Multi-agent control for centralized air conditioning systems serving multi-zone buildings

Coordinating different components in a complex air conditioning system is challenging for centralized controls due to the large number of optimization variables. In this scenario, de-centralized controls are more appropriate alternatives. This study proposes a multi-agent control methodology for the optimal control of centralized air conditioning systems that are typically adopted in multi-zone commercial buildings. A hierarchical multi-agent framework is developed in which the agents cooperate to find the optimal operating point. Two consensus-based distributed optimization algorithms are formulated for this specific type of problem, which form the underlying mechanism of intra-agent optimization and inter-agent cooperation. Finally, a 3-zone building case study is used to demonstrate the performance of the proposed approach.

[1]  Marcelo Godoy Simões,et al.  An Energy Management System for Building Structures Using a Multi-Agent Decision-Making Control Methodology , 2013 .

[2]  Mazen Alamir,et al.  Distributed constrained Model Predictive Control based on bundle method for building energy management , 2011, IEEE Conference on Decision and Control and European Control Conference.

[3]  Asuman Ozdaglar,et al.  Cooperative distributed multi-agent optimization , 2010, Convex Optimization in Signal Processing and Communications.

[4]  Steven T. Bushby,et al.  Are intelligent agents the key to optimizing building HVAC system performance? , 2012, HVAC&R Research.

[5]  Teresa Wu,et al.  Decentralized operation strategies for an integrated building energy system using a memetic algorithm , 2012, Eur. J. Oper. Res..

[6]  Ardeshir Mahdavi,et al.  An agent-based simulation-assisted approach to bi-lateral building systems control , 2003 .

[7]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[8]  Peter B. Luh,et al.  An integrated control of shading blinds, natural ventilation, and HVAC systems for energy saving and human comfort , 2010, 2010 IEEE International Conference on Automation Science and Engineering.

[9]  Zhang Guiqing,et al.  Building energy saving design based on multi-agent system , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[10]  Lingfeng Wang,et al.  Multi-agent control system with intelligent optimization for smart and energy-efficient buildings , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[11]  Petru-Daniel Morosan,et al.  Distributed model predictive control based on Benders' decomposition applied to multisource multizone building temperature regulation , 2010, 49th IEEE Conference on Decision and Control (CDC).

[12]  Paul Davidsson,et al.  Distributed monitoring and control of office buildings by embedded agents , 2005, Inf. Sci..

[13]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[14]  John N. Tsitsiklis,et al.  Parallel and distributed computation , 1989 .

[15]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[16]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.