IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v59y2016icp784-817.html
   My bibliography  Save this article

A review of recent developments and technological advancements of variable-air-volume (VAV) air-conditioning systems

Author

Listed:
  • Okochi, Godwine Swere
  • Yao, Ye

Abstract

This study reviewed VAV systems modeling and simulations, control strategies and optimization tools, the airflow characteristics of VAV systems, some common VAV systems׳ faults, detection and diagnosis, energy usage and analysis, and the current applications of variable air volume (VAV) air-conditioning systems. VAV system modeling is very complex as it involves complex structures and parameters a result of which has led to lack of models that combine both the AHU and building with all the required parameters.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:rensus:v:59:y:2016:i:c:p:784-817
    DOI: 10.1016/j.rser.2015.12.328
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032115017116
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2015.12.328?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Badescu, Viorel & Laaser, Nadine & Crutescu, Ruxandra & Crutescu, Marin & Dobrovicescu, Alexandru & Tsatsaronis, George, 2011. "Modeling, validation and time-dependent simulation of the first large passive building in Romania," Renewable Energy, Elsevier, vol. 36(1), pages 142-157.
    2. Ke, Yu-Pei & Mumma, Stanley A., 1997. "Optimized supply-air temperature (SAT) in variable-air-volume (VAV) systems," Energy, Elsevier, vol. 22(6), pages 601-614.
    3. Kusiak, Andrew & Li, Mingyang & Zhang, Zijun, 2010. "A data-driven approach for steam load prediction in buildings," Applied Energy, Elsevier, vol. 87(3), pages 925-933, March.
    4. Kusiak, Andrew & Xu, Guanglin, 2012. "Modeling and optimization of HVAC systems using a dynamic neural network," Energy, Elsevier, vol. 42(1), pages 241-250.
    5. Zheng, G.R. & Zaheer-Uddin, M., 1996. "Optimization of thermal processes in a variable air volume HVAC system," Energy, Elsevier, vol. 21(5), pages 407-420.
    6. Badescu, Viorel & Isvoranu, Dragos, 2011. "Pneumatic and thermal design procedure and analysis of earth-to-air heat exchangers of registry type," Applied Energy, Elsevier, vol. 88(4), pages 1266-1280, April.
    7. Mossolly, M. & Ghali, K. & Ghaddar, N., 2009. "Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm," Energy, Elsevier, vol. 34(1), pages 58-66.
    8. Badescu, Viorel, 2007. "Simple and accurate model for the ground heat exchanger of a passive house," Renewable Energy, Elsevier, vol. 32(5), pages 845-855.
    9. Shaikh, Pervez Hameed & Nor, Nursyarizal Bin Mohd & Nallagownden, Perumal & Elamvazuthi, Irraivan & Ibrahim, Taib, 2014. "A review on optimized control systems for building energy and comfort management of smart sustainable buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 409-429.
    10. Young-Hum Cho, 2012. "Development of a Terminal Control System with Variable Minimum Airflow Rate," Energies, MDPI, vol. 5(11), pages 1-22, November.
    11. Hepworth, S.J. & Dexter, A.L., 1996. "Adaptive neural control with stable learning," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 41(1), pages 39-51.
    12. Yoshida, Haruniri & Kumar, Sanjay, 2001. "Development of ARX model based off-line FDD technique for energy efficient buildings," Renewable Energy, Elsevier, vol. 22(1), pages 53-59.
    13. Foucquier, Aurélie & Robert, Sylvain & Suard, Frédéric & Stéphan, Louis & Jay, Arnaud, 2013. "State of the art in building modelling and energy performances prediction: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 272-288.
    14. Badescu, Viorel & Laaser, Nadine & Crutescu, Ruxandra, 2010. "Warm season cooling requirements for passive buildings in Southeastern Europe (Romania)," Energy, Elsevier, vol. 35(8), pages 3284-3300.
    15. Karunakaran, R. & Iniyan, S. & Goic, Ranko, 2010. "Energy efficient fuzzy based combined variable refrigerant volume and variable air volume air conditioning system for buildings," Applied Energy, Elsevier, vol. 87(4), pages 1158-1175, April.
