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

Byeongmo Seo, Yeo Beom Yoon, Jung Hyun Mun and Soolyeon Cho
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Byeongmo Seo: School of Architecture, College of Design, North Carolina State University, Raleigh, NC 27695, USA
Yeo Beom Yoon: School of Architecture, College of Design, North Carolina State University, Raleigh, NC 27695, USA
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

Energies, 2019, vol. 12, issue 24, 1-22

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.

Keywords: Double Skin Facade; HVAC optimal control; EnergyPlus; load prediction; artificial neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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