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Application of Artificial Neural Network for the Optimum Control of HVAC Systems in Double-Skinned Office Buildings

Author

Listed:
  • Byeongmo Seo

    (School of Architecture, College of Design, North Carolina State University, Raleigh, NC 27695, USA
    These authors contributed equally to this work.)

  • Yeo Beom Yoon

    (School of Architecture, College of Design, North Carolina State University, Raleigh, NC 27695, USA
    These authors contributed equally to this work.)

  • Jung Hyun Mun

    (Sun & Light R&D Center, Seoul 06648, Korea)

  • Soolyeon Cho

    (School of Architecture, College of Design, North Carolina State University, Raleigh, NC 27695, USA)

Abstract

Double Skin Façade (DSF) systems have become an alternative to the environmental and energy savings issues. DSF offers thermal buffer areas that can provide benefits to the conditioned spaces in the form of improved comforts and energy savings. There are many studies conducted to resolve issues about the heat captured inside DSF. Various window control strategies and algorithms were introduced to minimize the heat gain of DSF in summer. However, the thermal condition of the DSF causes a time lag between the response time of the Heating, Ventilation, and Air-Conditioning (HVAC) system and cooling loads of zones. This results in more cooling energy supply or sometimes less than required, making the conditioned zones either too cold or warm. It is necessary to operate the HVAC system in consideration of all conditions, i.e., DSF internal conditions and indoor environment, as well as proper DSF window controls. This paper proposes an optimal air supply control for a DSF office building located in a hot and humid climate. An Artificial Neural Network (ANN)-based control was developed and tested for its effectiveness. Results show a 10.5% cooling energy reduction from the DSF building compared to the non-DSF building with the same HVAC control. Additionally, 4.5% more savings were observed when using the ANN-based control.

Suggested Citation

  • Byeongmo Seo & Yeo Beom Yoon & Jung Hyun Mun & Soolyeon Cho, 2019. "Application of Artificial Neural Network for the Optimum Control of HVAC Systems in Double-Skinned Office Buildings," Energies, MDPI, vol. 12(24), pages 1-22, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4754-:d:297500
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    Cited by:

    1. Lucrezia Manservigi & Mattia Cattozzo & Pier Ruggero Spina & Mauro Venturini & Hilal Bahlawan, 2020. "Optimal Management of the Energy Flows of Interconnected Residential Users," Energies, MDPI, vol. 13(6), pages 1-21, March.
    2. Zhiqiang Wang & Qi Tian & Jie Jia, 2022. "The Convective Heat Transfer Performance and Structural Optimization of the Cavity in Energy-Saving Thermal Insulation Windows under Cold Air Penetration Condition," Energies, MDPI, vol. 15(7), pages 1-21, March.

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