Upper and lower threshold limit of chilled and condenser water temperature set-points during ANN based optimized control

Sang Hun Yeon; Won Hee Kang; Je Hyeon Lee; Kwan Woo Song; Young Tae Chae; Kwang Ho Lee

doi:10.1115/ES2021-63997

Upper and lower threshold limit of chilled and condenser water temperature set-points during ANN based optimized control

Sang Hun Yeon, Won Hee Kang, Je Hyeon Lee, Kwan Woo Song, Young Tae Chae, Kwang Ho Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this study, an artificial neural network (ANN) based realtime predictive control and optimization algorithm for a chillerbased cooling system was developed and applied to an actual building to analyze its cooling energy saving effects through insitu application and actual measurements. For this purpose, we set the cooling tower's condenser water outlet temperature and the chiller's chilled water outlet temperature as the system control variables. During the analysis, unexpected abnormal data were observed due to insufficient training data and a limited consideration of the outdoor air wet-bulb temperature when determining the condenser water temperature set-point. Therefore, it is necessary to build training data under a wide range of conditions and to set the condenser water temperature set-point lower limit to be outdoor air wet-bulb temperature +3.6°C in the outdoor wet-bulb temperature region above 23°C, so that further energy savings can be achieved.

Original language	English
Title of host publication	Proceedings of the ASME 2021 15th International Conference on Energy Sustainability, ES 2021
Publisher	American Society of Mechanical Engineers (ASME)
ISBN (Electronic)	9780791884881
DOIs	https://doi.org/10.1115/ES2021-63997
Publication status	Published - 2021
Event	ASME 2021 15th International Conference on Energy Sustainability, ES 2021 - Virtual, Online Duration: 2021 Jun 16 → 2021 Jun 18

Publication series

Name	Proceedings of the ASME 2021 15th International Conference on Energy Sustainability, ES 2021

Conference

Conference	ASME 2021 15th International Conference on Energy Sustainability, ES 2021
City	Virtual, Online
Period	21/6/16 → 21/6/18

Bibliographical note

Funding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. 2019R1A2C2087157).

Publisher Copyright:
Copyright © 2021 by ASME.

Keywords

ANN (artificial neural network)
Chilled water temperature
Chiller
Condenser water temperature
Cooling tower
HVAC system
In-situ measurement

ASJC Scopus subject areas

Fuel Technology
Renewable Energy, Sustainability and the Environment
Energy Engineering and Power Technology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1115/ES2021-63997

Cite this

Yeon, S. H., Kang, W. H., Lee, J. H., Song, K. W., Chae, Y. T., & Lee, K. H. (2021). Upper and lower threshold limit of chilled and condenser water temperature set-points during ANN based optimized control. In Proceedings of the ASME 2021 15th International Conference on Energy Sustainability, ES 2021 Article V001T15A009 (Proceedings of the ASME 2021 15th International Conference on Energy Sustainability, ES 2021). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/ES2021-63997

Upper and lower threshold limit of chilled and condenser water temperature set-points during ANN based optimized control. / Yeon, Sang Hun; Kang, Won Hee; Lee, Je Hyeon et al.
Proceedings of the ASME 2021 15th International Conference on Energy Sustainability, ES 2021. American Society of Mechanical Engineers (ASME), 2021. V001T15A009 (Proceedings of the ASME 2021 15th International Conference on Energy Sustainability, ES 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yeon, SH, Kang, WH, Lee, JH, Song, KW, Chae, YT & Lee, KH 2021, Upper and lower threshold limit of chilled and condenser water temperature set-points during ANN based optimized control. in Proceedings of the ASME 2021 15th International Conference on Energy Sustainability, ES 2021., V001T15A009, Proceedings of the ASME 2021 15th International Conference on Energy Sustainability, ES 2021, American Society of Mechanical Engineers (ASME), ASME 2021 15th International Conference on Energy Sustainability, ES 2021, Virtual, Online, 21/6/16. https://doi.org/10.1115/ES2021-63997

Yeon SH, Kang WH, Lee JH, Song KW, Chae YT, Lee KH. Upper and lower threshold limit of chilled and condenser water temperature set-points during ANN based optimized control. In Proceedings of the ASME 2021 15th International Conference on Energy Sustainability, ES 2021. American Society of Mechanical Engineers (ASME). 2021. V001T15A009. (Proceedings of the ASME 2021 15th International Conference on Energy Sustainability, ES 2021). doi: 10.1115/ES2021-63997

Yeon, Sang Hun ; Kang, Won Hee ; Lee, Je Hyeon et al. / Upper and lower threshold limit of chilled and condenser water temperature set-points during ANN based optimized control. Proceedings of the ASME 2021 15th International Conference on Energy Sustainability, ES 2021. American Society of Mechanical Engineers (ASME), 2021. (Proceedings of the ASME 2021 15th International Conference on Energy Sustainability, ES 2021).

