Research on Real-Time Control Strategy of Air-Conditioning Water System Based on Model Predictive Control
The optimization of the operation strategy for building HVAC systems is the key to achieving energy conservation and consumption reduction in air-conditioning systems. This study proposes an online real-time control strategy for the air-conditioning water system based on the model predictive control...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/10/1654 |
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| author | Dehan Liu Jing Zhao Yibing Wu Zhe Tian |
| author_facet | Dehan Liu Jing Zhao Yibing Wu Zhe Tian |
| author_sort | Dehan Liu |
| collection | DOAJ |
| description | The optimization of the operation strategy for building HVAC systems is the key to achieving energy conservation and consumption reduction in air-conditioning systems. This study proposes an online real-time control strategy for the air-conditioning water system based on the model predictive control (MPC) principle, implemented and validated on the integrated energy experimental platform. The experimental system simulates load generation and dissipation processes using a water tank, where hourly varying heating power output emulates the dynamic cooling loads of buildings. By regulating the chilled water system through different algorithms, the temperature tracking control performance and cooling supply regulation accuracy were rigorously validated. The control module was written in the Python 3.8 environment, and Niagara 4 software was used as an intermediate software to achieve data interaction and logical control with the laboratory system. The experimental results show that this algorithm can follow the hourly optimized parameters with a low overshoot in the short-term domain. Meanwhile, it can achieve the optimal control of cooling capacity and energy consumption in the long-term domain. Compared with the PID strategy, the temperature following control accuracy can be improved by 9.64%, and the cooling capacity can be saved by 6.24%. Compared with the day-ahead MPC algorithm, the temperature following control accuracy can be relatively improved by 16.52%, and the cooling capacity can be saved by 1.24%. |
| format | Article |
| id | doaj-art-a0abd0caade548779ce7fe4d31e69216 |
| institution | OA Journals |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-a0abd0caade548779ce7fe4d31e692162025-08-20T01:56:20ZengMDPI AGBuildings2075-53092025-05-011510165410.3390/buildings15101654Research on Real-Time Control Strategy of Air-Conditioning Water System Based on Model Predictive ControlDehan Liu0Jing Zhao1Yibing Wu2Zhe Tian3Tianjin Key Laboratory of Built Environment and Energy Application, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, ChinaTianjin Key Laboratory of Built Environment and Energy Application, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, ChinaTianjin Key Laboratory of Built Environment and Energy Application, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, ChinaTianjin Key Laboratory of Built Environment and Energy Application, School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, ChinaThe optimization of the operation strategy for building HVAC systems is the key to achieving energy conservation and consumption reduction in air-conditioning systems. This study proposes an online real-time control strategy for the air-conditioning water system based on the model predictive control (MPC) principle, implemented and validated on the integrated energy experimental platform. The experimental system simulates load generation and dissipation processes using a water tank, where hourly varying heating power output emulates the dynamic cooling loads of buildings. By regulating the chilled water system through different algorithms, the temperature tracking control performance and cooling supply regulation accuracy were rigorously validated. The control module was written in the Python 3.8 environment, and Niagara 4 software was used as an intermediate software to achieve data interaction and logical control with the laboratory system. The experimental results show that this algorithm can follow the hourly optimized parameters with a low overshoot in the short-term domain. Meanwhile, it can achieve the optimal control of cooling capacity and energy consumption in the long-term domain. Compared with the PID strategy, the temperature following control accuracy can be improved by 9.64%, and the cooling capacity can be saved by 6.24%. Compared with the day-ahead MPC algorithm, the temperature following control accuracy can be relatively improved by 16.52%, and the cooling capacity can be saved by 1.24%.https://www.mdpi.com/2075-5309/15/10/1654model predictive controloptimal scheduling and controlbuilding energy efficiency |
| spellingShingle | Dehan Liu Jing Zhao Yibing Wu Zhe Tian Research on Real-Time Control Strategy of Air-Conditioning Water System Based on Model Predictive Control Buildings model predictive control optimal scheduling and control building energy efficiency |
| title | Research on Real-Time Control Strategy of Air-Conditioning Water System Based on Model Predictive Control |
| title_full | Research on Real-Time Control Strategy of Air-Conditioning Water System Based on Model Predictive Control |
| title_fullStr | Research on Real-Time Control Strategy of Air-Conditioning Water System Based on Model Predictive Control |
| title_full_unstemmed | Research on Real-Time Control Strategy of Air-Conditioning Water System Based on Model Predictive Control |
| title_short | Research on Real-Time Control Strategy of Air-Conditioning Water System Based on Model Predictive Control |
| title_sort | research on real time control strategy of air conditioning water system based on model predictive control |
| topic | model predictive control optimal scheduling and control building energy efficiency |
| url | https://www.mdpi.com/2075-5309/15/10/1654 |
| work_keys_str_mv | AT dehanliu researchonrealtimecontrolstrategyofairconditioningwatersystembasedonmodelpredictivecontrol AT jingzhao researchonrealtimecontrolstrategyofairconditioningwatersystembasedonmodelpredictivecontrol AT yibingwu researchonrealtimecontrolstrategyofairconditioningwatersystembasedonmodelpredictivecontrol AT zhetian researchonrealtimecontrolstrategyofairconditioningwatersystembasedonmodelpredictivecontrol |