Chilled water temperature resetting using model-free reinforcement learning: Engineering application
Optimal operation of chillers could be realized by controlling chiller on–off status and resetting chilled water supply/return temperature. The chilled water supply temperature could influence both chiller power and indoor comfort simultaneously. Hence, it is necessary to optimize its set point. By...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Qiu, Shunian [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives - Plonowska, Karolina A. ELSEVIER, 2018, an international journal of research applied to energy efficiency in the built environment, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:255 ; year:2022 ; day:15 ; month:01 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.enbuild.2021.111694 |
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ELV05631549X |
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| 520 | |a Optimal operation of chillers could be realized by controlling chiller on–off status and resetting chilled water supply/return temperature. The chilled water supply temperature could influence both chiller power and indoor comfort simultaneously. Hence, it is necessary to optimize its set point. By combining the reinforcement learning (RL) technique and expertise knowledge, a hybrid model-free chilled water temperature resetting method for chillers is proposed, to improve the robustness and learning speed of RL control. The proposed method and the comparative expert manual control are applied to control an actual HVAC system in Shanghai. Engineering practice results suggest: (1) the energy-saving performance of the hybrid model-free method is close to that of expert manual control, with limited comfort sacrifice, acceptable robustness, learning speed and control stability; (2) without model establishing and persistent manual overwatch work, the proposed control method could conserve manual labor before and after the intervention of optimized control. Based on the engineering practice, a supplementary simulation study is conducted, which indicates: (1) the performance of the proposed method only takes one cooling season to converge; (2) the proposed hybrid method outperforms both pure RL-based control and pure rule-based control in the first training cooling season, while inferior to model predictive control; (3) the proposed method is an acceptable alternative for systems without sufficient data or certain equipment condition, due to its online self-learning mechanism and low pre-condition requirement on historical data and models. | ||
| 520 | |a Optimal operation of chillers could be realized by controlling chiller on–off status and resetting chilled water supply/return temperature. The chilled water supply temperature could influence both chiller power and indoor comfort simultaneously. Hence, it is necessary to optimize its set point. By combining the reinforcement learning (RL) technique and expertise knowledge, a hybrid model-free chilled water temperature resetting method for chillers is proposed, to improve the robustness and learning speed of RL control. The proposed method and the comparative expert manual control are applied to control an actual HVAC system in Shanghai. Engineering practice results suggest: (1) the energy-saving performance of the hybrid model-free method is close to that of expert manual control, with limited comfort sacrifice, acceptable robustness, learning speed and control stability; (2) without model establishing and persistent manual overwatch work, the proposed control method could conserve manual labor before and after the intervention of optimized control. Based on the engineering practice, a supplementary simulation study is conducted, which indicates: (1) the performance of the proposed method only takes one cooling season to converge; (2) the proposed hybrid method outperforms both pure RL-based control and pure rule-based control in the first training cooling season, while inferior to model predictive control; (3) the proposed method is an acceptable alternative for systems without sufficient data or certain equipment condition, due to its online self-learning mechanism and low pre-condition requirement on historical data and models. | ||
| 650 | 7 | |a Augmented intelligence |2 Elsevier | |
| 650 | 7 | |a Reinforcement learning |2 Elsevier | |
| 650 | 7 | |a Heating, ventilation and air-conditioning (HVAC) |2 Elsevier | |
| 650 | 7 | |a Central chiller plant |2 Elsevier | |
| 650 | 7 | |a Hybrid model-free control |2 Elsevier | |
| 700 | 1 | |a Li, Zhenhai |4 oth | |
| 700 | 1 | |a Fan, Dalian |4 oth | |
| 700 | 1 | |a He, Ruikai |4 oth | |
| 700 | 1 | |a Dai, Xinghui |4 oth | |
| 700 | 1 | |a Li, Zhengwei |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier Science |a Plonowska, Karolina A. ELSEVIER |t Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives |d 2018 |d an international journal of research applied to energy efficiency in the built environment |g Amsterdam [u.a.] |w (DE-627)ELV001764748 |
