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International Symposium on Neural Networks

ISNN 2019: Advances in Neural Networks – ISNN 2019 pp 120–128Cite as

Neurodynamics-Based Receding Horizon Control of an HVAC System

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11555))

Abstract

This paper addresses receding horizon control of a heating, ventilation, and air-conditioning (HVAC) system based on neurodynamic optimization. The receding horizon control problem for the HVAC system is formulated as sequential quadratic programs, which are solved by using a neurodynamic optimization model. Simulation results on temperature setpoint regulation of the HVAC system are discussed to substantiate the efficacy of the approach.

This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region of China, under Grants 11208517 and 11202318, in part by the National Natural Science Foundation of China under grants 61673330 and 61876105, and in part by International Partnership Program of Chinese Academy of Sciences under Grant GJHZ1849.

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References

  1. Afram, A., Janabi-Sharifi, F.: Theory and applications of HVAC control systems - a review of model predictive control (MPC). Build. Environ. 72, 343–355 (2014)

    Article  Google Scholar 

  2. Afram, A., Janabi-Sharifi, F., Fung, A.S., Raahemifar, K.: Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: a state of the art review and case study of a residential HVAC system. Energy Build. 141, 96–113 (2017)

    Article  Google Scholar 

  3. Aswani, A., Master, N., Taneja, J., Culler, D., Tomlin, C.: Reducing transient and steady state electricity consumption in HVAC using learning-based model-predictive control. Proc. IEEE 100(1), 240–253 (2012)

    Article  Google Scholar 

  4. Byrd, R., Hribar, M., Nocedal, J.: An interior point algorithm for large-scale nonlinear programming. SIAM J. Optim. 9(4), 877–900 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  5. Fong, K.F., Hanby, V.I., Chow, T.T.: System optimization for HVAC energy management using the robust evolutionary algorithm. Appl. Therm. Eng. 29(11), 2327–2334 (2009)

    Article  Google Scholar 

  6. Fong, K.F., Yuen, S.Y., Chow, C.K., Leung, S.W.: Energy management and design of centralized air-conditioning systems through the non-revisiting strategy for heuristic optimization methods. Appl. Energy 87(11), 3494–3506 (2010)

    Article  Google Scholar 

  7. Freire, R.Z., Oliveira, G.H., Mendes, N.: Predictive controllers for thermal comfort optimization and energy savings. Energy Build. 40(7), 1353–1365 (2008)

    Article  Google Scholar 

  8. Goddard, G., Klose, J., Backhaus, S.: Model development and identification for fast demand response in commercial HVAC systems. IEEE Trans. Smart Grid 5(4), 2084–2092 (2014)

    Article  Google Scholar 

  9. Hazyuk, I., Ghiaus, C., Penhouet, D.: Optimal temperature control of intermittently heated buildings using model predictive control: Part II - control algorithm. Build. Environ. 51, 388–394 (2012)

    Article  Google Scholar 

  10. Hu, X., Wang, J.: Design of general projection neural networks for solving monotone linear variational inequalities and linear and quadratic optimization problems. IEEE Trans. Syst. Man Cybern. Part B Cybern. 37(5), 1414–1421 (2007)

    Article  Google Scholar 

  11. Hu, X., Wang, J.: An improved dual neural network for solving a class of quadratic programming problems and its k-winners-take-all application. IEEE Trans. Neural Netw. 19(12), 2022–2031 (2008)

    Article  Google Scholar 

  12. Huang, H., Chen, L., Hu, E.: A neural network-based multi-zone modelling approach for predictive control system design in commercial buildings. Energy Build. 97, 86–97 (2015)

    Article  Google Scholar 

  13. Li, G., Yan, Z., Wang, J.: A one-layer recurrent neural network for constrained nonconvex optimization. Neural Netw. 61, 10–21 (2015)

    Article  MATH  Google Scholar 

  14. Liang, X., Wang, J.: A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints. IEEE Trans. Neural Netw. 11(6), 1251–1262 (2000)

    Article  Google Scholar 

  15. Liu, Q., Wang, J.: A one-layer recurrent neural network with a discontinuous hard-limiting activation function for quadratic programming. IEEE Trans. Neural Netw. 19(4), 558–570 (2008)

    Article  Google Scholar 

  16. Liu, Q., Wang, J.: A one-layer recurrent neural network for constrained nonsmooth optimization. IEEE Trans. Syst. Man Cybern. Part B Cybern. 40(5), 1323–1333 (2011)

    Google Scholar 

  17. Liu, S., Wang, J.: A simplified dual neural network for quadratic programming with its KWTA application. IEEE Trans. Neural Netw. 17(6), 1500–1510 (2006)

    Article  Google Scholar 

  18. Nocedal, J., Wright, S.J.: Numerical Optimization. Springer, New York (2006). https://doi.org/10.1007/978-0-387-40065-5

    Book  MATH  Google Scholar 

  19. Pan, Y., Wang, J.: Model predictive control of unknown nonlinear dynamical systems based on recurrent neural networks. IEEE Trans. Ind. Electron. 59(8), 3089–3101 (2012)

    Article  MathSciNet  Google Scholar 

  20. Peng, Z., Wang, D., Wang, J.: Predictor-based neural dynamic surface control for uncertain nonlinear systems in strict-feedback form. IEEE Trans. Neural Netw. Learn. Syst. 28(9), 2156–2167 (2017)

