Selective distributed model predictive control for comfort satisfaction in multi-zone buildings

Distributed model predictive control (DMPC) for thermal regulation in multi-zone buildings continues to gain attention over centralized approaches. Particularly, centralized control approaches have been shown to become impractical when applied to large-scale buildings due to for example, computation complexity, modeling complexity of large buildings and availability of required sensor and actuator infrastructure. In this paper a novel selective DMPC algorithm is developed in which, each agent optimizes a cost function to minimize the control effort in order to save energy and satisfying the comfort bound. The proposed method is useful especially in building thermal regulation when the objective is to keep the temperature of each zone in the building within the defined comfort bound.

[1]  Daniel E. Fisher,et al.  EnergyPlus: creating a new-generation building energy simulation program , 2001 .

[2]  Michael Wetter,et al.  Co-simulation of building energy and control systems with the Building Controls Virtual Test Bed , 2011 .

[3]  James E. Braun,et al.  A Distributed Model Predictive Control Approach for Optimal Coordination of Multiple Thermal Zones in a Large Open Space , 2016 .

[4]  Stephen J. Wright,et al.  Stability and optimality of distributed model predictive control , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[5]  Eduardo F. Camacho,et al.  Distributed model predictive control based on agent negotiation , 2011 .

[6]  Simeng Liu,et al.  Experimental analysis of simulated reinforcement learning control for active and passive building thermal storage inventory: Part 1. Theoretical foundation , 2006 .

[7]  Frauke Oldewurtel,et al.  Experimental analysis of model predictive control for an energy efficient building heating system , 2011 .

[8]  Truong Nghiem,et al.  MLE+: a tool for integrated design and deployment of energy efficient building controls , 2013, SIGBED Rev..

[9]  Niel Canty,et al.  Game theory-based distributed predictive control: Application to shell oil fractionator , 2015, 2015 26th Irish Signals and Systems Conference (ISSC).

[10]  Mohamed Yacine Lamoudi.  Distributed model predictive control for energy management in buildings , 2012 .

[11]  J. Maciejowski,et al.  Soft constraints and exact penalty functions in model predictive control , 2000 .

[12]  Francesco Borrelli,et al.  Predictive Control for Energy Efficient Buildings with Thermal Storage: Modeling, Stimulation, and Experiments , 2012, IEEE Control Systems.

[13]  Simeng Liu,et al.  Experimental analysis of simulated reinforcement learning control for active and passive building thermal storage inventory: Part 2: Results and analysis , 2006 .

[14]  Colin Neil Jones,et al.  OpenBuild : An integrated simulation environment for building control , 2015, 2015 IEEE Conference on Control Applications (CCA).

[15]  Bart De Schutter,et al.  Distributed Model Predictive Control and Estimation of Large-Scale Multi-Rate Systems , 2011 .

[16]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[17]  R. Williams,et al.  Folic Acid. I. Concentration from Spinach1a , 1944 .

[18]  Lorenz T. Biegler,et al.  Constraint handing and stability properties of model‐predictive control , 1994 .

[19]  Rudy R. Negenborn,et al.  Distributed Model Predictive Control Made Easy , 2013 .

[20]  Colin N. Jones,et al.  On the selection of the most appropriate MPC problem formulation for buildings , 2013 .