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Optimization of BuildingsEnergy Consumption by Designing Sliding Mode Control for Multizone VAV Air Conditioning Systems

Author

Listed:
  • Awais Shah

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Deqing Huang

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Tianpeng Huang

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Umar Farid

    (Department of Electrical Engineering, Comsats Institute of Information and Technology, Abbottabad 22010, Pakistan)

Abstract

Variable air volume (VAV) is the most common installation among heating ventilating and air conditioning (HVAC) systems. To maintain the comfort level and lessen energy utilization, there is a pressing need for its effective control. In this study, a lumped parameter model composed of multizone VAV is considered, and sliding mode control (SMC) is designed to guarantee robust operation in the presence of uncertainties. For comparison of the proposed controller performance, a proportional integral derivative (PID) controller is additionally designed. The indoor temperature of zones is controlled by positioning the supply air dampers. Tracking objectives of controllers are inspected via two practical cases of desired temperature setpoints including (a) sinusoidal waveform and (b) the combination of steps. Results obtained using SMC ensure the robust operation of the VAV system against parametric uncertainties. In addition, SMC is more energy efficient than PID in terms of overshoot and settling time.

Suggested Citation

  • Awais Shah & Deqing Huang & Tianpeng Huang & Umar Farid, 2018. "Optimization of BuildingsEnergy Consumption by Designing Sliding Mode Control for Multizone VAV Air Conditioning Systems," Energies, MDPI, vol. 11(11), pages 1-18, October.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2911-:d:178320
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    References listed on IDEAS

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    1. Clemente García Cutillas & Javier Ruiz Ramírez & Manuel Lucas Miralles, 2017. "Optimum Design and Operation of an HVAC Cooling Tower for Energy and Water Conservation," Energies, MDPI, vol. 10(3), pages 1-27, March.
    2. Farinaz Behrooz & Norman Mariun & Mohammad Hamiruce Marhaban & Mohd Amran Mohd Radzi & Abdul Rahman Ramli, 2018. "Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps," Energies, MDPI, vol. 11(3), pages 1-41, February.
    3. Chua, K.J. & Chou, S.K. & Yang, W.M. & Yan, J., 2013. "Achieving better energy-efficient air conditioning – A review of technologies and strategies," Applied Energy, Elsevier, vol. 104(C), pages 87-104.
    4. Yaser Imad Alamin & María Del Mar Castilla & José Domingo Álvarez & Antonio Ruano, 2017. "An Economic Model-Based Predictive Control to Manage the Users’ Thermal Comfort in a Building," Energies, MDPI, vol. 10(3), pages 1-18, March.
    5. Chang-Soon Kang & Jong-Il Park & Mignon Park & Jaeho Baek, 2014. "Novel Modeling and Control Strategies for a HVAC System Including Carbon Dioxide Control," Energies, MDPI, vol. 7(6), pages 1-19, June.
    6. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    7. Awais Shah & Deqing Huang & Yixing Chen & Xin Kang & Na Qin, 2017. "Robust Sliding Mode Control of Air Handling Unit for Energy Efficiency Enhancement," Energies, MDPI, vol. 10(11), pages 1-21, November.
    8. Okochi, Godwine Swere & Yao, Ye, 2016. "A review of recent developments and technological advancements of variable-air-volume (VAV) air-conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 784-817.
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    Cited by:

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    3. Ali Hamza & Muhammad Uneeb & Iftikhar Ahmad & Komal Saleem & Zunaib Ali, 2023. "Variable Structure-Based Control for Dynamic Temperature Setpoint Regulation in Hospital Extreme Healthcare Zones," Energies, MDPI, vol. 16(10), pages 1-27, May.

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