systems and centralized HVAC systems in office buildings

Kalaimani

Jain

Keshav

et al. 2018

Advances in Building Energy Research

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Most modern HVAC systems suffer from two intrinsic problems. First, inability to meet diverse comfort requirements of the occupants. Second, heat or cool an entire zone even when the zone is only partially occupied. Both issues can be mitigated by using personal comfort systems (PCS) which bridge the comfort gap between what is provided by a central HVAC system and the personal preferences of the occupants. In recent work, we have proposed and deployed such a system, called SPOT.We address the question, "How should an existing HVAC system modify its operation to benefit the availability of PCS like SPOT?" For example, energy consumption could be reduced during sparse occupancy by choosing appropriate thermal set backs, with the PCS providing the additional offset in thermal comfort required for each occupant. Our control strategy based on Model Predictive Control (MPC), employs a bi-linear thermal model, and has two time-scales to accommodate the physical constraints that limit certain components of the central HVAC system from frequently changing their set points.We compare the energy consumption and comfort offered by our SPOT-aware HVAC system with that of a state-of-the-art MPC-based central HVAC system in multiple settings including different room layouts and partial deployment of PCS. Numerical evaluations show that our system obtains, in average, 45% (15%) savings in energy in summer (winter), compared with the benchmark system for the case of homogeneous comfort requirements. For heterogeneous comfort requirements, we observe 51% (29%) improvement in comfort in summer (winter) in addition to significant savings in energy.

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Section: Related Workmentioning

confidence: 99%

On the interaction between personal comfort systems and centralized HVAC systems in office buildings

Kalaimani

Jain

Keshav

et al. 2018

Advances in Building Energy Research

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“…In fact, there are many multi-time scale decision-making problems in the field of building energy optimization. For example, supply air temperature and the ratio of re-use air in a commercial building HVAC system can be adjusted once every hour since the frequent adjustment can cause damage to HVAC components [73]- [75]. By contrast, supply air rate in each zone can be changed every 10-15 minutes [75].…”

Section: A Multi-time Scale Building Energy Optimizationmentioning

confidence: 99%

“…For example, supply air temperature and the ratio of re-use air in a commercial building HVAC system can be adjusted once every hour since the frequent adjustment can cause damage to HVAC components [73]- [75]. By contrast, supply air rate in each zone can be changed every 10-15 minutes [75]. When confronted with multi-time scale decision-making problems, existing DRLbased methods are not applicable.…”

Section: A Multi-time Scale Building Energy Optimizationmentioning

confidence: 99%

Deep Reinforcement Learning for Smart Home Energy Management

Xie

et al. 2020

IEEE Internet Things J.

308

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In this paper, we investigate an energy cost minimization problem for a smart home in the absence of a building thermal dynamics model with the consideration of a comfortable temperature range. Due to the existence of model uncertainty, parameter uncertainty (e.g., renewable generation output, nonshiftable power demand, outdoor temperature, and electricity price) and temporally-coupled operational constraints, it is very challenging to determine the optimal energy management strategy for scheduling Heating, Ventilation, and Air Conditioning (HVAC) systems and energy storage systems in the smart home. To address the challenge, we first formulate the above problem as a Markov decision process, and then propose an energy management strategy based on Deep Deterministic Policy Gradients (DDPG). It is worth mentioning that the proposed strategy does not require the prior knowledge of uncertain parameters and building thermal dynamics model. Simulation results based on real-world traces demonstrate the effectiveness and robustness of the proposed strategy.

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“…The above time-scales are typically determined by the physical limitation of an HVAC unit. For more details about this specific formulation of the optimization problem, please refer to Kalaimani et al [37].…”

Section: Model Predictive Controlmentioning

confidence: 99%

Using personal environmental comfort systems to mitigate the impact of occupancy prediction errors on HVAC performance

et al. 2018

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Heating, Ventilation and Air Conditioning (HVAC) consumes a significant fraction of energy in commercial buildings. Hence, the use of optimization techniques to reduce HVAC energy consumption has been widely studied. Model predictive control (MPC) is one state of the art optimization technique for HVAC control which converts the control problem to a sequence of optimization problems, each over a finite time horizon. In a typical MPC, future system state is estimated from a model using predictions of model inputs, such as building occupancy and outside air temperature. Consequently, as prediction accuracy deteriorates, MPC performance-in terms of occupant comfort and building energy use-degrades. In this work, we use a custom-built building thermal simulator to systematically investigate the impact of occupancy prediction errors on occupant comfort and energy consumption. Our analysis shows that in our test building, as occupancy prediction error increases from 5% to 20% the performance of an MPC-based HVAC controller becomes worse than that of even a simple static schedule. However, when combined with a personal environmental control (PEC) system, HVAC controllers are considerably more robust to prediction errors. Thus, we quantify the effectiveness of PECs in mitigating the impact of forecast errors on MPC control for HVAC systems.

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Multiple time-scale model predictive control for thermal comfort in buildings

Cited by 5 publications

References 5 publications

On the interaction between personal comfort systems and centralized HVAC systems in office buildings

On the interaction between personal comfort systems and centralized HVAC systems in office buildings

Deep Reinforcement Learning for Smart Home Energy Management

Using personal environmental comfort systems to mitigate the impact of occupancy prediction errors on HVAC performance

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