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10.1016/j.enbuild.2021.111694 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001623.pica (DE-627)ELV05631549X (ELSEVIER)S0378-7788(21)00978-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.94 bkl Qiu, Shunian verfasserin aut Chilled water temperature resetting using model-free reinforcement learning: Engineering application 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Optimal operation of chillers could be realized by controlling chiller on–off status and resetting chilled water supply/return temperature. The chilled water supply temperature could influence both chiller power and indoor comfort simultaneously. Hence, it is necessary to optimize its set point. By combining the reinforcement learning (RL) technique and expertise knowledge, a hybrid model-free chilled water temperature resetting method for chillers is proposed, to improve the robustness and learning speed of RL control. The proposed method and the comparative expert manual control are applied to control an actual HVAC system in Shanghai. Engineering practice results suggest: (1) the energy-saving performance of the hybrid model-free method is close to that of expert manual control, with limited comfort sacrifice, acceptable robustness, learning speed and control stability; (2) without model establishing and persistent manual overwatch work, the proposed control method could conserve manual labor before and after the intervention of optimized control. Based on the engineering practice, a supplementary simulation study is conducted, which indicates: (1) the performance of the proposed method only takes one cooling season to converge; (2) the proposed hybrid method outperforms both pure RL-based control and pure rule-based control in the first training cooling season, while inferior to model predictive control; (3) the proposed method is an acceptable alternative for systems without sufficient data or certain equipment condition, due to its online self-learning mechanism and low pre-condition requirement on historical data and models. Optimal operation of chillers could be realized by controlling chiller on–off status and resetting chilled water supply/return temperature. The chilled water supply temperature could influence both chiller power and indoor comfort simultaneously. Hence, it is necessary to optimize its set point. By combining the reinforcement learning (RL) technique and expertise knowledge, a hybrid model-free chilled water temperature resetting method for chillers is proposed, to improve the robustness and learning speed of RL control. The proposed method and the comparative expert manual control are applied to control an actual HVAC system in Shanghai. Engineering practice results suggest: (1) the energy-saving performance of the hybrid model-free method is close to that of expert manual control, with limited comfort sacrifice, acceptable robustness, learning speed and control stability; (2) without model establishing and persistent manual overwatch work, the proposed control method could conserve manual labor before and after the intervention of optimized control. Based on the engineering practice, a supplementary simulation study is conducted, which indicates: (1) the performance of the proposed method only takes one cooling season to converge; (2) the proposed hybrid method outperforms both pure RL-based control and pure rule-based control in the first training cooling season, while inferior to model predictive control; (3) the proposed method is an acceptable alternative for systems without sufficient data or certain equipment condition, due to its online self-learning mechanism and low pre-condition requirement on historical data and models. Augmented intelligence Elsevier Reinforcement learning Elsevier Heating, ventilation and air-conditioning (HVAC) Elsevier Central chiller plant Elsevier Hybrid model-free control Elsevier Li, Zhenhai oth Fan, Dalian oth He, Ruikai oth Dai, Xinghui oth Li, Zhengwei oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:255 year:2022 day:15 month:01 pages:0 https://doi.org/10.1016/j.enbuild.2021.111694 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 255 2022 15 0115 0 |
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10.1016/j.enbuild.2021.111694 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001623.pica (DE-627)ELV05631549X (ELSEVIER)S0378-7788(21)00978-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.94 bkl Qiu, Shunian verfasserin aut Chilled water temperature resetting using model-free reinforcement learning: Engineering application 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Optimal operation of chillers could be realized by controlling chiller on–off status and resetting chilled water supply/return temperature. The chilled water supply temperature could influence both chiller power and indoor comfort simultaneously. Hence, it is necessary to optimize its set point. By combining the reinforcement learning (RL) technique and expertise knowledge, a hybrid model-free chilled water temperature resetting method for chillers is proposed, to improve the robustness and learning speed of RL control. The proposed method and the comparative expert manual control are applied to control an actual HVAC system in Shanghai. Engineering practice results suggest: (1) the energy-saving