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Building Simulation 2014, 7(4): 375-387 https://doi.org/10.1007/s12273-014-0161-z
Research Article | Issue | Published: 08 January 2014

Optimization based integrated control of building HVAC system

Show Author's Information Zhe Liu1,  Fangting Song1( ),  Ziyan Jiang1,  Xi Chen1,  Xiaohong Guan1,2
Department of Building Science, School of Architecture, Tsinghua University, Beijing 100084, China
MOE KLINNS Lab, Xi’an Jiaotong University, Xi’an 710049, China
Keywords:
optimization, HVAC system, energy savings, control strategy, building energy systems
Cite this article:
Liu Z, Song F, Jiang Z, et al. Optimization based integrated control of building HVAC system. Building Simulation, 2014, 7(4): 375-387. https://doi.org/10.1007/s12273-014-0161-z
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Abstract Full text About this article

Improving the control strategy of building HVAC (heating, ventilation, and air-conditioning) systems can lead to significant energy savings while preserving human comfort requirements. This paper focuses on the analysis of the optimal control strategy of the whole HVAC system itself (such as set point value curves for different parts, number control curves of different components) and the followed operating curves of each equipment and device. In order to have a better understanding of the optimal control strategy, performances of the conventional control strategies widely used in China are also shown in this paper.


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About this article

Optimization based integrated control of building HVAC system

Show Author's information Zhe Liu1, Fangting Song1( ), Ziyan Jiang1, Xi Chen1, Xiaohong Guan1,2
Department of Building Science, School of Architecture, Tsinghua University, Beijing 100084, China
MOE KLINNS Lab, Xi’an Jiaotong University, Xi’an 710049, China

Abstract

Improving the control strategy of building HVAC (heating, ventilation, and air-conditioning) systems can lead to significant energy savings while preserving human comfort requirements. This paper focuses on the analysis of the optimal control strategy of the whole HVAC system itself (such as set point value curves for different parts, number control curves of different components) and the followed operating curves of each equipment and device. In order to have a better understanding of the optimal control strategy, performances of the conventional control strategies widely used in China are also shown in this paper.

Keywords: optimization, HVAC system, energy savings, control strategy, building energy systems

References(32)

