0
Research Papers

Energy Efficiency and Tracking Performance Evaluation for Dual-Mode Model Predictive Control of HVAC Systems

[+] Author and Article Information
Zicheng Cai

Department of Mechanical Engineering,
The University of Texas at Austin,
Austin, TX 78705
e-mail: zichengcai@utexas.edu

Asad A. Ul Haq

Department of Mechanical Engineering,
The University of Texas at Austin,
Austin, TX 78705
e-mail: asadulhaq@utexas.edu

Michael E. Cholette

Science and Engineering Faculty,
Queensland University of Technology,
Brisbane 4001, QLD, Australia
e-mail: michael.cholette@qut.edu.au

Dragan Djurdjanovic

Department of Mechanical Engineering,
The University of Texas at Austin,
Austin, TX 78705
e-mail: dragand@me.utexas.edu

1Corresponding author.

Contributed by the Heat Transfer Division of ASME for publication in the JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS. Manuscript received November 29, 2017; final manuscript received April 7, 2018; published online June 22, 2018. Assoc. Editor: Amir Jokar.

J. Thermal Sci. Eng. Appl 10(4), 041023 (Jun 22, 2018) (10 pages) Paper No: TSEA-17-1464; doi: 10.1115/1.4040281 History: Received November 29, 2017; Revised April 07, 2018

This paper presents evaluation of the energy consumption and tracking performance associated with the use of a recently introduced dual-mode model predictive controller (DMMPC) for control of a heating, ventilation, and air conditioning (HVAC) system. The study was conducted using detailed simulations of an HVAC system, which included a multizone air loop, a water loop, and a chiller. Energy consumption and tracking performance are computed from the simulations and evaluated in the presence of different types and magnitudes of noise and disturbances. Performance of the DMMPC is compared with a baseline proportional-integral-derivative (PID) control structure commonly used for HVAC system control, and this comparison showed clear and consistent superiority of the DMMPC.

FIGURES IN THIS ARTICLE
<>
Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.