performance of the hybrid model-free method is close to that of expert manual control, with limited comfort sacrifice, acceptable robustness, learning speed and control stability; (2) without model establishing and persistent manual overwatch work, the proposed control method could conserve manual labor before and after the intervention of optimized control. Based on the engineering practice, a supplementary simulation study is conducted, which indicates: (1) the performance of the proposed method only takes one cooling season to converge; (2) the proposed hybrid method outperforms both pure RL-based control and pure rule-based control in the first training cooling season, while inferior to model predictive control; (3) the proposed method is an acceptable alternative for systems without sufficient data or certain equipment condition, due to its online self-learning mechanism and low pre-condition requirement on historical data and models. Optimal operation of chillers could be realized by controlling chiller on–off status and resetting chilled water supply/return temperature. The chilled water supply temperature could influence both chiller power and indoor comfort simultaneously. Hence, it is necessary to optimize its set point. By combining the reinforcement learning (RL) technique and expertise knowledge, a hybrid model-free chilled water temperature resetting method for chillers is proposed, to improve the robustness and learning speed of RL control. The proposed method and the comparative expert manual control are applied to control an actual HVAC system in Shanghai. Engineering practice results suggest: (1) the energy-saving performance of the hybrid model-free method is close to that of expert manual control, with limited comfort sacrifice, acceptable robustness, learning speed and control stability; (2) without model establishing and persistent manual overwatch work, the proposed control method could conserve manual labor before and after the intervention of optimized control. Based on the engineering practice, a supplementary simulation study is conducted, which indicates: (1) the performance of the proposed method only takes one cooling season to converge; (2) the proposed hybrid method outperforms both pure RL-based control and pure rule-based control in the first training cooling season, while inferior to model predictive control; (3) the proposed method is an acceptable alternative for systems without sufficient data or certain equipment condition, due to its online self-learning mechanism and low pre-condition requirement on historical data and models. Augmented intelligence Elsevier Reinforcement learning Elsevier Heating, ventilation and air-conditioning (HVAC) Elsevier Central chiller plant Elsevier Hybrid model-free control Elsevier Li, Zhenhai oth Fan, Dalian oth He, Ruikai oth Dai, Xinghui oth Li, Zhengwei oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:255 year:2022 day:15 month:01 pages:0 https://doi.org/10.1016/j.enbuild.2021.111694 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 255 2022 15 0115 0 |
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10.1016/j.enbuild.2021.111694 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001623.pica (DE-627)ELV05631549X (ELSEVIER)S0378-7788(21)00978-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.94 bkl Qiu, Shunian verfasserin aut Chilled water temperature resetting using model-free reinforcement learning: Engineering application 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Optimal operation of chillers could be realized by controlling chiller on–off status and resetting chilled water supply/return temperature. The chilled water supply temperature could influence both chiller power and indoor comfort simultaneously. Hence, it is necessary to optimize its set point. By combining the reinforcement learning (RL) technique and expertise knowledge, a hybrid model-free chilled water temperature resetting method for chillers is proposed, to improve the robustness and learning speed of RL control. The proposed method and the comparative expert manual control are applied to control an actual HVAC system in Shanghai. Engineering practice results suggest: (1) the energy-saving performance of the hybrid model-free method is close to that of expert manual control, with limited comfort sacrifice, acceptable robustness, learning speed and control stability; (2) without model establishing and persistent manual overwatch work, the proposed control method could conserve manual labor before and after the intervention of optimized control. Based on the engineering practice, a supplementary simulation study is conducted, which indicates: (1) the performance of the proposed method only takes one cooling season to converge; (2) the proposed hybrid method outperforms both pure RL-based control and pure rule-based control in the first training cooling season, while inferior to model predictive control; (3) the proposed method is an acceptable alternative for systems without sufficient data or certain equipment condition, due to its online self-learning mechanism and low pre-condition requirement on historical data and models. Optimal operation of chillers could be