FMI real-time co-simulation-based machine deep learning control of HVAC systems in smart buildings: Digital-twins technology

Abstract

As heating, ventilation, and air conditioning (HVAC) systems have become one of the most contributing systems in energy consumption in the world, the control of these large-scale systems remains a challenging duty due to the decoupling effects of control variables. Accordingly, the penetration of these types of systems in all-smart buildings has increased in recent years. Furthermore, the application of digital twin as a fast-growing concept is being developed. In HVAC systems, independent and accurate control of temperature and humidity of the indoor air has been playing an undeniable role in reducing energy consumption. In this paper, to have cost-effective energy management in a single-zone HVAC system, a new reliable digital twin proximal policy optimization (PPO)–based model-independent nonsingular terminal sliding-mode control (MINTSMC) methodology has been proposed. Moreover, due to the nonlinear characteristics of HVAC systems, MINTSMC tends to handle the un-modeled system dynamics and disturbances. For regulating parameters of proposed control, an efficient PPO algorithm has been developed due to its actor-critic-based reinforcement learning. Extensive examinations and comparative analyses with particle swarm optimization designed sliding-mode control and proportional–integral–derivative controller have been made using digital twin of the proposed controller to show the importance, accuracy, and application of this method in the comfort and energy management achievement of HVAC control systems. A digital signal processor computing device has been utilized for implementation by utilizing hardware-in-loop (HIL) in the concept of the digital twin. To determine the interface between established models, software-in-loop, and HIL, the Functional Mock-up Interface has been utilized. The outcomes revealed a superior performance of suggested digital twin-based controller than the compared control methodologies in the compensation of unknown uncertainties, fast-tracking, and smooth response.

Keywords

HVAC system PPO algorithm nonsingular terminal sliding-mode controller hardware-in-loop digital twin (DT)

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References

Abrazeh

Parvaresh

Mohseni

S-R

, et al. (2021) Nonsingular terminal sliding mode control with ultra-local model and single input interval type-2 fuzzy logic control for pitch control of wind turbines. IEEE/CAA Journal of Automatica Sinica 8(3): 690–700.

Afram

Janabi-Sharifi

(2014) Theory and applications of HVAC control systems—A review of model predictive control (MPC). Building and Environment 72: 343–355.

Andersonetal

(2008) MIMO robust control for HVAC systems. IEEE Transactions on Control Systems Technology 16(3): 475–483.

Centomo

Lora

Fummi

(2020) Generation of functional mockup units for transactional cyber-physical virtual platforms. In: Kazmierski

Steinhorst

Große

(eds) Languages, Design Methods, and Tools for Electronic System Design. Cham: Springer, pp. 27–46.

Chi

P-H

Weng

F-B

, et al. (2006) Numerical modeling of proton exchange membrane fuel cell with considering thermal and relative humidity effects on the cell performance. Journal of Fuel Cell Science and Technology 3: 292–302.

Chiang

M-L

L-C

(2006) Adaptive and robust control for nonlinear HVAC system. In: 2006 IEEE international conference on systems, man and cybernetics, Taipei, 8–11 October, pp. 64982–64987. New York: IEEE.

Drees

(2019) Building management system with augmented deep learning using combined regression and artificial neural network modeling. Google Patents, patent grant number: 10935940, Cedarburg, WI, USA.

Grieves

(2014) Digital twin: Manufacturing excellence through virtual factory replication. White Paper 1: 1–7.

Hajihosseini

Andalibi

Gheisarnejad

, et al. (2020) DC/DC power converter control-based deep machine learning techniques: Real-time implementation. IEEE Transactions on Power Electronics 35(10): 9971–9977.

10.

Hendel

Khaber

Essounbouli

(2021) Adaptive high order sliding mode controller/observer based terminal sliding mode for MIMO uncertain nonlinear system. International Journal of Control 94: 486–506.

11.

Jahanshahi Zeitouni

Parvaresh

Abrazeh

, et al. (2020) Digital twins-assisted design of next-generation advanced controllers for power systems and electronics: Wind turbine as a case study. Inventions 5(2): 19.

12.

Jahedi

Ardehali

(2012) Wavelet based artificial neural network applied for energy efficiency enhancement of decoupled HVAC system. Energy Conversion and Management 54(1): 47–56.

13.

