Global optimal control of variable air volume air-conditioning system with iterative learning: an experimental case study

详细信息    查看全文

• 作者：Qing-long Meng (1)
  Xiu-ying Yan (2)
  Qing-chang Ren (2)
  
  1. School of Environmental Science and Engineering ; Chang鈥檃n University ; Xi鈥檃n ; 710054 ; China
  2. School of Information and Control Engineering ; Xi鈥檃n University of Architecture & Technology ; Xi鈥檃n ; 710055 ; China
  
• 关键词：Air ; conditioning system ; Large scale systems ; Iterative learning control (ILC) ; Global optimization ; TK323 ; TP29 ; 绌鸿皟 ; 澶х郴缁?/li> 杩唬瀛︿範鎺у埗 ; 鍏ㄥ眬浼樺寲
• 刊名：Journal of Zhejiang University - Science A
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：16
• 期：4
• 页码：302-315
• 全文大小：1,577 KB
• 参考文献：1. Afram, A, Janabi-Sharifi, F (2014) Review of modeling methods for HVAC systems. Applied Thermal Engineering 67: pp. 507-519 CrossRef
  2. Anderson, M, Buehner, M, Young, P (2007) An experimental system for advanced heating, ventilating and air conditioning (HVAC) control. Energy and Buildings 39: pp. 136-147 CrossRef
  3. Barbosa, RM, Mendes, N (2008) Combined simulation of central HVAC systems with a whole-building hygrothermal model. Energy & Buildings 40: pp. 276-288 CrossRef
  4. Blume, PA (2007) The LabVIEW Style Book. Pearson Education, San Antonio, USA
  5. Cho, YH, Wang, G, Liu, M (2010) Application of terminal box optimization of single-duct air-handling units. International Journal of Energy Research 34: pp. 54-66 CrossRef
  6. Fong, KF, Hanby, VI, Chow, TT (2006) HVAC system optimization for energy management by evolutionary programming. Energy and Buildings 38: pp. 220-231 CrossRef
  7. Hang, CC, Sin, KK (1991) A comparative performance study of PID auto-tuners. Control Systems, IEEE 11: pp. 41-47 CrossRef
  8. Hu, Q, Albert, TP, Tse, WL (2005) Development of ANN-based models to predict the static response and dynamic response of a heat exchanger in a real HVAC system. Journal of Physics: Conference Series 23: pp. 110-121
  9. Huang, W, Zaheeruddin, M, Cho, SH (2006) Dynamic simulation of energy management control functions for HVAC systems in buildings. Energy Conversion and Management 47: pp. 926-943 CrossRef
  10. Kulhav媒, R (1987) Restricted exponential forgetting in realtime identification. Automatica 23: pp. 589-600 CrossRef
  11. Kusiak, A, Tang, F, Xu, G (2011) Multi-objective optimization of HVAC system with an evolutionary computation algorithm. Energy 36: pp. 2440-2449 CrossRef
  12. Kwok, SSK, Lee, EWM (2011) A study of the importance of occupancy to building cooling load in prediction by intelligent approach. Energy Conversion and Management 52: pp. 2555-2564 CrossRef
  13. Lennart, L (1999) System Identification-Theory for the User. Prentice Hall, New Jersey, USA
  14. Ma, Z, Wang, S (2011) Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm. Applied Energy 88: pp. 198-211 CrossRef
  15. Mossolly, M, Ghali, K, Ghaddar, N (2009) Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm. Energy 34: pp. 58-66 CrossRef
  16. Myhren, JA, Holmberg, S (2008) Flow patterns and thermal comfort in a room with panel, floor and wall heating. Energy and Buildings 40: pp. 524-536 CrossRef
  17. Nassif, N (2010) Performance analysis of supply and return fans for HVAC systems under different operating strategies of economizer dampers. Energy and Buildings 42: pp. 1026-1037 CrossRef
  18. Nassif, N, Moujaes, S (2008) A new operating strategy for economizer dampers of VAV system. Energy and Buildings 40: pp. 289-299 CrossRef
  19. Nassif, N, Moujaes, S, Zaheeruddin, M (2008) Self-tuning dynamic models of HVAC system components. Energy and Buildings 40: pp. 1709-1720 CrossRef
  20. Platt, G, Li, J, Li, R (2010) Adaptive HVAC zone modeling for sustainable buildings. Energy and Buildings 42: pp. 412-421 CrossRef
  21. Ruan, XE, Bien, ZZ, Park, KH (2008) Decentralized iterative learning control to large-scale industrial processes for nonrepetitive trajectory tracking. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 38: pp. 238-252 CrossRef
  22. Svrcek, WY, Mahoney, DP, Young, BR (2006) A Realtime Approach to Process Control. John Wiley & Sons, New Jersey, USA CrossRef
  23. Wan, BW (2003) Industrial Large-scale System Optimization & Product Quality Control. Science Press, Beijing, China
  24. Wang, SW, Ma, ZJ (2008) Supervisory and optimal control of building HVAC systems: a review. HVAC&R Research 14: pp. 3-32 CrossRef
  25. Wang, SW, Sun, Z, Sun, Y (2011) Online optimal ventilation control of building air-conditioning systems. Indoor and Built Environment 20: pp. 129-136 CrossRef
  26. West, SR, Ward, JK, Wall, J (2014) Trial results from a model predictive control and optimisation system for commercial building HVAC. Energy and Buildings 72: pp. 271-279 CrossRef
  27. Yan, X, Ren, Q, Meng, Q (2010) Global optimization of VAV air conditioning system. Proceeding of the 8th World Congress on Intelligent Control and Automation, Jinan, China. pp. 5077-5081
  28. Yan, X, Ren, Q, Meng, Q (2010) Iterative learning control in large scale HVAC system. Proceeding of the 8th World Congress on Intelligent Control and Automation, Jinan, China. pp. 5063-5066
  29. Zhang, HG, Cai, LL (2002) Decentralized nonlinear adaptive control of an HVAC system. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 32: pp. 493-498 CrossRef
  
• 刊物类别：Engineering
• 刊物主题：Physics
  Mechanics, Fluids and Thermodynamics
  Chinese Library of Science
  
• 出版者：Zhejiang University Press, co-published with Springer
• ISSN：1862-1775

文摘

The air-conditioning system in a large commercial or high-rise building is a complex multi-variable system influenced by many factors. The energy saving potential from the optimal operation and control of heating, ventilating, and air-conditioning (HVAC) systems can be large, even when they are properly designed. The ultimate goal of optimization is to use the minimum amount of energy needed to improve system efficiency while meeting comfort requirements. In this study, a multi-zone variable air volume (VAV) and variable water volume (VWV) air-conditioning system is developed. The steady state modes and dynamic models of the HVAC subsystems are constructed. Optimal control based on large scale system theory for system-level energy-saving of HVAC is introduced. Control strategies such as proportional-integral-derivative (PID) controller (gearshift integral PID and self-tuning PID) and iterative learning control (ILC) are studied in the platform to improve the dynamic characteristics. The system performance is improved. An 18.2% energy saving is achieved with the integration of ILC and sequential quadratic programming based on a steady-state hierarchical optimization control scheme.