realized by controlling chiller on–off status and resetting chilled water supply/return temperature. The chilled water supply temperature could influence both chiller power and indoor comfort simultaneously. Hence, it is necessary to optimize its set point. By combining the reinforcement learning (RL) technique and expertise knowledge, a hybrid model-free chilled water temperature resetting method for chillers is proposed, to improve the robustness and learning speed of RL control. The proposed method and the comparative expert manual control are applied to control an actual HVAC system in Shanghai. Engineering practice results suggest: (1) the energy-saving performance of the hybrid model-free method is close to that of expert manual control, with limited comfort sacrifice, acceptable robustness, learning speed and control stability; (2) without model establishing and persistent manual overwatch work, the proposed control method could conserve manual labor before and after the intervention of optimized control. Based on the engineering practice, a supplementary simulation study is conducted, which indicates: (1) the performance of the proposed method only takes one cooling season to converge; (2) the proposed hybrid method outperforms both pure RL-based control and pure rule-based control in the first training cooling season, while inferior to model predictive control; (3) the proposed method is an acceptable alternative for systems without sufficient data or certain equipment condition, due to its online self-learning mechanism and low pre-condition requirement on historical data and models. Augmented intelligence Elsevier Reinforcement learning Elsevier Heating, ventilation and air-conditioning (HVAC) Elsevier Central chiller plant Elsevier Hybrid model-free control Elsevier Li, Zhenhai oth Fan, Dalian oth He, Ruikai oth Dai, Xinghui oth Li, Zhengwei oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:255 year:2022 day:15 month:01 pages:0 https://doi.org/10.1016/j.enbuild.2021.111694 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 255 2022 15 0115 0 |
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10.1016/j.enbuild.2021.111694 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001623.pica (DE-627)ELV05631549X (ELSEVIER)S0378-7788(21)00978-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.94 bkl Qiu, Shunian verfasserin aut Chilled water temperature resetting using model-free reinforcement learning: Engineering application 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Optimal operation of chillers could be realized by controlling chiller on–off status and resetting chilled water supply/return temperature. The chilled water supply temperature could influence both chiller power and indoor comfort simultaneously. Hence, it is necessary to optimize its set point. By combining the reinforcement learning (RL) technique and expertise knowledge, a hybrid model-free chilled water temperature resetting method for chillers is proposed, to improve the robustness and learning speed of RL control. The proposed method and the comparative expert manual control are applied to control an actual HVAC system in Shanghai. Engineering practice results suggest: (1) the energy-saving performance of the hybrid model-free method is close to that of expert manual control, with limited comfort sacrifice, acceptable robustness, learning speed and control stability; (2) without model establishing and persistent manual overwatch work, the proposed control method could conserve manual labor before and after the intervention of optimized control. Based on the engineering practice, a supplementary simulation study is conducted, which indicates: (1) the performance of the proposed method only takes one cooling season to converge; (2) the proposed hybrid method outperforms both pure RL-based control and pure rule-based control in the first training cooling season, while inferior to model predictive control; (3) the proposed method is an acceptable alternative for systems without sufficient data or certain equipment condition, due to its online self-learning mechanism and low pre-condition requirement on historical data and models. Optimal operation of chillers could be realized by controlling chiller on–off status and resetting chilled water supply/return temperature. The chilled water supply temperature could influence both chiller power and indoor comfort simultaneously. Hence, it is necessary to optimize its set point. By combining the reinforcement learning (RL) technique and expertise knowledge, a hybrid model-free chilled water temperature resetting method for chillers is proposed, to improve the robustness and learning speed of RL control. The proposed method and the comparative expert manual control are applied to control an actual HVAC system in Shanghai. Engineering practice results suggest: (1) the energy-saving performance of the hybrid model-free method is close to that of expert manual control, with limited comfort sacrifice, acceptable robustness, learning speed and control stability; (2) without model establishing and persistent manual overwatch work, the proposed control method could conserve manual labor before and after the