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Building Simulation 2020, 13(1): 155-170 https://doi.org/10.1007/s12273-019-0569-6
Research Article | Issue | Published: 23 September 2019

A decentralized method for energy conservation of an HVAC system

Show Author's Information Luping Zhuang1,3,  Xi Chen1( ),  Xiaohong Guan1,2
Department of Automation, Tsinghua University, Beijing 100084, China
MOE KLINNS Lab, Xi’an Jiaotong University, Xi’an 710049, China
SINOPEC Yanshan Petrochemical Company, Beijing 102500, China
Keywords:
energy consumption, energy conservation, HVAC, building and energy
Cite this article:
Zhuang L, Chen X, Guan X. A decentralized method for energy conservation of an HVAC system. Building Simulation, 2020, 13(1): 155-170. https://doi.org/10.1007/s12273-019-0569-6
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Abstract Full text Electronic supplementary material About this article

Building, heating, ventilation, and air-conditioning (HVAC) consumes nearly 48% of the entire building energy of the world. Improvement in the control strategy of an HVAC system can result in substantial energy savings, which is the motivation behind this research. In this study, a decentralized method is proposed to search for a good-enough control strategy to reduce the energy consumption of a typical HVAC system consisting of cooling towers, chillers, pumps, and air handling units (AHUs). The method is then compared to a centralized method. In addition to energy consumption, indoor environmental factors such as temperature and humidity are considered in the new method to meet the requirements of human comfort. We introduce experimental conditions for feasibility and the crude energy index in the decentralized method to reduce the computational time. The improved HVAC system was able to save 34% (per 12 h) on energy consumption and the average computational time was reduced to 1247.5 s, which proves the efficiency and effectiveness of the decentralized method and the performance of the proposed control strategy.


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Electronic supplementary material
About this article

A decentralized method for energy conservation of an HVAC system

Show Author's information Luping Zhuang1,3, Xi Chen1( ), Xiaohong Guan1,2
Department of Automation, Tsinghua University, Beijing 100084, China
MOE KLINNS Lab, Xi’an Jiaotong University, Xi’an 710049, China
SINOPEC Yanshan Petrochemical Company, Beijing 102500, China

Abstract

Building, heating, ventilation, and air-conditioning (HVAC) consumes nearly 48% of the entire building energy of the world. Improvement in the control strategy of an HVAC system can result in substantial energy savings, which is the motivation behind this research. In this study, a decentralized method is proposed to search for a good-enough control strategy to reduce the energy consumption of a typical HVAC system consisting of cooling towers, chillers, pumps, and air handling units (AHUs). The method is then compared to a centralized method. In addition to energy consumption, indoor environmental factors such as temperature and humidity are considered in the new method to meet the requirements of human comfort. We introduce experimental conditions for feasibility and the crude energy index in the decentralized method to reduce the computational time. The improved HVAC system was able to save 34% (per 12 h) on energy consumption and the average computational time was reduced to 1247.5 s, which proves the efficiency and effectiveness of the decentralized method and the performance of the proposed control strategy.

Keywords: energy consumption, energy conservation, HVAC, building and energy

References(27)