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abstract = "In this study, an artificial neural network (ANN) based realtime predictive control and optimization algorithm for a chillerbased cooling system was developed and applied to an actual building to analyze its cooling energy saving effects through insitu application and actual measurements. For this purpose, we set the cooling tower's condenser water outlet temperature and the chiller's chilled water outlet temperature as the system control variables. During the analysis, unexpected abnormal data were observed due to insufficient training data and a limited consideration of the outdoor air wet-bulb temperature when determining the condenser water temperature set-point. Therefore, it is necessary to build training data under a wide range of conditions and to set the condenser water temperature set-point lower limit to be outdoor air wet-bulb temperature +3.6°C in the outdoor wet-bulb temperature region above 23°C, so that further energy savings can be achieved. ",

keywords = "ANN (artificial neural network), Chilled water temperature, Chiller, Condenser water temperature, Cooling tower, HVAC system, In-situ measurement",

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note = "Funding Information: This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. 2019R1A2C2087157). Publisher Copyright: Copyright {\textcopyright} 2021 by ASME.; ASME 2021 15th International Conference on Energy Sustainability, ES 2021 ; Conference date: 16-06-2021 Through 18-06-2021",

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doi = "10.1115/ES2021-63997",

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AU - Yeon, Sang Hun

AU - Kang, Won Hee

AU - Lee, Je Hyeon

AU - Song, Kwan Woo

AU - Chae, Young Tae

AU - Lee, Kwang Ho

N1 - Funding Information: This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. 2019R1A2C2087157). Publisher Copyright: Copyright © 2021 by ASME.

PY - 2021

Y1 - 2021

N2 - In this study, an artificial neural network (ANN) based realtime predictive control and optimization algorithm for a chillerbased cooling system was developed and applied to an actual building to analyze its cooling energy saving effects through insitu application and actual measurements. For this purpose, we set the cooling tower's condenser water outlet temperature and the chiller's chilled water outlet temperature as the system control variables. During the analysis, unexpected abnormal data were observed due to insufficient training data and a limited consideration of the outdoor air wet-bulb temperature when determining the condenser water temperature set-point. Therefore, it is necessary to build training data under a wide range of conditions and to set the condenser water temperature set-point lower limit to be outdoor air wet-bulb temperature +3.6°C in the outdoor wet-bulb temperature region above 23°C, so that further energy savings can be achieved.

AB - In this study, an artificial neural network (ANN) based realtime predictive control and optimization algorithm for a chillerbased cooling system was developed and applied to an actual building to analyze its cooling energy saving effects through insitu application and actual measurements. For this purpose, we set the cooling tower's condenser water outlet temperature and the chiller's chilled water outlet temperature as the system control variables. During the analysis, unexpected abnormal data were observed due to insufficient training data and a limited consideration of the outdoor air wet-bulb temperature when determining the condenser water temperature set-point. Therefore, it is necessary to build training data under a wide range of conditions and to set the condenser water temperature set-point lower limit to be outdoor air wet-bulb temperature +3.6°C in the outdoor wet-bulb temperature region above 23°C, so that further energy savings can be achieved.

KW - ANN (artificial neural network)

KW - Chilled water temperature

KW - Chiller

KW - Condenser water temperature

KW - Cooling tower

KW - HVAC system

KW - In-situ measurement

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M3 - Conference contribution

AN - SCOPUS:85111782971

T3 - Proceedings of the ASME 2021 15th International Conference on Energy Sustainability, ES 2021

BT - Proceedings of the ASME 2021 15th International Conference on Energy Sustainability, ES 2021

PB - American Society of Mechanical Engineers (ASME)

T2 - ASME 2021 15th International Conference on Energy Sustainability, ES 2021

Y2 - 16 June 2021 through 18 June 2021

ER -

Upper and lower threshold limit of chilled and condenser water temperature set-points during ANN based optimized control

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this