intervention of optimized control. Based on the engineering practice, a supplementary simulation study is conducted, which indicates: (1) the performance of the proposed method only takes one cooling season to converge; (2) the proposed hybrid method outperforms both pure RL-based control and pure rule-based control in the first training cooling season, while inferior to model predictive control; (3) the proposed method is an acceptable alternative for systems without sufficient data or certain equipment condition, due to its online self-learning mechanism and low pre-condition requirement on historical data and models. Augmented intelligence Elsevier Reinforcement learning Elsevier Heating, ventilation and air-conditioning (HVAC) Elsevier Central chiller plant Elsevier Hybrid model-free control Elsevier Li, Zhenhai oth Fan, Dalian oth He, Ruikai oth Dai, Xinghui oth Li, Zhengwei oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:255 year:2022 day:15 month:01 pages:0 https://doi.org/10.1016/j.enbuild.2021.111694 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 255 2022 15 0115 0 |
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10.1016/j.enbuild.2021.111694 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001623.pica (DE-627)ELV05631549X (ELSEVIER)S0378-7788(21)00978-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.94 bkl Qiu, Shunian verfasserin aut Chilled water temperature resetting using model-free reinforcement learning: Engineering application 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Optimal operation of chillers could be realized by controlling chiller on–off status and resetting chilled water supply/return temperature. The chilled water supply temperature could influence both chiller power and indoor comfort simultaneously. Hence, it is necessary to optimize its set point. By combining the reinforcement learning (RL) technique and expertise knowledge, a hybrid model-free chilled water temperature resetting method for chillers is proposed, to improve the robustness and learning speed of RL control. The proposed method and the comparative expert manual control are applied to control an actual HVAC system in Shanghai. Engineering practice results suggest: (1) the energy-saving performance of the hybrid model-free method is close to that of expert manual control, with limited comfort sacrifice, acceptable robustness, learning speed and control stability; (2) without model establishing and persistent manual overwatch work, the proposed control method could conserve manual labor before and after the intervention of optimized control. Based on the engineering practice, a supplementary simulation study is conducted, which indicates: (1) the performance of the proposed method only takes one cooling season to converge; (2) the proposed hybrid method outperforms both pure RL-based control and pure rule-based control in the first training cooling season, while inferior to model predictive control; (3) the proposed method is an acceptable alternative for systems without sufficient data or certain equipment condition, due to its online self-learning mechanism and low pre-condition requirement on historical data and models. Optimal operation of chillers could be realized by controlling chiller on–off status and resetting chilled water supply/return temperature. The chilled water supply temperature could influence both chiller power and indoor comfort simultaneously. Hence, it is necessary to optimize its set point. By combining the reinforcement learning (RL) technique and expertise knowledge, a hybrid model-free chilled water temperature resetting method for chillers is proposed, to improve the robustness and learning speed of RL control. The proposed method and the comparative expert manual control are applied to control an actual HVAC system in Shanghai. Engineering practice results suggest: (1) the energy-saving performance of the hybrid model-free method is close to that of expert manual control, with limited comfort sacrifice, acceptable robustness, learning speed and control stability; (2) without model establishing and persistent manual overwatch work, the proposed control method could conserve manual labor before and after the intervention of optimized control. Based on the engineering practice, a supplementary simulation study is conducted, which indicates: (1) the performance of the proposed method only takes one cooling season to converge; (2) the proposed hybrid method outperforms both pure RL-based control and pure rule-based control in the first training cooling season, while inferior to model predictive control; (3) the proposed method is an acceptable alternative for systems without sufficient data or certain equipment condition, due to its online self-learning mechanism and low pre-condition requirement on historical data and models. Augmented intelligence Elsevier Reinforcement learning Elsevier Heating, ventilation and air-conditioning (HVAC) Elsevier Central chiller plant Elsevier Hybrid model-free control Elsevier Li, Zhenhai oth Fan, Dalian oth He, Ruikai oth Dai, Xinghui oth Li, Zhengwei oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:255 year:2022 day:15 month:01 pages:0 https://doi.org/10.1016/j.enbuild.2021.111694 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 255 2022 15 0115 0 |
