Model predictive control for HVAC systems &amp;#x2014; A review

Kwadzogah, R.; Zhou, MengChu; Li, Sisi

doi:10.1109/coase.2013.6654072

Cited by 26 publications

(16 citation statements)

References 21 publications

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“…Simulation results show that both electricity consumption and cost can be significantly reduced for the campus central plant at the University of California, Irvine. Future research includes more benchmark studies with other control methods [28], [29] and using the optimization methods successfully used in other fields to solve this problem [30]- [49].…”

Section: Discussionmentioning

confidence: 99%

Model Predictive Control of Central Chiller Plant With Thermal Energy Storage Via Dynamic Programming and Mixed-Integer Linear Programming

Deng

Sun

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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Abstract-This work considers the optimal scheduling problem for a campus central plant equipped with a bank of multiple electrical chillers and a thermal energy storage (TES). Typically, the chillers are operated in ON/OFF modes to charge TES and supply chilled water to satisfy the campus cooling demands. A bilinear model is established to describe the system dynamics of the central plant. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity cost. At each time step, the MPC problem is represented as a large-scale mixed-integer nonlinear programming problem. We propose a heuristic algorithm to obtain suboptimal solutions for it via dynamic programming (DP) and mixed integer linear programming (MILP). The system dynamics is linearized along the simulated trajectories of the system. The optimal TES operation profile is obtained by solving a DP problem at every horizon, and the optimal chiller operations are obtained by solving an MILP problem at every time step with a fixed TES operation profile. Simulation results show desired performance and computational tractability of the proposed algorithm. Note to Practitioners-This

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Section: Discussionmentioning

confidence: 99%

Model Predictive Control of Central Chiller Plant With Thermal Energy Storage Via Dynamic Programming and Mixed-Integer Linear Programming

Deng

Sun

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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“…So in this paper we also formulate the problem this way. In recent literature, the MPC method has emerged as an effective approach to solve the optimization problem of building operation under uncertainties mainly due to its ability to handle constrained optimal control problems [37] and take the prediction of future system behavior into consideration while satisfying the system constraints [13]. However, MPC method is time-triggered and may thus cause a waste of communication and computational resources due to the recalculation at stages when the building system is operating within a desirable range.…”

Section: Discussionmentioning

confidence: 99%

“…Model predictive control (MPC) is another method usually used to address the problem under uncertainties. Existing results obtained in [10], [12], and [13] showed that MPC features high energy and cost saving since the future evolution of system dynamics is involved in the decision-making process and the optimal solution is obtained based on a rolling horizon basis. Kwadzogah et al [13] provided a review of MPC for HVAC systems.…”

Section: Introductionmentioning

confidence: 99%

“…Existing results obtained in [10], [12], and [13] showed that MPC features high energy and cost saving since the future evolution of system dynamics is involved in the decision-making process and the optimal solution is obtained based on a rolling horizon basis. Kwadzogah et al [13] provided a review of MPC for HVAC systems. Event-based optimization methods have recently been applied to building energy efficiency such as in [14], [15].…”

Section: Introductionmentioning

confidence: 99%

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PMV-based event-triggered mechanism for building energy management under uncertainties

Spanos

et al. 2017

Energy and Buildings

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This paper provides a study of the optimal scheduling of building operation to minimize its energy cost under building operation uncertainties. Opposed to the usual way that describes thermal comfort using a static range of air temperature, the optimization of a tradeoff between energy cost and thermal comfort predicted mean vote (PMV) index is addressed in this paper. In order to integrate the calculation of the PMV index with the optimization procedure, we develop a sufficiently accurate approximation of the original PMV model which is computationally efficient. We develop a model-based periodic eventtriggered mechanism (ETM) to handle the uncertainties in the building operation. Upon the triggering of predefined events, the ETM determines whether the optimal strategy should be recalculated. In this way, the communication and computational resources required can be significantly reduced. Numerical results show that the ETM method is robust with respect to the uncertainties in prediction errors and results in a reduction of more than 60% in computation without perceivable degradation in system performance as compared to a typical closed-loop model predictive control.

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“…However, this linearization is valid only in the neighborhood of an operating point, reducing performance when the system deviates from it. More recent work uses a non-linear system model [1,6] combined with Model Predictive Control (MPC) [3]. In this approach, at each time step an optimal control action is computed using forecasts of the system behavior.…”

Section: Introductionmentioning

confidence: 99%

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

Kalaimani

Keshav

Rosenberg

2016

Proceedings of the Seventh International Conference on Future Energy Systems Poster Sessions

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Intelligent control of heating, ventilation, and air conditioning (HVAC) systems in commercial buildings have been extensively studied in the literature. Although prior work has shown the benefits of using Model Predictive Control (MPC), existing work falls short either by relying on linear HVAC models or using MPC assuming control actions at a single (hourly) time scale, although more frequent control is feasible for some HVAC elements. Our main contribution is the use of a bi-linear thermal model and the careful modeling of the multiple time-scales inherent in the operation of an HVAC system, which permits the design of a multiple time-scale MPC control. We find that employing a multiple time-scale MPC results in significantly better comfort in comparison to a single time-scale MPC, typically without an increase in power consumption. Moreover, there exist cases where there is a significant reduction in power consumption (40%) for the two time-scale MPC in comparison to the single time-scale, with no decrease in comfort.

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Model predictive control for HVAC systems — A review

Cited by 26 publications

References 21 publications

Model Predictive Control of Central Chiller Plant With Thermal Energy Storage Via Dynamic Programming and Mixed-Integer Linear Programming

Model Predictive Control of Central Chiller Plant With Thermal Energy Storage Via Dynamic Programming and Mixed-Integer Linear Programming

PMV-based event-triggered mechanism for building energy management under uncertainties

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

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