Jinzhi

Chen

Törngren

, et al. (2016) A model-driven and tool-integration framework for whole vehicle co-simulation environments. In: 8th European congress on embedded real time software and systems (ERTS 2016), Toulouse, January.

14.

Kang

C-S

Park

J-I

Park

, et al. (2014) Novel modeling and control strategies for a HVAC system including carbon dioxide control. Energies 7(6): 3599–3617.

15.

Khooban

Abadi

DNM

Alfi

, et al. (2014) Optimal type-2 fuzzy controller for HVAC systems. Automatika 55(1): 69–78.

16.

Kobayashi

(2021) Proximal policy optimization with relative Pearson divergence. In: IEEE international conference on robotics and automation (ICRA), Xi’an, China, 30 May–5 June, pp. 8416–8421. New York: IEEE.

17.

Zheng

, et al. (2014) Fast nonsingular integral terminal sliding mode control for nonlinear dynamical systems. In: 53rd IEEE conference on decision and control, Los Angeles, CA, 15–17 December, pp. 4739–4746. New York: IEEE.

18.

Mateev

(2020) Industry 40 and the digital twin for building industry. Industry 4.0 5(1): 29–32.

19.

Mittal

Tolk

Pyles

, et al. (2019) Digital twin modeling, co-simulation and cyber use-case inclusion methodology for IOT systems. In: 2019 winter simulation conference (WSC), National Harbor, MD, 8–11 December 2019, pp. 2653–2664. New York: IEEE.

20.

Mohammadi Moghadam

Foroozan

Gheisarnejad

, et al. (2021a) A survey on new trends of digital twin technology for power systems. Journal of Intelligent & Fuzzy Systems 41: 3873–3893.

21.

Mohammadi Moghadam

Gheisarnejad

Yalsavar

, et al. (2021b) A novel nonsingular terminal sliding mode control-based double interval type-2 fuzzy systems: Real-time implementation. Inventions 6(2): 40.

22.

Namatēvs

(2018) Deep reinforcement learning on HVAC control. Information Technology & Management Science 21. Available at: https://itms-journals.rtu.lv/article/view/itms-2018-0004

23.

Parvaresh

Abrazeh

Mohseni

S-R

, et al. (2020) A novel deep learning backstepping controller-based digital twins technology for pitch angle control of variable speed wind turbine. Designs 4(2): 15.

24.

Peng

Rysanek

Nagy

, et al. (2018) Using machine learning techniques for occupancy-prediction-based cooling control in office buildings. Applied Energy 211: 1343–1358.

25.

Pérez-Lombard

Ortiz

Pout

(2008) A review on buildings energy consumption information. Energy and Buildings 40(3): 394–398.

26.

Salazar

López

Rojano

(2007) A neural network model to predict temperature and relative humidity in a greenhouse. In: International symposium on high technology for greenhouse system management: Greensys 2007 801: 539–546.

27.

Schulman

Wolski

Dhariwal

, et al. (2017) Proximal policy optimization algorithms. arXiv preprint arXiv170706347.

28.

Schwan

Unger

Pipiorke

(2017) Aspects of FMI in building simulation. In: Proceedings of the 12th international Modelica conference, Prague, 15–17 May, vol. 132, pp. 73–78. Linköping: Linköping University Electronic Press.

29.

Kang

H-J

(2018) An adaptive terminal sliding mode control for robot manipulators with non-singular terminal sliding surface variables. IEEE Access 7: 8701–8712.

30.

Wei

Wang

Zhu

(2017) Deep reinforcement learning for building HVAC control. In: Proceedings of the 54th annual design automation conference 2017, Austin, TX, 18–22 June 2017, pp. 1–6. New York: IEEE.

31.

Yang

Shen

Gunay

, et al. (2019) Toward machine learning-based prognostics for heating ventilation and air-conditioning systems. ASHRAE Transactions 125: 106–115.

32.

Zavareh

Foroozan

Gheisarnejad

, et al. (2021) New trends on digital twin-based blockchain technology in zero-emission ship applications. Naval Engineers Journal 133(3): 115–135.

33.

Zhang

, et al. (2017) Sliding observer-based demagnetisation fault-tolerant control in permanent magnet synchronous motors. The Journal of Engineering 2017(6): 175–183.

34.

Zhao

Yin

Zhang

, et al. (2019) Robust model-free nonsingular terminal sliding mode control for PMSM demagnetization fault. IEEE Access 7: 15737–15748.