Control of Multi-Zone HVAC Systems via Neurodynamic Optimization

Yanghe Zou; Ping Guo; Zhongying Chen

doi:10.1109/NTCI67886.2025.11308204

Control of Multi-Zone HVAC Systems via Neurodynamic Optimization

Yanghe Zou
, Ping Guo
, Zhongying Chen

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Heating, ventilation, and air-conditioning (HVAC) systems account for a substantial proportion of the total energy usage in modem buildings. Due to the nonconvex mathematical model of multi-zone HVAC systems, existing control approaches face challenges in saving energy while satisfying indoor environment requirements. In this study, the control for multi-zone HVAC systems is formulated as a global optimization task under operational constraints. A projection neural network-based approach is developed to address the formulated problem. Simulation results indicate that the developed approach can effectively regulate indoor temperatures across multiple zones, demonstrating both reliable control performance and potential for energy-efficient operation. © 2025 IEEE.

Original language	English
Title of host publication	Proceedings of 2025 International Conference on New Trends in Computational Intelligence (NTCI)
Editors	Yuehui Chen, Ying Li, Jian Wang
Publisher	IEEE
Pages	564-568
ISBN (Electronic)	9798331556143
ISBN (Print)	979-8-3315-5615-0
DOIs	https://doi.org/10.1109/NTCI67886.2025.11308204
Publication status	Published - 2025
Event	2025 International Conference on New Trends in Computational Intelligence (NTCI 2025) - Ji'nan, China Duration: 17 Oct 2025 → 19 Oct 2025

Publication series

Name	Proceedings of International Conference on New Trends in Computational Intelligence, NTCI

Conference

Conference	2025 International Conference on New Trends in Computational Intelligence (NTCI 2025)
Place	China
City	Ji'nan
Period	17/10/25 → 19/10/25

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62406067, and in part by the Natural Science Foundation of Jiangsu Province of China under Grant BK20241298.

Research Keywords

Multi-Zone HVAC Systems
Neurodynamic Optimization
Projection Neural Network

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10.1109/NTCI67886.2025.11308204

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Zou, Y., Guo, P., & Chen, Z. (2025). Control of Multi-Zone HVAC Systems via Neurodynamic Optimization. In Y. Chen, Y. Li, & J. Wang (Eds.), Proceedings of 2025 International Conference on New Trends in Computational Intelligence (NTCI) (pp. 564-568). (Proceedings of International Conference on New Trends in Computational Intelligence, NTCI). IEEE. https://doi.org/10.1109/NTCI67886.2025.11308204

Zou, Yanghe ; Guo, Ping ; Chen, Zhongying. / Control of Multi-Zone HVAC Systems via Neurodynamic Optimization. Proceedings of 2025 International Conference on New Trends in Computational Intelligence (NTCI). editor / Yuehui Chen ; Ying Li ; Jian Wang. IEEE, 2025. pp. 564-568 (Proceedings of International Conference on New Trends in Computational Intelligence, NTCI).

@inproceedings{be4de9b2432749e097260537b7198bfb,

title = "Control of Multi-Zone HVAC Systems via Neurodynamic Optimization",

abstract = "Heating, ventilation, and air-conditioning (HVAC) systems account for a substantial proportion of the total energy usage in modem buildings. Due to the nonconvex mathematical model of multi-zone HVAC systems, existing control approaches face challenges in saving energy while satisfying indoor environment requirements. In this study, the control for multi-zone HVAC systems is formulated as a global optimization task under operational constraints. A projection neural network-based approach is developed to address the formulated problem. Simulation results indicate that the developed approach can effectively regulate indoor temperatures across multiple zones, demonstrating both reliable control performance and potential for energy-efficient operation. {\textcopyright} 2025 IEEE.",

keywords = "Multi-Zone HVAC Systems, Neurodynamic Optimization, Projection Neural Network",

author = "Yanghe Zou and Ping Guo and Zhongying Chen",

year = "2025",

doi = "10.1109/NTCI67886.2025.11308204",

language = "English",

isbn = "979-8-3315-5615-0",

series = "Proceedings of International Conference on New Trends in Computational Intelligence, NTCI",