    16. Dounis, A.I. & Caraiscos, C., 2009. "Advanced control systems engineering for energy and comfort management in a building environment--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1246-1261, August.
    17. Tashtoush, Bourhan & Molhim, M. & Al-Rousan, M., 2005. "Dynamic model of an HVAC system for control analysis," Energy, Elsevier, vol. 30(10), pages 1729-1745.
    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. Png, Ethan & Srinivasan, Seshadhri & Bekiroglu, Korkut & Chaoyang, Jiang & Su, Rong & Poolla, Kameshwar, 2019. "An internet of things upgrade for smart and scalable heating, ventilation and air-conditioning control in commercial buildings," Applied Energy, Elsevier, vol. 239(C), pages 408-424.
    2. Rosaria E.C. Amaral & Joel Brito & Matt Buckman & Elicia Drake & Esther Ilatova & Paige Rice & Carlos Sabbagh & Sergei Voronkin & Yewande S. Abraham, 2020. "Waste Management and Operational Energy for Sustainable Buildings: A Review," Sustainability, MDPI, vol. 12(13), pages 1-21, July.
    3. Behzad Rismanchi & Juan Mahecha Zambrano & Bryan Saxby & Ross Tuck & Mark Stenning, 2019. "Control Strategies in Multi-Zone Air Conditioning Systems," Energies, MDPI, vol. 12(3), pages 1-14, January.
    4. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Liu, Hongwu & Wang, Cheng, 2020. "An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control," Energy, Elsevier, vol. 199(C).
    5. Alessandro Franco & Lorenzo Miserocchi & Daniele Testi, 2021. "HVAC Energy Saving Strategies for Public Buildings Based on Heat Pumps and Demand Controlled Ventilation," Energies, MDPI, vol. 14(17), pages 1-20, September.
    6. 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.
    7. Mu, Yuanpeng & Zhang, Jili & Ma, Zhixian & Liu, Mingsheng, 2023. "A novel air flowrate control method based on terminal damper opening prediction in multi-zone VAV system," Energy, Elsevier, vol. 263(PD).
    8. Wei Wang & Xiaofang Shan & Syed Asad Hussain & Changshan Wang & Ying Ji, 2020. "Comparison of Multi-Control Strategies for the Control of Indoor Air Temperature and CO 2 with OpenModelica Modeling," Energies, MDPI, vol. 13(17), pages 1-20, August.
    9. Rafael Herrera-Limones & Ángel Luis León-Rodríguez & Álvaro López-Escamilla, 2019. "Solar Decathlon Latin America and Caribbean: Comfort and the Balance between Passive and Active Design," Sustainability, MDPI, vol. 11(13), pages 1-17, June.
    10. Nikolaos Kampelis & Nikolaos Sifakis & Dionysia Kolokotsa & Konstantinos Gobakis & Konstantinos Kalaitzakis & Daniela Isidori & Cristina Cristalli, 2019. "HVAC Optimization Genetic Algorithm for Industrial Near-Zero-Energy Building Demand Response," Energies, MDPI, vol. 12(11), pages 1-23, June.
    11. Miqdad Aziz & Kushsairy Kadir & Haziq Kamarul Azman & Kanendra Vijyakumar, 2023. "Optimization of Air Handler Controllers for Comfort Level in Smart Buildings Using Nature Inspired Algorithm," Energies, MDPI, vol. 16(24), pages 1-32, December.
    12. Wang, Hao & Chen, Xiwen & Vital, Natan & Duffy, Edward & Razi, Abolfazl, 2024. "Energy optimization for HVAC systems in multi-VAV open offices: A deep reinforcement learning approach," Applied Energy, Elsevier, vol. 356(C).
    13. Ma, Nan & Aviv, Dorit & Guo, Hongshan & Braham, William W., 2021. "Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    14. Hyo-Jun Kim & Young-Hum Cho, 2017. "A Study on a Control Method with a Ventilation Requirement of a VAV System in Multi-Zone," Sustainability, MDPI, vol. 9(11), pages 1-17, November.
    15. Nina Szczepanik-Scislo & Jacek Schnotale, 2020. "An Air Terminal Device with a Changing Geometry to Improve Indoor Air Quality for VAV Ventilation Systems," Energies, MDPI, vol. 13(18), pages 1-20, September.
    16. 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).
    17. Afroz, Zakia & Shafiullah, GM & Urmee, Tania & Higgins, Gary, 2018. "Modeling techniques used in building HVAC control systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 64-84.