Abdalla EAH, Nallagownden P, Nor NM, Romlie MF, Abdalsalam ME, Muthuvalu MS (2016). Intelligent approach for optimal energy management of chiller plant using fuzzy and PSO techniques. In: Proceedings of the 6th International Conference on Intelligent and Advanced Systems, Kuala Lumpur, Malaysia.
DOI
Athienitis AK, Tzempelikos A (2001). A methodology for detailed calculation of room illuminance levels and light dimming in a room with motorized blinds integrated in an advanced window. In: Proceedings of the National Building Simulation Conference, Ottawa, Canada.
Bee E, Prada A, Baggio P, Psimopoulos E (2019). Air-source heat pump and photovoltaic systems for residential heating and cooling: Potential of self-consumption in different European climates. Building Simulation, 12: 453-463.
DOI Google Scholar
Brooks J, Kumar S, Goyal S, Subramany R, Barooah P (2015). Energy- efficient control of under-actuated HVAC zones in commercial buildings. Energy and Buildings, 93: 160-168.
DOI Google Scholar
Chang I-S, Wang X, Wei Q-F (2008). The COP index of chiller in large public buildings: Measurement and application. In: Proceedings of the Academic Annual Conference on the National HVAC. (in Chinese)
Dai Y-C, Jiang Z-Y, Shen Q, Chen PZ, Wang SQ, Jiang Y (2016). A decentralized algorithm for optimal distribution in HVAC systems. Building and Environment, 95: 21-31.
DOI Google Scholar
Dai Y-C, Jiang Z-Y, Wang S (2017). Decentralized control of parallel- connected chillers. Energy Procedia, 122: 86-91.
DOI Google Scholar
DeST (2008). DeST Software. Tsinghua University. Available at http://www.dest.com.cn.
He S, Gurgenci H, Guan Z, Hooman K, Zou Z, Sun F (2016). Comparative study on the performance of natural draft dry, pre-cooled and wet cooling towers. Applied Thermal Engineering, 99: 103-113.
DOI Google Scholar
Ho Y-C, Zhao Q-C, Jia Q-S (2007). Ordinal Optimization: Soft Optimization for Hard Problems. New York: Springer.
DOI
IEA (2018). World Energy Investment 2018. International Energy Agency. Available at https://www.iea.org.
Liu Z, Chen X, Xu X, Guan X (2013). A decentralized optimization method for energy saving of HVAC system. In: Proceedings of IEEE International Conference on Automation Science and Engineering (CASE), Madison, WI, USA.
DOI
Liu Z, Song F, Jiang Z, Chen X, Guan X (2014). Optimization based integrated control of building HVAC system. Building Simulation, 7: 375-387.
DOI Google Scholar
Liu Z-H, Tan H-W, Luo D, Yu G-B, Li J, Li Z-Y (2017). Optimal chiller sequencing control in an office building considering the variation of chiller maximum cooling capacity. Energy and Buildings, 140: 430-442.
DOI Google Scholar
Lu L, Cai W-J, Chai Y-S, Xie L-H (2005a). Global optimization for overall HVAC systems—Part I problem formulation and analysis. Energy Conversion and Management, 46: 999-1014.
DOI Google Scholar
Lu L, Cai W-J, Chai Y-S, Xie L-H (2005b). Global optimization for overall HVAC systems—Part II problem solution and simulations. Energy Conversion and Management, 46: 1015-1028.
DOI Google Scholar
Su Y, Su R, Polla K (2014). Distributed scheduling for efficient HVAC pre-cooling operations. In: Proceedings of the 19th World Congress of the International Federation of Automatic Control, Cape Town, South Africa, pp. 10451-1456.
DOI
Sun B, Luh P-B, Jia Q-S, Jiang Z-Y, Wang F-L, Song C (2013). Building energy management: integrated control of active and passive heating, cooling, lighting, shading, and ventilation systems. IEEE Transactions on Automation Science and Engineering, 10: 588-602.
DOI
Tsinghua University (2010). Annual Report on China’s Building Energy Efficiency. Beijing: China Architecture & Building Press. (in Chinese)
Wu D, Jia Q-S (2014). Sample path sharing in policy improvement for indoor air temperature control. IFAC Proceedings Volumes, 47: 247-252.
DOI Google Scholar
Xu X, Jia Q, Xu Z, Zhang B, Guan X (2016). On joint control of heating, ventilation, and air conditioning and natural ventilation in a meeting room for energy saving. Asian Journal of Control, 18: 1781-1804.
DOI Google Scholar
Xue Z-F, Jiang Y (2007). Energy-Saving Diagnosis and Retrofit in Existing Building. Beijing: China Architecture & Building Press. (in Chinese)
Yao Y, Chen J (2010). Global optimization of a central air-conditioning system using decomposition-coordination method. Energy and Buildings, 42: 570-583.
DOI Google Scholar
Zhang H-G, Cai L-L (2002). Decentralized nonlinear adaptive control of an HVAC system. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 32: 493-498.
DOI
Zhao RY, Fan CY, Xue DH (2009). Air Conditioning. Beijing: China Architecture & Building Press. (in Chinese)
Zhuang L, Chen X, Guan X (2017). A bi-level optimization for an HVAC system. Cluster Computing, 20: 3237-3249.
DOI Google Scholar
Zhuang L, Chen X, Guan X (2018). Regression model of heat exchange efficiency of cooling tower in an HVAC system, Control and Decision, 33: 1801-1806. (in Chinese)
Google Scholar
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Publication history
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Acknowledgements

Publication history

Received: 11 January 2019
Accepted: 17 June 2019
Published: 23 September 2019
Issue date: February 2020

Copyright

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Acknowledgements

This work was supported in part by the National Key Research and Development Program of China (2016YFB0901900 and 2017YFC0704100).

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