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booktitle = "Proceedings of 2025 International Conference on New Trends in Computational Intelligence (NTCI)",

address = "United States",

note = "2025 International Conference on New Trends in Computational Intelligence (NTCI 2025) ; Conference date: 17-10-2025 Through 19-10-2025",

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Zou, Y, Guo, P & Chen, Z 2025, Control of Multi-Zone HVAC Systems via Neurodynamic Optimization. in Y Chen, Y Li & J Wang (eds), Proceedings of 2025 International Conference on New Trends in Computational Intelligence (NTCI). Proceedings of International Conference on New Trends in Computational Intelligence, NTCI, IEEE, pp. 564-568, 2025 International Conference on New Trends in Computational Intelligence (NTCI 2025), Ji'nan, China, 17/10/25. https://doi.org/10.1109/NTCI67886.2025.11308204

Control of Multi-Zone HVAC Systems via Neurodynamic Optimization. / Zou, Yanghe; Guo, Ping; Chen, Zhongying.
Proceedings of 2025 International Conference on New Trends in Computational Intelligence (NTCI). ed. / Yuehui Chen; Ying Li; Jian Wang. IEEE, 2025. p. 564-568 (Proceedings of International Conference on New Trends in Computational Intelligence, NTCI).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Control of Multi-Zone HVAC Systems via Neurodynamic Optimization

AU - Zou, Yanghe

AU - Guo, Ping

AU - Chen, Zhongying

PY - 2025

Y1 - 2025

N2 - Heating, ventilation, and air-conditioning (HVAC) systems account for a substantial proportion of the total energy usage in modem buildings. Due to the nonconvex mathematical model of multi-zone HVAC systems, existing control approaches face challenges in saving energy while satisfying indoor environment requirements. In this study, the control for multi-zone HVAC systems is formulated as a global optimization task under operational constraints. A projection neural network-based approach is developed to address the formulated problem. Simulation results indicate that the developed approach can effectively regulate indoor temperatures across multiple zones, demonstrating both reliable control performance and potential for energy-efficient operation. © 2025 IEEE.

AB - Heating, ventilation, and air-conditioning (HVAC) systems account for a substantial proportion of the total energy usage in modem buildings. Due to the nonconvex mathematical model of multi-zone HVAC systems, existing control approaches face challenges in saving energy while satisfying indoor environment requirements. In this study, the control for multi-zone HVAC systems is formulated as a global optimization task under operational constraints. A projection neural network-based approach is developed to address the formulated problem. Simulation results indicate that the developed approach can effectively regulate indoor temperatures across multiple zones, demonstrating both reliable control performance and potential for energy-efficient operation. © 2025 IEEE.

KW - Multi-Zone HVAC Systems

KW - Neurodynamic Optimization

KW - Projection Neural Network

UR - https://www.scopus.com/pages/publications/105032535025

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-105032535025&origin=recordpage

U2 - 10.1109/NTCI67886.2025.11308204

DO - 10.1109/NTCI67886.2025.11308204

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 979-8-3315-5615-0

T3 - Proceedings of International Conference on New Trends in Computational Intelligence, NTCI

SP - 564

EP - 568

BT - Proceedings of 2025 International Conference on New Trends in Computational Intelligence (NTCI)

A2 - Chen, Yuehui

A2 - Li, Ying

A2 - Wang, Jian

PB - IEEE

T2 - 2025 International Conference on New Trends in Computational Intelligence (NTCI 2025)

Y2 - 17 October 2025 through 19 October 2025

ER -

Zou Y, Guo P, Chen Z. Control of Multi-Zone HVAC Systems via Neurodynamic Optimization. In Chen Y, Li Y, Wang J, editors, Proceedings of 2025 International Conference on New Trends in Computational Intelligence (NTCI). IEEE. 2025. p. 564-568. (Proceedings of International Conference on New Trends in Computational Intelligence, NTCI). Epub 2025 Dec 29. doi: 10.1109/NTCI67886.2025.11308204

Control of Multi-Zone HVAC Systems via Neurodynamic Optimization

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