Building temperature distributed control via explicit MPC and “Trim and Respond” methods

Koehler, Sarah; Borrelli, Francesco

doi:10.23919/ecc.2013.6669781

Cited by 35 publications

(17 citation statements)

References 9 publications

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“…Examples of RC network modeling include [4,5,[19][20][21][22][23][24]. These models of the building physics capture the temperatures, thermal loads, and associated dynamics of the air and structural elements.…”

Section: Supervisory Control Of Building Loadsmentioning

confidence: 99%

Building-level power demand forecasting framework using building specific inputs: Development and applications

Touretzky

Patil²

2015

Applied Energy

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Section: Supervisory Control Of Building Loadsmentioning

confidence: 99%

Building-level power demand forecasting framework using building specific inputs: Development and applications

Touretzky

Patil²

2015

Applied Energy

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“…The BC has been fine-tuned by professional and implements a trim and respond control algorithm. Details on the BC algorithm can be found in [28]. The main idea is to control the heating coil valve position command and the airflow setpoint as a function of the difference between zone temperature and the bounds on comfort level.…”

Section: A Bancroft Library Simulations 1) Simulation Setupmentioning

confidence: 99%

Stochastic Model Predictive Control for Building HVAC Systems: Complexity and Conservatism

Matuško

Borrelli

2015

IEEE Trans. Contr. Syst. Technol.

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209

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This paper presents a stochastic model predictive control (SMPC) approach to building heating, ventilation, and air conditioning (HVAC) systems. The building HVAC system is modeled as a network of thermal zones controlled by a central air handling unit and local variable air volume boxes. In the first part of this paper, simplified nonlinear models are presented for thermal zones and HVAC system components. The uncertain load forecast in each thermal zone is modeled by finitely supported probability density functions (pdfs). These pdfs are initialized using historical data and updated as new data becomes available. In the second part of this paper, we present a SMPC design that minimizes expected energy cost and bounds the probability of thermal comfort violations. SMPC uses predictive knowledge of uncertain loads in each zone during the design stage. The complexity of a commercial building requires special handling of system nonlinearities and chance constraints to enable real-time implementation, minimize energy cost, and guarantee thermal comfort. This paper focuses on the tradeoff between computational tractability and conservatism of the resulting SMPC scheme. The proposed SMPC scheme is compared with alternative SMPC designs, and the effectiveness of the proposed approach is demonstrated by simulation and experimental tests.Index Terms-Building energy system, nonlinear system, stochastic model predictive control (SMPC).

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“…The intuition behind this algorithm is that the initial active sets indicate a node's request for control action; if the initial active sets are all empty, then all actuators should do nothing. This initialization method was used in previous work to achieve a one‐step communication solution for a distributed model predictive controller . The idea of nodes requesting control action comes from the logic exhibited in the ‘Trim and Respond’ algorithm in .…”

Section: A Distributed Implementation On Star Communication Networkmentioning

confidence: 99%

“…This initialization method was used in previous work to achieve a one‐step communication solution for a distributed model predictive controller . The idea of nodes requesting control action comes from the logic exhibited in the ‘Trim and Respond’ algorithm in . Moreover, this algorithm is particularly suited for problems where the penalty on u is higher than the penalty on x , that is, R ≻ Q , and the number of links is greater than the number of nodes, that is,

s \geq r

.…”

Section: A Distributed Implementation On Star Communication Networkmentioning

confidence: 99%

A primal‐dual active‐set method for distributed model predictive control

Koehler

Danielson

Borrelli

2016

Optim Control Appl Methods

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Summary We present a novel distributed primal‐dual active‐set method for model predictive control. The primal‐dual active‐set method is used for solving model predictive control problems for large‐scale systems with quadratic cost, linear dynamics, additive disturbance, and box constraints. The proposed algorithm is compared with dual decomposition and an alternating direction method of multipliers. Theoretical and experimental results show the effectiveness of the proposed approach for large‐scale systems with communication delays. The application to building control systems is thoroughly investigated. Copyright © 2016 John Wiley & Sons, Ltd.

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Building temperature distributed control via explicit MPC and “Trim and Respond” methods

Cited by 35 publications

References 9 publications

Building-level power demand forecasting framework using building specific inputs: Development and applications

Building-level power demand forecasting framework using building specific inputs: Development and applications

Stochastic Model Predictive Control for Building HVAC Systems: Complexity and Conservatism

A primal‐dual active‐set method for distributed model predictive control

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