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

Byeongmo Seo,Jung Hyun Mun,Yeo Beom Yoon,Soolyeon Cho

doi:10.3390/en12244754

Byeongmo Seo, Jung Hyun Mun + Show 2 more

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https://doi.org/10.3390/en12244754

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Journal: Energies	Publication Date: Dec 13, 2019
Citations: 14	License type: CC BY 4.0

Affiliation: North Carolina State University

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.

Highlights

There is currently international cooperation for the reduction of greenhouse gases under the paradigm of climate change
Urban areas account for most fossil fuel consumption; in other words, most of the carbon dioxide generated by fossil fuels are generated from the energy use of urban space
The results indicate that the Artificial Neural Network (ANN) model developed for load prediction can be used for HVAC optimal control

Summary

Introduction

There is currently international cooperation for the reduction of greenhouse gases under the paradigm of climate change. According to the Energy Outlook 2019, the total carbon dioxide emissions in the US are classified into industrial, transportation, residential, and commercial. Considering the combined CO2 emissions from residential and commercial buildings that make up cities, the construction sector is expected to emit about 40% more carbon dioxide than the transportation sector. Compared to residential buildings in 2019, commercial buildings have about 10% higher carbon dioxide emissions, which is expected to remain steady and not decrease until 2050. These data stress that measures to reduce greenhouse gas emissions from commercial buildings need priority over residential buildings [2]

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