DP Bertsekas (1995). Nonlinear Programming. New York: Athena Scientific.
JE Braun (1990). Reducing energy costs and peak electrical demand through optimal control of building thermal storage. ASHRAE Transactions, 96(2): 876-888.
Google Scholar
TT Chow, GQ Zhang, Z Lin, CL Song (2002). Global optimization of absorption chiller system by genetic algorithm and neural network. Energy and Buildings, 34:103-109.
DOI Google Scholar
V Congradac, F Kulic (2009). HVAC system optimization with CO2 concentration control using genetic algorithms. Energy and Buildings, 41: 571-577.
DOI Google Scholar
DeST (2008). DeST-c User Manual. Beijing: Tsinghua University.
C Felsmann, G Knabe (2001). Simulation and optimal control strategies of HVAC systems. In: Proceedings of 7th International IBPSA Conference, Rio de Janeiro, Brazil, pp.1233-1239.
BA Flake (1998). Parameter estimation and optimal supervisory control of chilled water plants. PhD Dissertation, University of Wisconsin-Madison, USA.
KF Fong, VI Hanby, TT Chow (2006). HVAC system optimization for energy management by evolutionary programming. Energy and Buildings, 38: 220-231.
DOI Google Scholar
J Hyvärinen, S Kärki (1996). IEA Annex 25: Building Optimization and Fault Diagnosis Source Book. Espoo, Finland: Technical Research Centre of Finland.
X Jin, H Ren, X Xiao (2005). Prediction-based online optimal control of outdoor air of multi-zone VAV air conditioning systems. Energy and buildings, 37: 939-944.
DOI Google Scholar
M Kintner-Meyer, AF Emery (1995). Optimal control of an HVAC system using cold storage and building thermal capacitance. Energy and Buildings, 23: 19-31.
DOI Google Scholar
A Kusiak, M Li (2010). Cooling output optimization of an air handling unit. Applied Energy, 87: 901-909.
DOI Google Scholar
A Kusiak, M Li, F Tang (2010). Modeling and optimization of HVAC energy consumption. Applied Energy, 87: 3092-3102.
DOI Google Scholar
A Kusiak, GL Xu, F Tang (2011). Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm. Energy, 36: 5935-5943.
DOI Google Scholar
J Liang, R Du (2008). Design of intelligent comfort control system with human learning and minimum power control strategies. Energy Conversion and Management, 49: 517-528.
DOI Google Scholar
Y Liu, Y Pan, Z Huang (2013). Simulation-based receding-horizon supervisory control of HVAC system. In: Proceedings of 13th International IBPSA Conference, Chambéry, France, pp.1492-1498.
L Lu, W Cai, YC Soh, L Xie (2005a). Global optimization for overall HVAC systems—Part II problem solution and simulations. Energy Conversion and Management, 46: 1015-1028.
DOI Google Scholar
L Lu, W Cai, YC Soh, L Xie (2005b). Global optimization for overall HVAC systems—Part I problem formulation and analysis. Energy Conversion and Management, 46: 999-1014.
DOI Google Scholar
ZJ Ma, SW Wang (2009). An optimal control strategy for complex building chilled water systems for practical and real-time applications. Building and Environment, 44: 1188-1198.
DOI Google Scholar
DD Massie (2002). Optimization of a building's cooling plant for operating cost and energy use. International Journal of Thermal Sciences, 41: 1121-1129.
DOI Google Scholar
EH Mathews, CP Botha, DC Arndt, A Malan (2001). HVAC control strategies to enhance comfort and minimise energy usage. Energy and Buildings, 33: 853-863.
DOI Google Scholar
N Nassif, S Kajl, R Sabourin (2005). Optimization of HVAC control system strategy using two-objective genetic algorithm. HVAC & R Research, 11: 459-486.
DOI Google Scholar
RE Rink, N Li (1995). Aggregation/disaggregation method for optimal control of multizone HVAC systems. Energy Conversion and Management, 36: 79-86.
DOI Google Scholar
J Sun, A Reddy (2005). Optimal control of building HAVA&R systems using complete simulation-based sequential quadratic programming (CSR-SQP). Building and Environment, 40: 657-669.
DOI Google Scholar
Y Sun, G Huang, Z Li, S Wang (2013). Multiplexed optimization for complex air conditioning systems. Building and Environment, 65: 99-108.
DOI Google Scholar
RA Waltz, JL Morales, J Nocedal, D Orban (2006). An interior algorithm for nonlinear optimization that combines line search and trust region steps. Mathematical Programming, 107: 391-408.
DOI Google Scholar
S Wang (1999). Dynamic simulation of building VAV air-conditioning system and evaluation of EMCS on-line control strategies. Building and Environment, 34: 681-705.
DOI Google Scholar
Z Xue (2007). Energy-Saving Diagnosis and Retrofit in Existing Buildings. Beijing: China Architecture & Building Press. (in Chinese)
D Yan, J Xia, W Tang, F Song, X Zhang, Y Jiang (2008). DeST—An integrated building simulation toolkit, Part 1: Fundamentals. Building Simulation, 1: 95-110.
DOI Google Scholar
Y Yao, J Chen (2010). Global optimization of a central air-conditioning system using decomposition-coordination method. Energy and Buildings, 42: 570-583.
DOI Google Scholar
X Zhang, J Xia, Z Jiang, J Huang, R Qin, Y Zhang, Y Liu, Y Jiang (2008). DeST—An integrated building simulation toolkit, Part 2: Applications. Building Simulation, 1: 193-209.
DOI Google Scholar
GR Zheng, M Zaheer-Uddin (1996). Optimization of thermal processes in a variable air volume HVAC system. Energy, 21: 407-420.
DOI Google Scholar
Publication history
Copyright
Acknowledgements

Publication history

Received: 29 August 2013
Revised: 06 October 2013
Accepted: 25 October 2013
Published: 08 January 2014
Issue date: August 2014

Copyright

© Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2014

Acknowledgements

This work was supported in part by the Tsinghua-UTC Research Institute for Building Energy, Safety and Control System, Tsinghua National Laboratory for Information Science and Technology (TNList) Cross-discipline Foundation, and the National Natural Science Foundation of China (No. 61221063) and 111 International Collaboration Program of China. The authors would like to thank Zheng O’Neill, James Fan from United Technologies Research Center for their valuable comments.

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