Improvement of Energy Efficiency of Markov ACMV Systems based on PTS Information of Occupants

Deqing Zhai; Tanaya Chaudhuri; Yeng Chai Soh; Xianhua Ou; Chaoyang Jiang

doi:10.1109/IJCNN.2018.8489214

Improvement of Energy Efficiency of Markov ACMV Systems based on PTS Information of Occupants

Deqing Zhai
, Tanaya Chaudhuri
, Yeng Chai Soh
, Xianhua Ou
, Chaoyang Jiang

Nanyang Technological University
Zhejiang University of Technology

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

2 Citations (Scopus)

Abstract

This study proposes a novel method of energy efficiency improvement in Air-Conditioning and Mechanical Ventilation (ACMV) systems on the basis of occupant thermal states (ComforyDiscomfort) evaluated by Predictive Thermal State (PTS) models. An ACMV Operating State (OS) algorithm is proposed and integrated with PTS models under assumptions of Markov Decision Process (MDP). The ACMV OS algorithm and the developed PTS models are applied in our thermal laboratory. The results show that NN based PTS models perform better than ELM based ones in terms of energy saving in our case studies. The optimal sampling time of the applied ACMV OS algorithm is 10 mins without the issues of system lagging and losing sharpness of tracking thermal states of occupants. The experimental results show that the proposed ACMV OS algorithm can significantly reduce around 10 kWh out of 74 kWh (about 13.5% energy saving) daily in laboratory conditions without compromising the thermal comfort level of occupants.

Original language	English
Title of host publication	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509060146
DOIs	https://doi.org/10.1109/IJCNN.2018.8489214
Publication status	Published - 10 Oct 2018
Externally published	Yes
Event	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2018-July

Conference

Conference	2018 International Joint Conference on Neural Networks, IJCNN 2018
Country/Territory	Brazil
City	Rio de Janeiro
Period	8/07/18 → 13/07/18

UN SDGs

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

SDG 7 Affordable and Clean Energy

Access to Document

10.1109/IJCNN.2018.8489214

Cite this

Zhai, D., Chaudhuri, T., Soh, Y. C., Ou, X., & Jiang, C. (2018). Improvement of Energy Efficiency of Markov ACMV Systems based on PTS Information of Occupants. In 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings Article 8489214 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2018.8489214

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title = "Improvement of Energy Efficiency of Markov ACMV Systems based on PTS Information of Occupants",

abstract = "This study proposes a novel method of energy efficiency improvement in Air-Conditioning and Mechanical Ventilation (ACMV) systems on the basis of occupant thermal states (ComforyDiscomfort) evaluated by Predictive Thermal State (PTS) models. An ACMV Operating State (OS) algorithm is proposed and integrated with PTS models under assumptions of Markov Decision Process (MDP). The ACMV OS algorithm and the developed PTS models are applied in our thermal laboratory. The results show that NN based PTS models perform better than ELM based ones in terms of energy saving in our case studies. The optimal sampling time of the applied ACMV OS algorithm is 10 mins without the issues of system lagging and losing sharpness of tracking thermal states of occupants. The experimental results show that the proposed ACMV OS algorithm can significantly reduce around 10 kWh out of 74 kWh (about 13.5\% energy saving) daily in laboratory conditions without compromising the thermal comfort level of occupants.",

author = "Deqing Zhai and Tanaya Chaudhuri and Soh, \{Yeng Chai\} and Xianhua Ou and Chaoyang Jiang",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Joint Conference on Neural Networks, IJCNN 2018 ; Conference date: 08-07-2018 Through 13-07-2018",

year = "2018",

month = oct,

day = "10",

doi = "10.1109/IJCNN.2018.8489214",

language = "English",

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Zhai, D, Chaudhuri, T, Soh, YC, Ou, X & Jiang, C 2018, Improvement of Energy Efficiency of Markov ACMV Systems based on PTS Information of Occupants. in 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings., 8489214, Proceedings of the International Joint Conference on Neural Networks, vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, 8/07/18. https://doi.org/10.1109/IJCNN.2018.8489214

Improvement of Energy Efficiency of Markov ACMV Systems based on PTS Information of Occupants. / Zhai, Deqing; Chaudhuri, Tanaya; Soh, Yeng Chai et al.
2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8489214 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July).

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

TY - GEN

T1 - Improvement of Energy Efficiency of Markov ACMV Systems based on PTS Information of Occupants

AU - Zhai, Deqing

AU - Chaudhuri, Tanaya

AU - Soh, Yeng Chai

AU - Ou, Xianhua

AU - Jiang, Chaoyang

PY - 2018/10/10

Y1 - 2018/10/10

N2 - This study proposes a novel method of energy efficiency improvement in Air-Conditioning and Mechanical Ventilation (ACMV) systems on the basis of occupant thermal states (ComforyDiscomfort) evaluated by Predictive Thermal State (PTS) models. An ACMV Operating State (OS) algorithm is proposed and integrated with PTS models under assumptions of Markov Decision Process (MDP). The ACMV OS algorithm and the developed PTS models are applied in our thermal laboratory. The results show that NN based PTS models perform better than ELM based ones in terms of energy saving in our case studies. The optimal sampling time of the applied ACMV OS algorithm is 10 mins without the issues of system lagging and losing sharpness of tracking thermal states of occupants. The experimental results show that the proposed ACMV OS algorithm can significantly reduce around 10 kWh out of 74 kWh (about 13.5% energy saving) daily in laboratory conditions without compromising the thermal comfort level of occupants.

AB - This study proposes a novel method of energy efficiency improvement in Air-Conditioning and Mechanical Ventilation (ACMV) systems on the basis of occupant thermal states (ComforyDiscomfort) evaluated by Predictive Thermal State (PTS) models. An ACMV Operating State (OS) algorithm is proposed and integrated with PTS models under assumptions of Markov Decision Process (MDP). The ACMV OS algorithm and the developed PTS models are applied in our thermal laboratory. The results show that NN based PTS models perform better than ELM based ones in terms of energy saving in our case studies. The optimal sampling time of the applied ACMV OS algorithm is 10 mins without the issues of system lagging and losing sharpness of tracking thermal states of occupants. The experimental results show that the proposed ACMV OS algorithm can significantly reduce around 10 kWh out of 74 kWh (about 13.5% energy saving) daily in laboratory conditions without compromising the thermal comfort level of occupants.

UR - https://www.scopus.com/pages/publications/85056489722

U2 - 10.1109/IJCNN.2018.8489214

DO - 10.1109/IJCNN.2018.8489214

M3 - Conference contribution

AN - SCOPUS:85056489722

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 International Joint Conference on Neural Networks, IJCNN 2018

Y2 - 8 July 2018 through 13 July 2018

ER -

Zhai D, Chaudhuri T, Soh YC, Ou X, Jiang C. Improvement of Energy Efficiency of Markov ACMV Systems based on PTS Information of Occupants. In 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8489214. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN.2018.8489214

Improvement of Energy Efficiency of Markov ACMV Systems based on PTS Information of Occupants

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