    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. 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.
    2. Afroz, Zakia & Shafiullah, GM & Urmee, Tania & Higgins, Gary, 2018. "Modeling techniques used in building HVAC control systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 64-84.
    3. Kusiak, Andrew & Tang, Fan & Xu, Guanglin, 2011. "Multi-objective optimization of HVAC system with an evolutionary computation algorithm," Energy, Elsevier, vol. 36(5), pages 2440-2449.
    4. Muhammad Fayaz & DoHyeun Kim, 2018. "Energy Consumption Optimization and User Comfort Management in Residential Buildings Using a Bat Algorithm and Fuzzy Logic," Energies, MDPI, vol. 11(1), pages 1-22, January.
    5. 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.
    6. Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
    7. Mossolly, M. & Ghali, K. & Ghaddar, N., 2009. "Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm," Energy, Elsevier, vol. 34(1), pages 58-66.
    8. Kusiak, Andrew & Li, Mingyang, 2010. "Reheat optimization of the variable-air-volume box," Energy, Elsevier, vol. 35(5), pages 1997-2005.
    9. Clara Ceccolini & Roozbeh Sangi, 2022. "Benchmarking Approaches for Assessing the Performance of Building Control Strategies: A Review," Energies, MDPI, vol. 15(4), pages 1-30, February.
    10. Halhoul Merabet, Ghezlane & Essaaidi, Mohamed & Ben Haddou, Mohamed & Qolomany, Basheer & Qadir, Junaid & Anan, Muhammad & Al-Fuqaha, Ala & Abid, Mohamed Riduan & Benhaddou, Driss, 2021. "Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    11. Kusiak, Andrew & Li, Mingyang, 2009. "Optimal decision making in ventilation control," Energy, Elsevier, vol. 34(11), pages 1835-1845.
    12. Sha, Huajing & Xu, Peng & Yang, Zhiwei & Chen, Yongbao & Tang, Jixu, 2019. "Overview of computational intelligence for building energy system design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 76-90.
    13. Paulína Šujanová & Monika Rychtáriková & Tiago Sotto Mayor & Affan Hyder, 2019. "A Healthy, Energy-Efficient and Comfortable Indoor Environment, a Review," Energies, MDPI, vol. 12(8), pages 1-37, April.
    14. Afram, Abdul & Janabi-Sharifi, Farrokh, 2015. "Gray-box modeling and validation of residential HVAC system for control system design," Applied Energy, Elsevier, vol. 137(C), pages 134-150.
    15. Kim, Wonuk & Jeon, Yongseok & Kim, Yongchan, 2016. "Simulation-based optimization of an integrated daylighting and HVAC system using the design of experiments method," Applied Energy, Elsevier, vol. 162(C), pages 666-674.
    16. Israr Ullah & DoHyeun Kim, 2017. "An Improved Optimization Function for Maximizing User Comfort with Minimum Energy Consumption in Smart Homes," Energies, MDPI, vol. 10(11), pages 1-21, November.
    17. Buonomano, Annamaria & Montanaro, Umberto & Palombo, Adolfo & Santini, Stefania, 2016. "Dynamic building energy performance analysis: A new adaptive control strategy for stringent thermohygrometric indoor air requirements," Applied Energy, Elsevier, vol. 163(C), pages 361-386.
    18. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Liu, Hongwu & Wang, Cheng, 2020. "An energy-saving control strategy for multi-zone demand controlled ventilation system with data-driven model and air balancing control," Energy, Elsevier, vol. 199(C).
    19. Chiu, Chien-Chin & Tsai, Nan-Chyuan & Lin, Chun-Chi, 2014. "Near-optimal order-reduced control for A/C (air-conditioning) system of EVs (electric vehicles)," Energy, Elsevier, vol. 66(C), pages 342-353.
    20. Wenquan Jin & Israr Ullah & Shabir Ahmad & Dohyeun Kim, 2019. "Occupant Comfort Management Based on Energy Optimization Using an Environment Prediction Model in Smart Homes," Sustainability, MDPI, vol. 11(4), pages 1-18, February.

    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:eee:rensus:v:59:y:2016:i:c:p:784-817. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.