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Review of HVAC Systems History and Future Applications

Author

Listed:
  • DeQuante Rashon Mckoy

    (Department of Computational Science and Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA)

  • Raymond Charles Tesiero

    (Department of Civil Architect & Environmental Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA)

  • Yaa Takyiwaa Acquaah

    (Department of Computational Science and Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA)

  • Balakrishna Gokaraju

    (Department of Computational Science and Engineering, North Carolina Agricultural and Technical State University, Greensboro, NC 27411, USA)

Abstract

Today, HVAC (heating, ventilation, and air conditioning) systems have become an integral part of modern buildings and are designed to provide comfortable indoor environments while conserving energy and reducing carbon emissions. With advancement in technology, HVAC systems have a variety of sensors that are used to detect the occupants within a controlled environment. Advancements in computer control systems and the use of smart technology have made HVAC systems even more sophisticated, allowing for approximate temperature control and energy management. This paper will review the historical development of technology and the current state of HVAC systems. With the proper data, development of artificial intelligence models can, in theory, improve the overall optimization and reduce energy consumption This paper will provide a review of HVAC history and the key concepts around the usefulness of using AI from previous research conducted in this field of study.

Suggested Citation

  • DeQuante Rashon Mckoy & Raymond Charles Tesiero & Yaa Takyiwaa Acquaah & Balakrishna Gokaraju, 2023. "Review of HVAC Systems History and Future Applications," Energies, MDPI, vol. 16(17), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6109-:d:1222266
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    References listed on IDEAS

    as
    1. Yang, Shiyu & Wan, Man Pun & Chen, Wanyu & Ng, Bing Feng & Dubey, Swapnil, 2020. "Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization," Applied Energy, Elsevier, vol. 271(C).
    2. Du, Yan & Zandi, Helia & Kotevska, Olivera & Kurte, Kuldeep & Munk, Jeffery & Amasyali, Kadir & Mckee, Evan & Li, Fangxing, 2021. "Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning," Applied Energy, Elsevier, vol. 281(C).
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    Cited by:

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    2. Amal Azzi & Mohamed Tabaa & Badr Chegari & Hanaa Hachimi, 2024. "Balancing Sustainability and Comfort: A Holistic Study of Building Control Strategies That Meet the Global Standards for Efficiency and Thermal Comfort," Sustainability, MDPI, vol. 16(5), pages 1-36, March.

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