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Optimal operation of chillers could be realized by controlling chiller on–off status and resetting chilled water supply/return temperature. The chilled water supply temperature could influence both chiller power and indoor comfort simultaneously. Hence, it is necessary to optimize its set point. By combining the reinforcement learning (RL) technique and expertise knowledge, a hybrid model-free chilled water temperature resetting method for chillers is proposed, to improve the robustness and learning speed of RL control. The proposed method and the comparative expert manual control are applied to control an actual HVAC system in Shanghai. Engineering practice results suggest: (1) the energy-saving performance of the hybrid model-free method is close to that of expert manual control, with limited comfort sacrifice, acceptable robustness, learning speed and control stability; (2) without model establishing and persistent manual overwatch work, the proposed control method could conserve manual labor before and after the intervention of optimized control. Based on the engineering practice, a supplementary simulation study is conducted, which indicates: (1) the performance of the proposed method only takes one cooling season to converge; (2) the proposed hybrid method outperforms both pure RL-based control and pure rule-based control in the first training cooling season, while inferior to model predictive control; (3) the proposed method is an acceptable alternative for systems without sufficient data or certain equipment condition, due to its online self-learning mechanism and low pre-condition requirement on historical data and models. |
| abstractGer |
Optimal operation of chillers could be realized by controlling chiller on–off status and resetting chilled water supply/return temperature. The chilled water supply temperature could influence both chiller power and indoor comfort simultaneously. Hence, it is necessary to optimize its set point. By combining the reinforcement learning (RL) technique and expertise knowledge, a hybrid model-free chilled water temperature resetting method for chillers is proposed, to improve the robustness and learning speed of RL control. The proposed method and the comparative expert manual control are applied to control an actual HVAC system in Shanghai. Engineering practice results suggest: (1) the energy-saving performance of the hybrid model-free method is close to that of expert manual control, with limited comfort sacrifice, acceptable robustness, learning speed and control stability; (2) without model establishing and persistent manual overwatch work, the proposed control method could conserve manual labor before and after the intervention of optimized control. Based on the engineering practice, a supplementary simulation study is conducted, which indicates: (1) the performance of the proposed method only takes one cooling season to converge; (2) the proposed hybrid method outperforms both pure RL-based control and pure rule-based control in the first training cooling season, while inferior to model predictive control; (3) the proposed method is an acceptable alternative for systems without sufficient data or certain equipment condition, due to its online self-learning mechanism and low pre-condition requirement on historical data and models. |
| abstract_unstemmed |
Optimal operation of chillers could be realized by controlling chiller on–off status and resetting chilled water supply/return temperature. The chilled water supply temperature could influence both chiller power and indoor comfort simultaneously. Hence, it is necessary to optimize its set point. By combining the reinforcement learning (RL) technique and expertise knowledge, a hybrid model-free chilled water temperature resetting method for chillers is proposed, to improve the robustness and learning speed of RL control. The proposed method and the comparative expert manual control are applied to control an actual HVAC system in Shanghai. Engineering practice results suggest: (1) the energy-saving performance of the hybrid model-free method is close to that of expert manual control, with limited comfort sacrifice, acceptable robustness, learning speed and control stability; (2) without model establishing and persistent manual overwatch work, the proposed control method could conserve manual labor before and after the intervention of optimized control. Based on the engineering practice, a supplementary simulation study is conducted, which indicates: (1) the performance of the proposed method only takes one cooling season to converge; (2) the proposed hybrid method outperforms both pure RL-based control and pure rule-based control in the first training cooling season, while inferior to model predictive control; (3) the proposed method is an acceptable alternative for systems without sufficient data or certain equipment condition, due to its online self-learning mechanism and low pre-condition requirement on historical data and models. |
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