Power-constrained VRF system optimization using symbolic regression for multiple zones environment

Theint Thu, Theint; Kato, Kenshiro; Zhao, Dafang; Nishikawa, Hiroki; Taniguchi, Ittetsu; Onoye, Takao

doi:https://doi.org/10.1016/j.enbuild.2025.116264

Power-constrained VRF system optimization using symbolic regression for multiple zones environment

Theint Thu, Theint; Kato, Kenshiro; Zhao, Dafang et al.

Energy and Buildings, 2025, 347, 116264

アクセス数:25件（2025-11-27 18:01 集計）

固定URL: https://hdl.handle.net/11094/102866

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タイトル	Power-constrained VRF system optimization using symbolic regression for multiple zones environment
著者	著者名 Theint Thu, Theint
	著者名 Kato, Kenshiro
	NRID 1000010986962 1000010986962 ORCID 0000-0001-8107-4260 0000-0001-8107-4260 researchmap permalink zhaodafang zhaodafang Scopus Author ID 57315020300 57315020300 著者名 Zhao, Dafang
	NRID 1000090963538 1000090963538 ORCID 0000-0002-9626-0944 0000-0002-9626-0944 researchmap permalink hiroki_nishikawa hiroki_nishikawa Scopus Author ID 57205765139 57205765139 著者名 Nishikawa, Hiroki
	NRID 1000040551453 1000040551453 ORCID 0000-0002-7843-5907 0000-0002-7843-5907 researchmap permalink ittetsu ittetsu Scopus Author ID 23013657500 23013657500 著者名 Taniguchi, Ittetsu
	NRID 1000060252590 1000060252590 researchmap permalink read0096872 read0096872 Scopus Author ID 7007073634 7007073634 著者名 Onoye, Takao
キーワード等	Energy efficiency
	Model predictive control
	Power constraints
	Symbolic regression
	Thermal comfort
	Variable refrigerant flow
内容	Theint Thu T., Kato K., Zhao D., et al. Power-constrained VRF system optimization using symbolic regression for multiple zones environment. Energy and Buildings 347, 116264 (2025); https://doi.org/10.1016/j.enbuild.2025.116264.
抄録	The rapid growth in global energy consumption highlights the urgency of doubling energy efficiency improvements by 2030. Heating, ventilation, and air-conditioning (HVAC) systems, which account for nearly half of building energy use, represent a critical target for optimization. Conventional HVAC control strategies, however, often suffer from inefficient power allocation, high peak demand, and compromised thermal comfort, especially under dynamic occupancy and environmental conditions. Existing multi-zone control methods often overlook peak power constraints and are not designed to optimize energy use under variable occupancy conditions, resulting in suboptimal energy performance. This study proposes a symbolic regression-based model predictive control (MPC) framework to address these challenges. The framework optimizes energy consumption and thermal comfort for multi-zone variable refrigerant flow (VRF) systems while addressing peak power constraints to reduce energy costs and improve thermal comfort. The method is evaluated under three operating priorities, ω = 0.1, 0.5, and 0.9, across varying power constraints. Simulation results demonstrate that the proposed method consistently outperforms a decentralized MPC state-of-the-art (SOTA) baseline, achieving up to 16 % energy savings under a 30 % power constraint, with average temperature deviations (ATD) remaining within comfortable bounds (<2∘C). Even under tight energy constraints, the framework maintains stable control performance, outperforming existing methods that fail to adequately manage peak loads. Compared to rule-based and model-based MPC approaches, the proposed method is more flexible and robust, as it does not require detailed system identification or extensive training data. These results highlight the method's potential as a scalable and energy-efficient solution for contributing to global energy efficiency goals.
出版者	Elsevier Ltd
収録物名	Energy and Buildings
巻(号)	347
ページ	116264
刊行年月	2025-11-15
言語	英語
ハンドルURL	https://hdl.handle.net/11094/102866
PISSN	03787788
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1016/j.enbuild.2025.116264
アクセス権	open access
権利情報	This article is licensed under a Creative Commons Attribution 4.0 International License.
出版タイプ	Version of Record
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCMI資源タイプ	text
DCTERMS.bibliographicCitation	Energy and Buildings. 2025, 347, p. 116264
DC.title	Power-constrained VRF system optimization using symbolic regression for multiple zones environment
DC.creator	Theint Thu, Theint
	Kato, Kenshiro
	Zhao, Dafang
	Nishikawa, Hiroki
	Taniguchi, Ittetsu
	Onoye, Takao
DC.publisher	Elsevier Ltd
DC.language' scheme='DCTERMS.RFC1766	英語
DC.type' scheme='DCTERMS.DCMIType	text
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2025-11-15
DC.identifier	https://doi.org/10.1016/j.enbuild.2025.116264
DC.subject	Energy efficiency
	Model predictive control
	Power constraints
	Symbolic regression
	Thermal comfort
	Variable refrigerant flow
DC.description	Theint Thu T., Kato K., Zhao D., et al. Power-constrained VRF system optimization using symbolic regression for multiple zones environment. Energy and Buildings 347, 116264 (2025); https://doi.org/10.1016/j.enbuild.2025.116264.
DCTERMS.abstract	The rapid growth in global energy consumption highlights the urgency of doubling energy efficiency improvements by 2030. Heating, ventilation, and air-conditioning (HVAC) systems, which account for nearly half of building energy use, represent a critical target for optimization. Conventional HVAC control strategies, however, often suffer from inefficient power allocation, high peak demand, and compromised thermal comfort, especially under dynamic occupancy and environmental conditions. Existing multi-zone control methods often overlook peak power constraints and are not designed to optimize energy use under variable occupancy conditions, resulting in suboptimal energy performance. This study proposes a symbolic regression-based model predictive control (MPC) framework to address these challenges. The framework optimizes energy consumption and thermal comfort for multi-zone variable refrigerant flow (VRF) systems while addressing peak power constraints to reduce energy costs and improve thermal comfort. The method is evaluated under three operating priorities, ω = 0.1, 0.5, and 0.9, across varying power constraints. Simulation results demonstrate that the proposed method consistently outperforms a decentralized MPC state-of-the-art (SOTA) baseline, achieving up to 16 % energy savings under a 30 % power constraint, with average temperature deviations (ATD) remaining within comfortable bounds (<2∘C). Even under tight energy constraints, the framework maintains stable control performance, outperforming existing methods that fail to adequately manage peak loads. Compared to rule-based and model-based MPC approaches, the proposed method is more flexible and robust, as it does not require detailed system identification or extensive training data. These results highlight the method's potential as a scalable and energy-efficient solution for contributing to global energy efficiency goals.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	102866
citation_title	Power-constrained VRF system optimization using symbolic regression for multiple zones environment
citation_author	Theint Thu, Theint
	Kato, Kenshiro
	Zhao, Dafang
	Nishikawa, Hiroki
	Taniguchi, Ittetsu
	Onoye, Takao
citation_publisher	Elsevier Ltd
citation_language	英語
citation_date	2025-11-15
citation_journal_title	Energy and Buildings
citation_volume	347
citation_firstpage	116264
citation_doi	https://doi.org/10.1016/j.enbuild.2025.116264
citation_public_url	https://hdl.handle.net/11094/102866

Power-constrained VRF system optimization using symbolic regression for multiple zones environment

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