References

EIA, 2011, “ Residential Energy Consumption Survey (RECS)—Analysis and Projections—U.S. Energy Information Administration (EIA),” U.S. Energy Information Administration, Washington, DC, accessed June 1, 2017, https://www.eia.gov/consumption/residential/reports/2009/air-conditioning.php
EIA, 2015, “ How Much Energy Is Consumed in Residential and Commercial Buildings in the United States?—FAQ—U.S. Energy Information Administration (EIA),” U.S. Energy Information Administration, Washington, DC, Report. https://www.eia.gov/tools/faqs/faq.php?id=86&t=1
EIA, 2012, “ Energy Information Administration (EIA)—Commercial Buildings Energy Consumption Survey (CBECS) Data,” U.S. Energy Information Administration, Washington, DC, accessed June 1, 2017, https://www.eia.gov/consumption/commercial/data/2012/
Pérez-Lombard, L. , Ortiz, J. , and Pout, C. , 2008, “ A Review on Buildings Energy Consumption Information,” Energy Build., 40(3), pp. 394–398. [CrossRef]
Fadzli Haniff, M. , Selamat, H. , Yusof, R. , Buyamin, S. , and Sham Ismail, F. , 2013, “ Review of HVAC Scheduling Techniques for Buildings Towards Energy-Efficient and Cost-Effective Operations,” Renewable Sustainable Energy Rev., 27, pp. 94–103. [CrossRef]
Khalajzadeh, V. , Farmahini-Farahani, M. , and Heidarinejad, G. , 2012, “ A Novel Integrated System of Ground Heat Exchanger and Indirect Evaporative Cooler,” Energy Build., 49, pp. 604–610. [CrossRef]
Dincer, I. , 2002, “ On Thermal Energy Storage Systems and Applications in Buildings,” Energy Build., 34(4), pp. 377–388. [CrossRef]
McQuiston, F. C. , Parker, J. D. , and Spitler, J. D. , 2005, Heating, Ventilating and Air Conditioning: Analysis and Design, Wiley, New York.
Afram, A. , and Janabi-Sharifi, F. , 2014, “ Theory and Applications of HVAC Control Systems – A Review of Model Predictive Control (MPC),” Build. Environ., 72, pp. 343–355. [CrossRef]
Huang, W. Z. , Zaheeruddin, M. , and Cho, S. H. , 2006, “ Dynamic Simulation of Energy Management Control Functions for HVAC Systems in Buildings,” Energy Convers. Manage., 47(7–8), pp. 926–943. [CrossRef]
Khanpara, J. C. , and Pickles, E. C. , 1999, “ Fan Motor on/Off Control System for a Refrigeration Appliance,” Whirlpool Corp., Benton Harbor, MI, U.S. Patent No. 5918474. https://patents.google.com/patent/US5918474
Wang, S. , and Ma, Z. , 2008, “ Supervisory and Optimal Control of Building HVAC Systems: A Review,” HVACR Res., 14(1), pp. 3–32. [CrossRef]
Salsbury, T. I. , 2005, “ A Survey of Control Technologies in the Building Automation Industry,” IFAC Proc. 38(1), pp. 90–100. [CrossRef]
Wang, Q. G. , Lee, T. H. , Fung, H. W. , Bi, Q. , and Zhang, Y. , 1999, “ PID Tuning for Improved Performance,” IEEE Trans. Control Syst. Technol., 7(4), pp. 457–465. [CrossRef]
Guo, W. , and Zhou, M. , 2009, “ Technologies Toward Thermal Comfort-Based and Energy-Efficient HVAC Systems: A Review,” IEEE International Conference on Systems, Man and Cybernetics, San Antonio, TX, Oct. 11–14, pp. 3883–3888.
Bi, Q. , Cai, W. J. , Wang, Q. G. , Hang, C. C. , E.-L., Lee, Sun , Y., Liu , K. D., Zhang , Y. , and Zou, B. , 2000, “ Advanced Controller Auto-Tuning and Its Application in HVAC Systems,” Control Eng. Pract., 8(6), pp. 633–644. [CrossRef]
Han, J. , 2009, “ From PID to Active Disturbance Rejection Control,” IEEE Trans. Ind. Electron., 56(3), pp. 900–906.
Shen, J. C. , 2001, “ Fuzzy Neural Networks for Tuning PID Controller for Plants With Underdamped Responses,” IEEE Trans. Fuzzy Syst., 9(2), pp. 333–342. [CrossRef]
Wu, J. , and Cai, W. J. , 2000, “ Development of an Adaptive Neuro-Fuzzy Method for Supply Air Pressure Control in HVAC System,” SMC IEEE Int. Conf. Syst. Man Cybern., 1–5, pp. 3806–3809.
Veronesi, M. , and Visioli, A. , 2009, “ Performance Assessment and Retuning of PID Controllers,” Ind. Eng. Chem. Res., 48(5), pp. 2616–2623. [CrossRef]
Zheng, G. R. , 1997, “ Dynamic Modeling and Global Optimal Operation of Multizone Variable Air Volume HVAC Systems,” Ph.D. thesis, Concordia University, Montreal, QC, Canada. http://www.nlc-bnc.ca/obj/s4/f2/dsk3/ftp04/nq25927.pdf
García, C. E. , Prett, D. M. , and Morari, M. , 1989, “ Model Predictive Control: Theory and Practice-a Survey,” Automatica, 25(3), pp. 335–348. [CrossRef]
Nowak, M. , and Urbaniak, A. , 2011, “ Utilization of Intelligent Control Algorithms for Thermal Comfort Optimization and Energy Saving,” 12th IEEE Control Conference (ICCC), Velke Karlovice, Czech Republic, May 25–28, pp. 270–274.
Mirinejad, H. , Welch, K. C. , and Spicer, L. , 2012, “ A Review of Intelligent Control Techniques in HVAC Systems,” IEEE Energytech, Cleveland, OH, May 29–31, pp. 1–5.
Ul Haq, A. A. , Cholette, M. E. , and Djurdjanovic, D. , 2017, “ A Dual-Mode Model Predictive Control Algorithm Trajectory Tracking in Discrete-Time Nonlinear Dynamic Systems,” ASME J. Dyn. Syst., Meas., Control, 139(4), p. 044501. [CrossRef]
Wagner, W. , and Pruß, A. , 2002, “ The IAPWS Formulation 1995 for the Thermodynamic Properties of Ordinary Water Substance for General and Scientific Use,” J. Phys. Chem. Ref. Data, 31(2), p. 429. [CrossRef]
Bertsekas, D. , 1995, Dynamic Programming and Optimal Control, Vol. 1, Athena Scientific, Belmont, MA.
Fong, K. F. , Hanby, V. I. , and Chow, T. T. , 2006, “ HVAC System Optimization for Energy Management by Evolutionary Programming,” Energy Build., 38(3), pp. 220–231. [CrossRef]
Butler, D. S. , Stanke, D. A. , Persily, A. K. , Apte, M. G. , Bellenger, L. G. , Bowman, J. D. , Cagwin, D. J. , Coggins, J. L. , Fanger, P. O. , Fisher, F. J. , Gallo, F. M. , Groah, W. J. , Halliwell, J. L. , Houston, T. P. , Howard, E. P. , Joeckel, R. T. , Morris, R. A. , Muller, C. O. , Navas, G. A. , Hogan, J. F. , and Jakob, F. E. , 2004, “ Ventilation for Acceptable Indoor Air Quality,” ASHRAE, Atlanta, GA, ASHRAE Standard No. 62.1-2004. https://www.ashrae.org/technical-resources/bookstore/standards-62-1-62-2
Kusiak, A. , Li, M. , and Tang, F. , 2010, “ Modeling and Optimization of HVAC Energy Consumption,” Appl. Energy, 87(10), pp. 3092–3102. [CrossRef]
Blevins, T. L. , 2012, “ PID Advances in Industrial Control,” IFAC Proc., 45(3), pp. 23–28.
Murphy, J. , and Maldeis, N. , 2009, “ Using Time-of-Day Scheduling to Save Energy,” ASHRAE J., 51(5), pp. 42–48. https://www.trane.com/commercial/Uploads/pdf/newsRoom/May2009ASHRAE.pdf
Ferhatbegovic, T. , Zucker, G. , and Palensky, P. , 2012, “ An Unscented Kalman Filter Approach for the Plant-Model Mismatch Reduction in HVAC System Model Based Control,” Industrial Electronics Conference (IECON), Montreal, QC, Canada, Oct. 25–28, pp. 2180–2185.
Burdick, A. , 2011, “ Advanced Strategy Guideline: Air Distribution Basics and Duct Design,” U.S. Department of Energy, Washington, DC, Report No. DOE/GO-102011-3461. https://www.nrel.gov/docs/fy12osti/53352.pdf
Wemhoff, A. P. , 2012, “ Calibration of HVAC Equipment PID Coefficients for Energy Conservation,” Energy Build., 45, pp. 60–66. [CrossRef]
Rieger, C. G. , 2008, “ Advanced Control Strategies for HVAC Systems in Critical Building Structures,” Idaho State University, Idaho Falls, ID.

