Collaborative optimal operation control of HVAC systems based on multi-agent
The HVAC system of public buildings, as a thermostatically controlled load, accounting for a relatively significant proportion of building energy consumption. Therefore, it is necessary to optimize energy efficient of HVAC systems in public buildings. Nevertheless, the complication of HVAC systems i...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1609210/full |
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| author | Chen Fu Kaipeng Chen Yan Xu Dongyue Ming Ruiwen Ye Yingjun Wu Lixia Sun |
| author_facet | Chen Fu Kaipeng Chen Yan Xu Dongyue Ming Ruiwen Ye Yingjun Wu Lixia Sun |
| author_sort | Chen Fu |
| collection | DOAJ |
| description | The HVAC system of public buildings, as a thermostatically controlled load, accounting for a relatively significant proportion of building energy consumption. Therefore, it is necessary to optimize energy efficient of HVAC systems in public buildings. Nevertheless, the complication of HVAC systems is on the rise. As a consequence, the computing efficiency of optimization algorithms is relatively low, posing challenges for real-time optimal operation control. Hence, there is an immediate requirement to boost both the energy efficiency of the system and the computing efficiency in order to strengthen the system’s robustness. In this paper, a collaborative optimization approach based on multi-agent is initially put forward to address the overall optimization issue (OOI) of a complicated HVAC system. The OOI is disintegrated into numerous sub-optimization issues within the multi-agent structure. These sub-issues take into account the interaction features among components. By doing so, the complication of the OOI within HVAC systems is effectively decreased. Secondly, the adaptive hybrid-artificial fish swarm algorithm (AH-AFSA) is proposed for solving optimization issues with mixed decision variables. Finally, the effectiveness of the proposed method is verified by an arithmetic example. The analysis reveals that the proposed approach is capable of reducing power consumption by 18.9% and the computation time for each operation condition is 12.2 s, which saves about 54% of time cost compared with the centralized method, and can enhance the computing efficiency of the optimization approach for a complicated HVAC system while reducing power consumption. |
| format | Article |
| id | doaj-art-e545c6d31c6e46bb892a3a818256dcb6 |
| institution | Kabale University |
| issn | 2296-598X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj-art-e545c6d31c6e46bb892a3a818256dcb62025-08-20T03:29:31ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2025-07-011310.3389/fenrg.2025.16092101609210Collaborative optimal operation control of HVAC systems based on multi-agentChen Fu0Kaipeng Chen1Yan Xu2Dongyue Ming3Ruiwen Ye4Yingjun Wu5Lixia Sun6State Grid Hubei Marketing Service Center (Measurement Center), Wuhan, ChinaSchool of Electrical and Power Engineering, Hohai University, Nanjing, ChinaState Grid Hubei Marketing Service Center (Measurement Center), Wuhan, ChinaState Grid Hubei Marketing Service Center (Measurement Center), Wuhan, ChinaState Grid Hubei Shiyan Power Supply Company, Shiyan, ChinaSchool of Electrical and Power Engineering, Hohai University, Nanjing, ChinaSchool of Electrical and Power Engineering, Hohai University, Nanjing, ChinaThe HVAC system of public buildings, as a thermostatically controlled load, accounting for a relatively significant proportion of building energy consumption. Therefore, it is necessary to optimize energy efficient of HVAC systems in public buildings. Nevertheless, the complication of HVAC systems is on the rise. As a consequence, the computing efficiency of optimization algorithms is relatively low, posing challenges for real-time optimal operation control. Hence, there is an immediate requirement to boost both the energy efficiency of the system and the computing efficiency in order to strengthen the system’s robustness. In this paper, a collaborative optimization approach based on multi-agent is initially put forward to address the overall optimization issue (OOI) of a complicated HVAC system. The OOI is disintegrated into numerous sub-optimization issues within the multi-agent structure. These sub-issues take into account the interaction features among components. By doing so, the complication of the OOI within HVAC systems is effectively decreased. Secondly, the adaptive hybrid-artificial fish swarm algorithm (AH-AFSA) is proposed for solving optimization issues with mixed decision variables. Finally, the effectiveness of the proposed method is verified by an arithmetic example. The analysis reveals that the proposed approach is capable of reducing power consumption by 18.9% and the computation time for each operation condition is 12.2 s, which saves about 54% of time cost compared with the centralized method, and can enhance the computing efficiency of the optimization approach for a complicated HVAC system while reducing power consumption.https://www.frontiersin.org/articles/10.3389/fenrg.2025.1609210/fullHVACmulti-agentpower consumptionAH-AFSAcollaborative optimization |
| spellingShingle | Chen Fu Kaipeng Chen Yan Xu Dongyue Ming Ruiwen Ye Yingjun Wu Lixia Sun Collaborative optimal operation control of HVAC systems based on multi-agent Frontiers in Energy Research HVAC multi-agent power consumption AH-AFSA collaborative optimization |
| title | Collaborative optimal operation control of HVAC systems based on multi-agent |
| title_full | Collaborative optimal operation control of HVAC systems based on multi-agent |
| title_fullStr | Collaborative optimal operation control of HVAC systems based on multi-agent |
| title_full_unstemmed | Collaborative optimal operation control of HVAC systems based on multi-agent |
| title_short | Collaborative optimal operation control of HVAC systems based on multi-agent |
| title_sort | collaborative optimal operation control of hvac systems based on multi agent |
| topic | HVAC multi-agent power consumption AH-AFSA collaborative optimization |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1609210/full |
| work_keys_str_mv | AT chenfu collaborativeoptimaloperationcontrolofhvacsystemsbasedonmultiagent AT kaipengchen collaborativeoptimaloperationcontrolofhvacsystemsbasedonmultiagent AT yanxu collaborativeoptimaloperationcontrolofhvacsystemsbasedonmultiagent AT dongyueming collaborativeoptimaloperationcontrolofhvacsystemsbasedonmultiagent AT ruiwenye collaborativeoptimaloperationcontrolofhvacsystemsbasedonmultiagent AT yingjunwu collaborativeoptimaloperationcontrolofhvacsystemsbasedonmultiagent AT lixiasun collaborativeoptimaloperationcontrolofhvacsystemsbasedonmultiagent |