IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i11p2911-d178320.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/11/2911/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/11/2911/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mpho J. Lencwe & SP Daniel Chowdhury & Sipho Mahlangu & Maxwell Sibanyoni & Louwrance Ngoma, 2021. "An Efficient HVAC Network Control for Safety Enhancement of a Typical Uninterrupted Power Supply Battery Storage Room," Energies, MDPI, vol. 14(16), pages 1-23, August.
    2. Jieun Lee & Seokkwon Jeong, 2021. "Robust Temperature Control of a Variable-Speed Refrigeration System Based on Sliding Mode Control with Optimal Parameters Derived Using the Genetic Algorithm," Energies, MDPI, vol. 14(19), pages 1-18, October.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    2. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2020. "Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system," Applied Energy, Elsevier, vol. 257(C).
    3. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2021. "Experiment study of machine-learning-based approximate model predictive control for energy-efficient building control," Applied Energy, Elsevier, vol. 288(C).
    4. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    5. Cheng, Fanyong & Cui, Can & Cai, Wenjian & Zhang, Xin & Ge, Yuan & Li, Bingxu, 2022. "A novel data-driven air balancing method with energy-saving constraint strategy to minimize the energy consumption of ventilation system," Energy, Elsevier, vol. 239(PB).
    6. Serafín Alonso & Antonio Morán & Miguel Ángel Prada & Perfecto Reguera & Juan José Fuertes & Manuel Domínguez, 2019. "A Data-Driven Approach for Enhancing the Efficiency in Chiller Plants: A Hospital Case Study," Energies, MDPI, vol. 12(5), pages 1-28, March.
    7. Yang, Shiyu & Wan, Man Pun, 2022. "Machine-learning-based model predictive control with instantaneous linearization – A case study on an air-conditioning and mechanical ventilation system," Applied Energy, Elsevier, vol. 306(PB).
    8. Ahmad Esmaeilzadeh & Brian Deal & Aghil Yousefi-Koma & Mohammad Reza Zakerzadeh, 2022. "How Multi-Criterion Optimized Control Methods Improve Effectiveness of Multi-Zone Building Heating System Upgrading," Energies, MDPI, vol. 15(22), pages 1-27, November.
    9. 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.
    10. Mpho J. Lencwe & SP Daniel Chowdhury & Sipho Mahlangu & Maxwell Sibanyoni & Louwrance Ngoma, 2021. "An Efficient HVAC Network Control for Safety Enhancement of a Typical Uninterrupted Power Supply Battery Storage Room," Energies, MDPI, vol. 14(16), pages 1-23, August.
    11. Whei-Min Lin & Chung-Yuen Yang & Ming-Tang Tsai & Hong-Jey Gow, 2019. "The Optimized Energy Saving of a Refrigerating Chamber," Energies, MDPI, vol. 12(10), pages 1-16, May.
    12. Zbigniew Buryn & Anna Kuczuk & Janusz Pospolita & Rafał Smejda & Katarzyna Widera, 2021. "Impact of Weather Conditions on the Operation of Power Unit Cooling Towers 905 MWe," Energies, MDPI, vol. 14(19), pages 1-19, October.
    13. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    14. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    15. Hyo-Jun Kim & Young-Hum Cho, 2021. "Optimal Control Method of Variable Air Volume Terminal Unit System," Energies, MDPI, vol. 14(22), pages 1-15, November.
    16. Niamsuwan, Sathit & Kittisupakorn, Paisan & Suwatthikul, Ajaree, 2015. "Enhancement of energy efficiency in a paint curing oven via CFD approach: Case study in an air-conditioning plant," Applied Energy, Elsevier, vol. 156(C), pages 465-477.
    17. Anass Berouine & Radouane Ouladsine & Mohamed Bakhouya & Mohamed Essaaidi, 2020. "Towards a Real-Time Predictive Management Approach of Indoor Air Quality in Energy-Efficient Buildings," Energies, MDPI, vol. 13(12), pages 1-16, June.
    18. Svetlana Ratner & Yuri Chepurko & Larisa Drobyshecskaya & Anna Petrovskaya, 2018. "Management of Energy Enterprises: Energy-efficiency Approach in Solar Collectors Industry: The Case of Russia," International Journal of Energy Economics and Policy, Econjournals, vol. 8(4), pages 237-243.
    19. Yang, Xiaohu & Yu, Jiabang & Guo, Zengxu & Jin, Liwen & He, Ya-Ling, 2019. "Role of porous metal foam on the heat transfer enhancement for a thermal energy storage tube," Applied Energy, Elsevier, vol. 239(C), pages 142-156.
    20. Wang, Wenqing & Kolditz, Olaf & Nagel, Thomas, 2017. "Parallel finite element modelling of multi-physical processes in thermochemical energy storage devices," Applied Energy, Elsevier, vol. 185(P2), pages 1954-1964.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2911-:d:178320. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.