    MathSciNet  Google Scholar 

  21. Privara, S., Širokỳ, J., Ferkl, L., Cigler, J.: Model predictive control of a building heating system: the first experience. Energy Build. 43(2–3), 564–572 (2011)

    Article  Google Scholar 

  22. Qin, S., Le, X., Wang, J.: A neurodynamic optimization approach to bilevel quadratic programming. IEEE Trans. Neural Netw. Learn. Syst. 28(11), 2580–2591 (2017)

    Article  MathSciNet  Google Scholar 

  23. Rousseau, P., Mathews, E.: Needs and trends in integrated building and HVAC thermal design tools. Build. Environ. 28(4), 439–452 (1993)

    Article  Google Scholar 

  24. Teeter, J., Chow, M.Y.: Application of functional link neural network to HVAC thermal dynamic system identification. IEEE Trans. Ind. Electron. 45(1), 170–176 (1998)

    Article  Google Scholar 

  25. Wang, J.: A deterministic annealing neural network for convex programming. Neural Netw. 7(4), 629–641 (1994)

    Article  MATH  Google Scholar 

  26. Xia, Y., Feng, G., Wang, J.: A recurrent neural network with exponential convergence for solving convex quadratic program and related linear piecewise equations. Neural Netw. 17(7), 1003–1015 (2004)

    Article  MATH  Google Scholar 

  27. Xia, Y., Feng, G., Wang, J.: A novel recurrent neural network for solving nonlinear optimization problems with inequality constraints. IEEE Trans. Neural Netw. 19(8), 1340–1353 (2008)

    Article  Google Scholar 

  28. Xia, Y., Leung, H., Wang, J.: A projection neural network and its application to constrained optimization problems. IEEE Trans. Circuits Syst. Part I 49(4), 447–458 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  29. Xia, Y., Wang, J.: A general methodology for designing globally convergent optimization neural networks. IEEE Trans. Neural Netw. 9(6), 1331–1343 (1998)

    Article  Google Scholar 

  30. Xia, Y., Wang, J.: Global exponential stability of recurrent neural networks for solving optimization and related problems. IEEE Trans. Neural Netw. 11(4), 1017–1022 (2000)

    Article  Google Scholar 

  31. Xia, Y., Wang, J.: A general projection neural network for solving monotone variational inequalities and related optimization problems. IEEE Trans. Neural Netw. 15(2), 318–328 (2004)

    Article  Google Scholar 

  32. Xia, Y., Wang, J.: A recurrent neural network for nonlinear convex optimization subject to nonlinear inequality constraints. IEEE Trans. Circuits Syst. Part I 51(7), 1385–1394 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  33. Xia, Y., Wang, J.: A bi-projection neural network for solving constrained quadratic optimization problems. IEEE Trans. Neural Netw. Learn. Syst. 27(2), 214–224 (2016)

    Article  MathSciNet  Google Scholar 

  34. Yan, Z., Le, X., Wang, J.: Tube-based robust model predictive control of nonlinear systems via collective neurodynamic optimization. IEEE Trans. Ind. Electron. 63(7), 4377–4386 (2016)

    Article  Google Scholar 

  35. Yan, Z., Wang, J.: Model predictive control of nonlinear systems with unmodeled dynamics based on feedforward and recurrent neural networks. IEEE Trans. Ind. Inform. 8(4), 746–756 (2012)

    Article  Google Scholar 

  36. Yan, Z., Fan, J., Wang, J.: A collective neurodynamic approach to constrained global optimization. IEEE Trans. Neural Netw. Learn. Syst. 28(5), 1206–1215 (2017)

    Article  Google Scholar 

  37. Yan, Z., Wang, J.: Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks. IEEE Trans. Neural Netw. Learn. Syst. 25(3), 457–469 (2014)

    Article  MathSciNet  Google Scholar 

  38. Yan, Z., Wang, J.: Nonlinear model predictive control based on collective neurodynamic optimization. IEEE Trans. Neural Netw. Learn. Syst. 26(4), 840–850 (2015)

    Article  MathSciNet  Google Scholar 

  39. Yang, L., Nagy, Z., Goffin, P., Schlueter, A.: Reinforcement learning for optimal control of low exergy buildings. Appl. Energy 156, 577–586 (2015)

    Article  Google Scholar 

  40. Zhang, Y., Wang, J.: A dual neural network for convex quadratic programming subject to linear equality and inequality constraints. Phys. Lett. A 298, 271–278 (2002)

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

    Jiasen Wang & Jun Wang

  2. School of Data Science, City University of Hong Kong, Kowloon, Hong Kong

    Jun Wang

  3. Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China

    Jiasen Wang & Jun Wang

  4. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China

    Shenshen Gu

Authors
  1. Jiasen Wang
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  2. Jun Wang
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  3. Shenshen Gu
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Corresponding author

Correspondence to Jiasen Wang .

Editor information

Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Huchuan Lu

  2. Sichuan University, Chengdu, China

    Huajin Tang

  3. Northeastern University, Shenyang, China

    Zhanshan Wang

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Wang, J., Wang, J., Gu, S. (2019). Neurodynamics-Based Receding Horizon Control of an HVAC System. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_13

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  • DOI: https://doi.org/10.1007/978-3-030-22808-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22807-1

  • Online ISBN: 978-3-030-22808-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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