Figures

Grahic Jump Location
Fig. 1

Schematic of the centralized HVAC system

Grahic Jump Location
Fig. 2

Schematic of the air flow subsystem

Grahic Jump Location
Fig. 3

Reference temperature trajectories

Grahic Jump Location
Fig. 4

Root-mean-square error calculated from DMMPC and PID tracking results without noise or disturbance

Grahic Jump Location
Fig. 5

Energy consumption calculated from DMMPC and PID tracking results without noise or disturbance

Grahic Jump Location
Fig. 6

Root-mean-square error calculated from DMMPC and PID tracking results in the presence of air flow and water flow subsystem process noise (γ=0.01)

Grahic Jump Location
Fig. 7

Total energy consumption in the presence of air flow and water flow subsystem process noise (γ=0.01)

Grahic Jump Location
Fig. 8

Root-mean-square error calculated from DMMPC and PID tracking results in the presence of air flow subsystem fault (θ = 0.02)

Grahic Jump Location
Fig. 9

Total energy consumption in the presence of air flow subsystem fault (θ = 0.02)

Grahic Jump Location
Fig. 10

Root-mean-square error calculated from DMMPC and PID tracking results in the presence of pulse disturbance

Grahic Jump Location
Fig. 11

Total energy consumption in the presence of pulse disturbance

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In