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Energy optimization for HVAC systems in multi-VAV open offices: A deep reinforcement learning approach

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  • Wang, Hao
  • Chen, Xiwen
  • Vital, Natan
  • Duffy, Edward
  • Razi, Abolfazl

Abstract

With global warming intensifying and resource conflicts escalating, the world is undergoing a transformative shift toward sustainable practices and energy-efficient solutions. With more than 32% of the global energy used by commercial and residential buildings, there is an urgent need to revisit traditional approaches to Building Energy Management (BEM). Within a BEMSplatform, regulating the operation of Heating, Ventilation, and Air Conditioning (HVAC) systems is more important, noting that HVAC systems account for about 40% of the total energy cost in the commercial sector.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:356:y:2024:i:c:s030626192301718x
    DOI: 10.1016/j.apenergy.2023.122354
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    References listed on IDEAS

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    1. 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:

    1. Dalia Mohammed Talat Ebrahim Ali & Violeta Motuzienė & Rasa Džiugaitė-Tumėnienė, 2024. "AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings," Energies, MDPI, vol. 17(17), pages 1-35, August.

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